Adaptive learning status · 20261011

Generated 2026-10-12T09:21:33.063795+00:00 · schema adaptive_status_report_v2

The self-improvement cycle

Every day the system runs a closed learning loop against official results. Gated mode requires prospective evidence. Operator-enabled direct mode applies first, then measures subsequent settled slates under automatic rollback guardrails.

Measure

Reconcile every published forecast against the official MLB final: winner, score, win probability, and starting-pitcher line. This produces the verified result set below.

Diagnose

Attribute each miss to a concrete driver: a compressed win probability, an under-projected run total, or a starting-pitcher projection that diverged from the box score.

Propose

Translate the dominant drivers into a small, bounded change to a single registered parameter. Proposals are data, never commands.

Validate

Run the change in shadow on the same inputs, or in a chronological backtest for training parameters, until it clears the coverage and improvement gates across multiple slates.

Promote

Only a change that beats the current champion under those gates, with human approval, replaces production. A regression triggers automatic rollback.

Definitions

Champion

The single prediction configuration currently serving every live pick. Today it is candidate-668e4305abca59bc.

Candidate / shadow

A proposed change scored in the background on the same games. It never affects published output until approved.

Coverage gate (60 games, 5 dates)

A slate is only ~15 games, too few to separate skill from luck. A change must prove itself over roughly four days before it can replace the champion. This gate governs promotion only; the daily diagnosis below runs regardless.

Brier score

Calibration quality of the win probabilities. Lower is better; 0.25 equals always publishing 50/50, so any value above 0.25 is worse than a coin flip.

Yesterday's games: what happened (20261011)

Winners correct

1/1 · 100%

0 missed

Mean Brier

0.2041

0.25 = coin flip

Total-run error

runs off per game

Near coin-flip picks

0/1

system edge < 0.04 (p within 2 points of 50%)

Win/loss and the model spread (run line) are the same game two ways: Margin is model-projected margin vs actual (home minus away); Cover is whether the actual margin landed on the model's side.

Game	Result	Pick vs winner	p(home)	Pred vs actual	Margin (model vs actual)	Cover	Run err	Primary diagnosis
LAD@MIL	✓ hit	MIL → won MIL	54.8%	2-4 vs 1-2	+2 vs +1	✓ covered	3

Luna analysis coverage receipt

High-level causal issue register

Deterministic cross-layer findings. Critical means evidence or contract failure; high means material model/process risk. Each row pairs the action to take (Do this) with the wrong conclusion to avoid (Don't conclude).

Severity	Domain	Finding	Action & guardrail
HIGH	calibration	Published confidence buckets were poorly aligned on this slate weighted absolute calibration error 0.4518 across 1 games	Do thisWatch the same confidence buckets over the full rolling window before changing any calibration weight. Don't concludeA single slate has far too few games to establish a stable calibration curve.
HIGH	model_validation	Validated run models explain very little holdout variance home/away/total R² = 0.0021/0.0309/0.0024	Do thisTreat exact run scores as low-confidence estimates, and look for stronger features or a better target before tuning the score tails. Don't concludeA small positive R² only means the model beats guessing the average; it does not make the exact scores reliable.
INFO	coverage	Every settled game received structural QA 1/1 games structurally audited	Do thisKeep the per-game audit receipt in every report. Don't concludeFull audit coverage does not cancel the findings raised inside the audited games.
INFO	simulation	Simulation produced no winner-direction intervention on this slate flip candidates 0; published flips 0	Do thisMeasure the simulation's contribution across rolling slates, separating score realization from winner flipping. Don't concludeZero winner flips is not a defect and does not mean the simulation did no score-reconciliation work.

Pipeline stage regression

Stage	Winner hits	Accuracy	Team-run MAE	Margin MAE
historical_ml	1/1	100.0%	2.984	0.352
variance_applied	1/1	100.0%	3	0
enhanced_prediction	1/1	100.0%	2	0
kpi_layer	1/1	100.0%	2	0
embedded_savant_run_environment	1/1	100.0%	1.5	1
fundamental_edge_target_review	1/1	100.0%	1.5	1
capped_target	1/1	100.0%	1.5	1
simulation_result	1/1	100.0%	1.5	1
published_final	1/1	100.0%	1.5	1

Raw → calibrated → published probability

All values use the picked side. A higher published value is only a bypass if no named transformation explains it.

Game	Raw model	Calibrated	Published	Recorded transformation
LAD@MIL	57.1%	54.8%	54.8%	published probability matches the calibrated/governed value

What yesterday's misses mean for today

System self-assessment (can it recover?)

The system's honest read of its own state from measured evidence, and a prioritized plan split into what the nightly loop can fix by itself (auto) and what needs a human (operator: code, an isolated training backtest, or better data).

#	Owner	Problem & evidence	Action
1	operator	simulation_inert_on_slate persisted 11 consecutive slates	confirm the simulation module is running, not silently no-oping Code to changeFiles: `routers/team_router.py` Where: _build_simulation_distribution (~L6153) + the draw loop early-exits (~L6107, ~L6115) and constants NUM_SIMULATIONS/MIN_DRAWS_BEFORE_CLOSE_EXIT (~L5719-5722) Root cause: The draw distribution is built from the SAME draws collected while searching for a play-by-play that realizes the model target, and the loop breaks as soon as it finds one (default draw_count ~2). away_win_rate over ~2 selected draws is not a probability, so the flip/agreement signal is vacuous (0 flip candidates for 11 slates). Change: Separate the two jobs: keep the target-realization search for the box score, but compute the win-probability distribution from an INDEPENDENT unsteered batch (e.g. 200-500 draws, or a closed-form Skellam/Poisson-difference on the expected runs + variance) that does NOT early-exit. Make the batch size a registered parameter so the loop can trade compute for signal. Feed that real win-rate into final_prediction_policy._distribution_agreement. Applies: coding_agent_or_operator
1	operator	run_model_near_zero_signal holdout R^2 total=0.002415 home=0.002143 away=0.030936	the run models carry almost no signal; improve features/target and run an isolated training backtest. Nightly variance/confidence tuning cannot fix this. Real run-line/spread data as a feature would help most. Code to changeFiles: `services/historical_game_features.py, services/historical_game_analyzer.py` Where: the shared feature contract in historical_game_features.py + training in historical_game_analyzer.py Root cause: Holdout R^2 for run targets is near zero, so the features carry almost no run signal; no bounded nightly knob can raise this. Change: Add predictive features (market no-vig implied probability when a real source-labelled price exists, park/weather run environment, bullpen rest/fatigue, platoon-adjusted lineup quality) to the feature contract, then retrain and compare holdout R^2 via an isolated same-fingerprint backtest. Bump MODEL_SCHEMA_VERSION when feature semantics change. Applies: operator
2	auto (loop)	confidence_overconfident weighted calibration error 0.4518; high-conviction miss rate None	lower confidence caps and widen run variance levers: confidence.base_cap, confidence.moderate_cap, score.variance_multiplier, score.variance_floor

Spread / margin analysis (the model's own line)

The model's predicted run margin is its implied spread. This shows how far the actual margin fell from it, whether the actual landed on the model's side (cover), and whether the model's own conviction is predictive. When high-conviction picks lose more than they win, published confidence is anti-predictive and the promotable fix is lower confidence caps and wider run variance.

Game	Conviction	Model margin	Actual margin	Margin err	Covered
LAD@MIL	lean	2.0	1.0	1.0	✓

Pitcher versus batter: patterns across the whole slate

One blown start is noise. The same failure repeating across the slate is a model problem. These are the repeats.

Lineups scored 1.5 runs fewer than projected on average across 2 sides on this slate, which points at the run environment rather than any single matchup. The persistence test below says whether it repeats.
Of 9 player deviations big enough to move a game, only 7 are backed by the hitter's own season contact quality (78%). The rest are good or bad days on profiles that do not support the result, and tuning on those teaches the model noise. Worth learning from: Brice Turang (MIL, 1.26 runs, xwOBA 0.344, elite), Andrew Vaughn (MIL, 1.11 runs, xwOBA 0.329, above average), Teoscar Hernández (LAD, 0.94 runs, xwOBA 0.332, above average), Shohei Ohtani (LAD, 0.8 runs, xwOBA 0.397, elite).
The most common way a side broke on this slate was the lineup failing to score its projection (1 of 2 sides).

Sides analyzed	Broke on pitching	Broke on offense	Both	Variance	Mean starter ERA error	Mean lineup runs vs proj
2	0	1	0	1	-0.84	-1.5

Trained winner blend: does it reach the published number?

The blend mixes the trained win classifier with the margin-derived probability. It is only worth its weight if its decision survives the downstream reconciliation to the projected score.

On all 1 games with an applied blend, the blend's side and the published side agree.

Blend side survived on 1 of 1 games; the blend flagged a winner change on 0.

Repeated bias across slates, and the lever that moves it

A bias on one slate is noise. The same sign across slates, large relative to how much it moves between them, is a model defect. Only the ones that pass both tests are turned into a named parameter change; the rest are held deliberately.

starter ERA error (actual minus projected)

Mean -0.34 over 10 slates (sign shared by 0.60 of them, z = -0.83).

starters beat their projection: the run model credits opposing offenses with more than they produced.

Hold: no change: this bias is not separable from slate-to-slate noise yet

6 of 10 slates share the sign, mean -0.34 +/- 0.41 standard error: not separable from noise

lineup runs actual minus projected

Mean -0.75 over 10 slates (sign shared by 0.70 of them, z = -2.13).

lineups scored below projection: the run environment is set too high.

Actionable: the total-runs projection is running hot; check the run environment before touching variance, and treat score.variance_floor only as the spread lever
Lever: score.variance_floor (default 2.75); watch total_run_mae, team_run_mae.

7 of 10 slates share the sign and the mean is 2.1 standard errors from zero

Slate	Games	Winner acc	Mean starter ERA error	Mean lineup runs vs proj	Sides broke on pitching	Sides broke on offense
20261010	1	1.000	-1.93	-2.00		1
20261009		—	—	—
20261008	1	1.000	2.31	1.00
20261007	4	0.750	0.85	-0.38	1	2
20261006	2	1.000	-1.40	-1.25		1
20261005	2	0.000	-1.11	-0.75		2
20261004	2	0.500	-1.24	-2.00		2
20261003	4	1.000	-0.95	-2.25		4
20261002		—	—	—
20261001	1	1.000	0.00	0.50
20260930	4	0.750	0.78	0.12		1
20260929	4	1.000	-0.76	-0.50		3

Forensic review of every game

Same evidence packet supplied to Luna. Lost games lead the list and open by default with a plain-language read (what went wrong, why, and whether it was a system fault or a game/team-specific event) followed by a full reconstruction: predicted vs actual, the score-construction path, probability layers, run environment, recent form, head-to-head, the largest player adjustments, and starters. Hits keep the lean view.

LAD@MIL · HIT · solid_correct_call

Evidence edges (home-away): season 0.003, recent -0.122, starter 0.072, composite 0.489

Deterministic observations

published system edge 0.10 (lean); this is not the half-scale distance from 50%
team-run absolute error 3; total bias +3
pregame home-minus-away evidence: season +0.003, recent -0.122, pitcher +0.072, composite +0.489
confidence pipeline reached 0.548 before later publication/recency controls; published home probability was 0.548
score-volatility actions: no_change
forecast-file integrity: clean (0 defects, 1 informational note(s)); the saved artifact is not why the pick missed

Quantified contributors

total runs over-projected by 3 (6 vs 3)

Starting pitchers: projected vs actual (ERA = earned runs over nine innings, so the miss is judged against the outs actually recorded: 4 ER in 6 outs is 18.00 ERA, 4 ER in 21 outs is 5.14. Raw ER cannot tell those apart.)

Team	Starter	Projected	Actual	ERA error
LAD	Tarik Skubal	5.68 ERA (4 ER / 19 outs)	3.6 ERA (2 ER / 15 outs)	-2.08
MIL	Jacob Misiorowski	1.69 ERA (1 ER / 16 outs)	2.08 ERA (1 ER / 13 outs)	0.39

Player-level check (correct winner)

The winner was right. This is the player evidence behind it, so a correct call built on a wrong player picture is still visible.

Side	Starter ERA (actual / proj)	ERA error	Bullpen ER	Lineup runs vs proj	HR	Starter experience	Verdict
away	3.6 / 5.68	-2.08	0	-1	0		neither side broke materially - the result is closer to variance
home	2.08 / 1.69	0.39	0	-2	1		offense: the lineup scored well below its projection

Biggest movers vs projection:

Andy Pages (LAD): 1-for-4, 0 HR, 1 RBI (projected 1.0 H, 1.0 HR) [run impact -1.26]
Brice Turang (MIL): 1-for-2, 1 HR, 1 RBI (projected 1.0 H, 0.0 HR) [run impact 1.26]
Andrew Vaughn (MIL): 1-for-3, 0 HR, 0 RBI (projected 0.0 H, 0.0 HR) [run impact 1.11]
Teoscar Hernández (LAD): 2-for-4, 0 HR, 0 RBI (projected 0.0 H, 0.0 HR) [run impact 0.94]
Shohei Ohtani (LAD): 1-for-3, 0 HR, 0 RBI (projected 0.0 H, 0.0 HR) [run impact 0.8]

Forecast-file integrity (checks on the saved forecast file, not why the pick missed)

PASS — 109 checks passed, 0 defects, 1 informational note(s) (expected design notes, not problems). The saved file is clean; it is not the reason this game missed.

Cross-game impact before changing a lever

Lever	Would help	Would hurt / risk
score.variance_multiplier / score.variance_floor (widen run distributions)
player.pitcher.v4 form weights / player.pitcher_prior_games
recent.* / season.* run-mean weighting

Combined deep report — full evidence (20261011)

The complete forensic base that also feeds the standalone deep report and Luna, inlined so this page carries everything for the day under review. Nothing shown above is repeated.

Player metric accuracy across the slate

Every hitter projection on the slate (18 lines) pooled per metric. Bias is the level error on the slate total. Spread is the projection standard deviation over the real one: near zero means every hitter got roughly the same number, so the total can be right while no hitter is separated. Flat MAE is the error from publishing the slate mean for everyone, which the projection has to beat to carry any per-player value.

Metric	Lines	Projected	Actual	Bias	MAE	Flat MAE	Spread	Corr	Verdict
AB	18	61	57	+7%	0.667	0.556	0.71	-0.193	no per-hitter value
H	18	10	13	-23.1%	0.722	0.562	0.76	-0.21	no per-hitter value
BB	18	8	10	-20%	0.667	0.556	0.83	-0.27	no per-hitter value
SO	18	18	21	-14.3%	0.944	0.648	0.98	-0.098	no per-hitter value
RBI	18	6	3	+100%	0.389	0.278	1.26	0	no per-hitter value
R	18	6	3	+100%	0.389	0.278	1.26	0	no per-hitter value
HR	18	1	1	0%	0.111	0.105	1	-0.059	no per-hitter value
2B	18	2	1	+100%	0.167	0.105	1.37	-0.086	no per-hitter value

What this slate says

AB: publishing the slate mean for every hitter would have scored a lower error (0.556 against 0.667), so the per-hitter projection added nothing on this slate.

H: slate total under-projected by 23% (10 against 13 real), which is a calibration problem in the rate or the at-bat volume.

H: publishing the slate mean for every hitter would have scored a lower error (0.562 against 0.722), so the per-hitter projection added nothing on this slate.

BB: slate total under-projected by 20% (8 against 10 real), which is a calibration problem in the rate or the at-bat volume.

BB: publishing the slate mean for every hitter would have scored a lower error (0.556 against 0.667), so the per-hitter projection added nothing on this slate.

SO: slate total under-projected by 14% (18 against 21 real), which is a calibration problem in the rate or the at-bat volume.

SO: publishing the slate mean for every hitter would have scored a lower error (0.648 against 0.944), so the per-hitter projection added nothing on this slate.

RBI: slate total over-projected by 100% (6 against 3 real), which is a calibration problem in the rate or the at-bat volume.

RBI: publishing the slate mean for every hitter would have scored a lower error (0.278 against 0.389), so the per-hitter projection added nothing on this slate.

R: slate total over-projected by 100% (6 against 3 real), which is a calibration problem in the rate or the at-bat volume.

R: publishing the slate mean for every hitter would have scored a lower error (0.278 against 0.389), so the per-hitter projection added nothing on this slate.

HR: publishing the slate mean for every hitter would have scored a lower error (0.105 against 0.111), so the per-hitter projection added nothing on this slate.

2B: slate total over-projected by 100% (2 against 1 real), which is a calibration problem in the rate or the at-bat volume.

2B: publishing the slate mean for every hitter would have scored a lower error (0.105 against 0.167), so the per-hitter projection added nothing on this slate.

Biggest per-hitter misses on the slate (actual / projected, ordered by run impact)

Hitter	Team	Game	Run impact	Line (H BB SO RBI R)
Andy Pages	LAD	20261011_LAD@MIL	-1.26	H 1/1 BB 0/1 SO 1/2 RBI 1/1 R 0/1
Brice Turang	MIL	20261011_LAD@MIL	1.26	H 1/1 BB 1/0 SO 0/1 RBI 1/0 R 1/1
Andrew Vaughn	MIL	20261011_LAD@MIL	1.11	H 1/0 BB 1/0 SO 1/1 RBI 0/1 R 0/0
Teoscar Hernández	LAD	20261011_LAD@MIL	0.94	H 2/0 BB 0/0 SO 1/1 RBI 0/0 R 0/0
Shohei Ohtani	LAD	20261011_LAD@MIL	0.8	H 1/0 BB 1/0 SO 1/1 RBI 0/0 R 1/0
Joey Ortiz	MIL	20261011_LAD@MIL	-0.8	H 0/1 BB 0/1 SO 2/1 RBI 0/1 R 0/0
Enrique Hernández	LAD	20261011_LAD@MIL	-0.78	H 0/1 BB 0/0 SO 0/1 RBI 0/0 R 0/1
William Contreras	MIL	20261011_LAD@MIL	-0.78	H 0/1 BB 1/1 SO 2/1 RBI 0/1 R 0/1
Kyle Tucker	LAD	20261011_LAD@MIL	0.66	H 0/0 BB 2/0 SO 2/1 RBI 0/0 R 0/0
Mookie Betts	LAD	20261011_LAD@MIL	0.47	H 1/0 BB 1/1 SO 0/0 RBI 0/0 R 0/0
Jackson Chourio	MIL	20261011_LAD@MIL	0.47	H 2/1 BB 0/0 SO 1/0 RBI 0/1 R 0/0
Cooper Pratt	MIL	20261011_LAD@MIL	-0.47	H 0/1 BB 0/0 SO 2/0 RBI 0/0 R 0/1
Max Muncy	LAD	20261011_LAD@MIL	-0.33	H 1/1 BB 0/1 SO 2/0 RBI 0/0 R 0/0
Garrett Mitchell	MIL	20261011_LAD@MIL	0.33	H 0/0 BB 1/0 SO 1/3 RBI 0/0 R 1/0
Freddie Freeman	LAD	20261011_LAD@MIL	0.14	H 1/0 BB 0/1 SO 1/2 RBI 0/0 R 0/0

Pipeline stage transitions

Winner-direction change at each stage boundary. A negative Δ accuracy is an intermediate regression even when a later stage recovers the pick.

Transition	Δ team-run MAE	Δ margin MAE
historical_ml → variance_applied	0.0157	-0.3521
variance_applied → enhanced_prediction	-1	0
enhanced_prediction → kpi_layer	0	0
kpi_layer → embedded_savant_run_environment	-0.5	1
embedded_savant_run_environment → fundamental_edge_target_review	0	0
fundamental_edge_target_review → capped_target	0	0
capped_target → simulation_result	0	0
simulation_result → published_final	0	0

Score distribution and margin compression

Narrow predicted margins next to wide actual margins is the signature the score-variance tail is too tight.

Predicted margins ≤2: 1 · Actual margins ≥4: 0

Game	Pred margin	Actual margin	Pred − actual
LAD@MIL	2	1	1

Cross-slate trends (persistent-pattern memory)

Window: 20261001, , 20261003, 20261004, 20261005, 20261006, 20261007, 20261008, , 20261010, 20261011 (10 prior slates loaded). Highest-confidence pick loss streak: 0 slate(s). Actionable pattern detected.

Escalated persistent patterns

A finding that has repeated at or beyond the escalation streak is no longer single-slate noise. It carries its target registered lever and an action-oriented guardrail.

Finding	Persistence	Target lever(s)	Direction	Guardrail
simulation_inert_on_slate	11 consecutive · 11 in window	simulation.allow_score	confirm the simulation module is running, not silently no-oping	simulation_inert_on_slate has persisted 11 consecutive slates; this is no longer single-slate noise. Treat it as an actionable pattern and raise the code/diagnostic action named; do not force a parameter move.

Finding streaks across the window

Finding	Consecutive slates	Incidence	Present now	Fix type
pipeline_winner_instability	0	1/11	no	code/diagnostic
score_margin_compression	0	2/11	no	param
simulation_inert_on_slate	11	11/11	yes	code/diagnostic
single_slate_calibration_instability	2	9/11	yes	param
weak_run_model_signal	2	9/11	yes	code/diagnostic

Metric direction across the window

Metric	Direction	Series (old → new)
winner_accuracy	improving	1.0, –, 1.0, 0.5, 0.0, 1.0, 0.75, 1.0, –, 1.0, 1.0
weighted_calibration_error	flat	0.4832, –, 0.4705, 0.4806, 0.5291, 0.4642, 0.2272, 0.495, –, 0.4732, 0.4518
margin_bias	improving	-3.0, –, -0.25, 0.0, -0.5, -0.5, -1.75, -3.0, –, -1.0, 1.0
team_run_error_on_losses	improving	–, –, –, 2.0, 3.5, –, 2.0, –, –, –, –

Source coverage receipt

Which reports actually fed the diagnosis. A source shown as missing was not silently assumed.

Source	Status	Evidence supplied	Path	Bytes
prediction review	loaded	official outcomes and scoring errors	/app/output/reports/prediction_review_20261011.json	11400
forensic deep audit	loaded	probability, provenance, starter and decision contracts	/app/output/reports/PREDICTION_SYSTEM_DEEP_AUDIT_20261011.json	30976
continuous learning	loaded	pipeline-stage quality, calibration, probability lineage, score distribution, and official starter comparisons	/app/output/learning/continuous_learning_daily_20261011.json	181366
detailed report	loaded	full game and simulation rendering	/app/output/reports/detailed_20261011.html	31609
ultra detailed player audit	loaded	player-level prediction trace	/app/output/reports/20261011_ultra_detailed_player_audit.html	1635801
structural audit	loaded	all invariant findings	/app/output/reports/audit_report_20261011.html	120679
integrity revision audit	loaded	structural audit of every post-start revision of the slate's games	/app/output/reports/audit_report_20261011_integrity_revision.html	344105

Per-game evidence coverage

Expected games

Artifacts loaded

Structural audits

Deep-audit games

Learning rows

Starter comparisons

Day over day: did it improve or worsen

Compared against packet 20261010. Last recorded change: v15.28.3 (2026-10-07) Audit the confidence blend against the adaptive cap actually in force.

Metric	Prior	Current	Δ	Result
brier_score	0.245	0.2449	-0.000101	improved
accuracy	0.6195	0.6195	+0	flat
team_run_mae	2.4292	2.4159	-0.0133	improved
total_run_mae	3.6549	3.6637	+0.0088	regressed
calibration_error	0.1325	0.1324	-0.0001	improved
starter_er_mae	1.6435	1.6226	-0.0209	improved
starter_outs_mae	4.2222	4.184	-0.0382	improved

Luna deep post-mortem (full)

1. Evidence receipt and gaps

All seven source_manifest entries are marked loaded:

Source	Status and stated purpose
Prediction review, `/app/output/reports/prediction_review_20261011.json` (11,400 bytes)	Loaded; official outcomes and scoring errors
Forensic deep audit, `/app/output/reports/PREDICTION_SYSTEM_DEEP_AUDIT_20261011.json` (30,976 bytes)	Loaded; probability, provenance, starter, and decision contracts
Continuous learning, `/app/output/learning/continuous_learning_daily_20261011.json` (181,366 bytes)	Loaded; stage quality, calibration, probability lineage, score distribution, starter comparisons
Detailed report, `/app/output/reports/detailed_20261011.html` (31,609 bytes)	Loaded; game and simulation rendering
Ultra-detailed player audit, `/app/output/reports/20261011_ultra_detailed_player_audit.html` (1,635,801 bytes)	Loaded; player-level prediction trace
Structural audit, `/app/output/reports/audit_report_20261011.html` (120,679 bytes)	Loaded; invariant findings
Integrity revision audit, `/app/output/reports/audit_report_20261011_integrity_revision.html` (344,105 bytes)	Loaded; structural audit of three post-start game revisions

Slate coverage: The diagnosis reports one expected game and one game reviewed; continuous-learning, forensic-deep-audit, starter-comparison, and structural-audit coverage are each 1/1. The reviewed game is LAD@MIL. No additional game is identified as missing by the supplied coverage matrix.

Gaps and internal contradictions:

The game-level structural audit reports 0 critical, 0 error, 0 warning, 109 good, and 1 info; its findings array is empty. The supplied data does not state the text of that single informational finding. Forecast-file integrity is separately described as clean, with zero defects and one informational note.
Probability provenance has an upstream field inconsistency: probability_lineage.raw_model_pick_probability is 0.5713, while model_trace.confidence_path.raw_pick_probability is 0.550827; the lineage also gives 0.550827 as the governor input. The record does not explain why the field called “raw model” differs from the value used as governor input. The final published probability and governed value do match closely, and the lineage marks no unexplained material delta; this is a provenance ambiguity, not evidence that publication bypassed a recorded transformation.
The revision-audit artifact is listed and loaded, but no revision-specific findings or details are present in the supplied diagnosis. Its contents cannot be independently summarized from this JSON.
The data is one settled game. That supports a complete slate review, but not stable single-slate conclusions about calibration or individual player effects.

2. Executive diagnosis

Winner accuracy: 1/1 (100%), MIL correctly predicted. This is a correct winner call, not a correct score forecast. The model’s holdout winner accuracy is 0.600414, and its walk-forward accuracy is 0.570149; one winning call does not validate those broader estimates.

Calibration quality: Published MIL win probability was 0.548241 (about 54.8%), with a home-minus-away probability edge of 0.0965 (about 9.65 percentage points). The pick was a hit, producing Brier score 0.204086. The single-slate calibration bucket had average confidence 0.5482 and observed hit rate 1.0, so confidence-minus-hit-rate was −0.4518; the monitor labels that bucket underconfident, and weighted absolute calibration error was 0.4518. This is a one-game bucket, not evidence for a stable calibration curve. A confidence edge of 0.12, if encountered elsewhere, is moderate—not a near coin flip; this game’s edge was smaller, about 0.10.

Score accuracy: The published score was LAD 2, MIL 4; the actual score was LAD 1, MIL 2. Winner was right, but both team-run forecasts were high: LAD by 1 and MIL by 2. The total was forecast at 6 versus 3 actual, a +3 total-run bias/error. Team-run absolute error is 3 as reported. Exact score quality should be treated cautiously: validated holdout run-model R² is only 0.002143 home, 0.030936 away, and 0.002415 total.

Starter accuracy: Both starter identities matched. Skubal was projected for 4 ER, 5.68 ERA, and 19 outs; actual was 2 ER, 3.60 ERA, and 15 outs. Misiorowski was projected for 1 ER, 1.69 ERA, and 16 outs; actual was 1 ER, 2.08 ERA, and 13 outs. The away starter substantially outperformed the projected ERA/ER line, while the home starter allowed the projected ER but had a higher actual ERA and fewer outs. This is mixed starter performance, not a uniform starter miss.

Artifact integrity: The per-game structural audit passed, with no critical/error/warning findings; forecast-file integrity is explicitly called clean. No artifact defect is identified as the cause of this game’s score error. The conflicting raw-probability fields remain a provenance ambiguity that should be reconciled, but the reported published probability matches the governed value and the lineage says no unexplained material delta.

Causal issue register, reconciled entry by entry:

High — single_slate_calibration_instability. Evidence: weighted absolute calibration error 0.4518 across one game, affecting LAD@MIL. Recommended action: watch the same confidence buckets over a rolling window before changing calibration weights. Guardrail: a single slate is far too small to establish a stable calibration curve. This high-severity label marks material calibration risk, not a critical evidence failure.
High — weak_run_model_signal. Evidence: home/away/total R² 0.0021/0.0309/0.0024, affecting LAD@MIL. Recommended action: treat exact scores as low-confidence; seek stronger features or a better target before tuning score tails. Guardrail: small positive R² only means better than guessing the average; it does not make exact scores reliable.
Info — complete_slate_audit_coverage. Evidence: 1/1 games structurally audited. Action: keep the per-game audit receipt in every report. Guardrail: full coverage does not cancel findings inside audited games.
Info — simulation_inert_on_slate. Evidence: 0 flip candidates, 0 published flips, affecting LAD@MIL. Action: measure simulation contribution over rolling slates, separating score realization from winner flipping. Guardrail: zero winner flips alone is not a defect and does not show that score reconciliation did no work. The cross-slate escalation changes the persistence diagnosis; see sections 7–8.

3. Every game

LAD@MIL — correct winner, wrong score

Prediction: LAD 2, MIL 4; predicted winner MIL.
Actual: LAD 1, MIL 2; actual winner MIL.
Winner result: Hit. Correct-winner/wrong-score game.
Published probability and edge: MIL/home probability 0.548241 (reported as 0.548); home-minus-away confidence edge 0.0965. Conviction bucket: lean. The diagnostic describes this as a system edge of about 0.10, not a half-scale distance from 50%.
Errors: Brier 0.204086; team-run absolute error 3; total forecast 6 versus actual 3, total bias/error +3. Predicted margin was MIL by 2; actual margin was MIL by 1, signed margin error +1 in the supplied margin distribution.
Fundamental edges: Season home-minus-away +0.003; recent −0.122; pitcher +0.0718 (rounded in the diagnostic to +0.072); composite +0.4891. Season quality adjustment was 0.0 because the quality edge was below its action threshold. Season-quality edge is separately reported as +0.108 and recent-quality edge as −0.192. These are distinct reported measures; they should not be conflated.
Confidence and recency controls: Calibration sample size 699; empirical hit rate 0.5751. The governor was applied; governed probability 0.5482. Recency discount factor is null, and the recency guardrail was not applied. Starter-experience shrinkage was not applied (minimum starter games 27; shrink factor 1.0). The diagnosis says the confidence pipeline reached 0.548 before later publication/recency controls and that published home probability was 0.548; the probability-lineage fields have the raw-value inconsistency noted in section 1.
Starter projections versus actuals:
LAD starter Tarik Skubal: projected 4 ER, 5.68 ERA, 19 outs; actual 2 ER, 3.60 ERA, 15 outs. Reported ER error −2.0, ERA error −2.08 (actual minus projected). Identity match: true.
MIL starter Jacob Misiorowski: projected 1 ER, 1.69 ERA, 16 outs; actual 1 ER, 2.08 ERA, 13 outs. Reported ER error 0.0, ERA error +0.39. Identity match: true.
Reported worst starter ER error: 2.0 in absolute magnitude. Both sides show zero bullpen ER in the player-level breakdown. No starter line is marked missing.
Score path and audit: Historical ML and later stage metrics are detailed in section 4. The final score is reported as unchanged from the simulation result to publication. Forecast integrity is clean; game structural audit is PASS, risk score 0, with no critical/error/warning findings and one informational finding. The audit itself does not identify a file defect as the reason for the score miss.
Important player-level evidence and adjustments: The pitcher adjustment was applied, with away-runs factor 0.9522 and home-runs factor 1.024; Savant adjustment was applied. No score-volatility change was made (no_change). The matchup breakdown attributes the home-side shortfall to offense; the away side is characterized as not materially breaking and closer to variance. The strongest listed contributors include Andy Pages (LAD, −1.26 runs versus projection), Brice Turang (MIL, +1.26), Andrew Vaughn (MIL, +1.11), Teoscar Hernández (LAD, +0.94), and Shohei Ohtani (LAD, +0.80). The learning analysis labels the latter four as learnable hitter deviations based on their reported season contact-quality tiers and xwOBA; it does not establish that a one-game player adjustment would improve future forecasts. The slate-level player metrics report generally weak per-hitter projections against a flat slate mean; see section 5.
Audit findings and causes: Primary and secondary driver fields are null. The supplied evidence supports a correct directional pick and an over-high run forecast, with home offense specifically identified as below projection. It does not support assigning the miss to a single starter, bullpen, or player. Causal attribution is therefore limited to the diagnosed run-environment/score error and the reported home-offense shortfall.

4. Miss reconstruction

There were no winner losses: misses 0, hits 1. There is consequently no losing pick for which to reconstruct the earliest stage that moved the predicted winner toward the wrong result. The score forecast did miss, and stage-level score metrics show where run and margin errors changed.

Score-stage quality, compared directly with the official final score:

Stage	Team-run MAE	Change vs historical ML	Total-run MAE	Margin MAE	Change in winner accuracy
`historical_ml`	2.9843	0.0000	5.9685	0.3521	Baseline; 1/1
`variance_applied`	3.0000	+0.0157	6.0000	0.0000	0
`enhanced_prediction`	2.0000	−0.9843	4.0000	0.0000	0
`kpi_layer`	2.0000	−0.9843	4.0000	0.0000	0
`embedded_savant_run_environment`	1.5000	−1.4843	3.0000	1.0000	0
`fundamental_edge_target_review`	1.5000	−1.4843	3.0000	1.0000	0
`capped_target`	1.5000	−1.4843	3.0000	1.0000	0
`simulation_result`	1.5000	−1.4843	3.0000	1.0000	0
`published_final`	1.5000	−1.4843	3.0000	1.0000	0

The transition log reports no changed-direction games and no winner-direction regressions. It does, however, show a score-metric tradeoff: historical ML → variance worsened team-run MAE by 0.0157 while margin MAE improved by 0.3521; variance → enhanced prediction improved team-run MAE by 1.0; KPI made no change; KPI → embedded run environment improved team-run MAE by 0.5 but worsened margin MAE by 1.0. Later improvements in team-run/total-run error do not erase the intermediate margin regression. The supplied per-stage table does not provide each stage’s exact predicted score, so those scores cannot be reconstructed from these MAEs alone.

Cause categories for this game:

Bad input: No identified game-input defect in the structural audit. The upstream raw-probability field mismatch is a provenance concern, not evidence that it changed the pick or score.
Bad weighting: The evidence does not isolate a specific weighting error for this game. The season and recent edges conflict in sign, but the winner was correct; one game cannot establish which evidence source was misweighted.
Calibration: The one-game published bucket is poorly aligned with observed outcome, but it cannot establish a stable calibration error. Published confidence was 0.548 and the pick hit.
Starter error: Mixed. Skubal allowed fewer ER and posted lower ERA than projected, but also worked fewer outs; Misiorowski matched projected ER, had a higher ERA, and worked fewer outs. No blown start or bullpen ER is reported.
Offense/bullpen: The game audit calls the MIL/home lineup’s output below projection; both teams’ bullpen ER are 0 in the supplied breakdown. The lineup/run shortfall is relevant to score accuracy, not a winner loss.
Run-environment miss: Strong immediate evidence: total was over-projected by 3, and lineups scored an average 1.5 fewer runs than projected per side. The 10-slate lineup-run-delta pattern is persistent by the supplied test (section 7), although it does not by itself identify which mean-run parameter caused this particular game.
Irreducible variance: The away-side player breakdown is characterized as closer to variance. Player-level deviations include both favorable and unfavorable results. This remains a possible contributor, not a quantified residual attribution.

Pipeline-stage audit across all stages and transitions: All nine recorded stages and all eight transitions are accounted for above. Winner accuracy remains 1/1 at every stage; changed-direction, improved-game, and regressed-game arrays are empty. The numerical MAE deltas still matter: variance_applied slightly worsened team-run MAE; enhanced_prediction improved it; the embedded environment stage improved team-run and total-run errors but regressed margin MAE. fundamental_edge_target_review, capped_target, simulation_result, and published_final then leave those metrics unchanged. A later score-error recovery therefore does not erase the intermediate margin regression.

5. Cross-game counterfactuals

There is one game, so “helps” and “hurts” can only name that game; the supplied lever_tension entries all explicitly report no helped games and no hurt games. That is absence of counterfactual evidence, not proof of neutrality.

Lever tension entry	Games it helps / hurts	Net evidence on changing it
`score.variance_multiplier / score.variance_floor` (widen run distributions); targets Brier and calibration error	Helps: none listed. Hurts: none listed.	No slate-specific help/hurt evidence. Do not infer that widening variance fixes the +3 total-run mean bias; variance is a spread lever.
`player.pitcher.v4 form weights / player.pitcher_prior_games`; targets starter ER/outs MAE	Helps: none listed. Hurts: none listed.	No counterfactual evidence. Starter outcomes were mixed; no game-based case for changing these weights.
`recent.* / season.* run-mean weighting`; targets team-run MAE, total-run MAE, accuracy	Helps: none listed. Hurts: none listed.	The game’s total was over-projected, but the single game does not show whether a recent/season reweighting would correct it. Cross-slate lineup evidence supports investigating the run environment, not a broad untested reweighting.

Tempting one-game fixes not justified: Do not raise MIL’s hitter projections solely from Turang and Vaughn’s results, lower LAD projections solely from Pages’ result, or adjust pitcher weights from these two starts. Do not change calibration weights because the one-game bucket error is large; the monitor expressly recommends rolling-window observation. Do not widen score variance as a substitute for correcting a possibly hot run mean. These changes either lack counterfactual support or risk learning from one-game noise.

6. Data and contract defects

Audit finding inventory available in the supplied diagnosis:

Critical: 0.
Error: 0.
Warning: 0.
Info: 1 in the structural audit; the exact text is not supplied. The causal register separately records two informational items: full 1/1 audit coverage and no simulation winner flip/candidate on this slate.
Good checks: 109 in the game structural-audit summary. Structural audit status PASS, risk score 0.
Coverage reconciliation: Coverage matrix is complete at 1/1 for expected games, games with starter comparisons, structural audits, completed structural audits, forensic deep audit, and continuous learning. This warrants calling coverage complete, not calling the slate defect-free: the calibration, run-model, stage, and persistence findings remain.

Model versus artifact defects:

Model/process risks: Very weak holdout run-model R²; single-slate confidence bucket misalignment; forecast total too high; and a persistent simulation-inert finding described below.
Presentation/artifact: Forecast-file integrity is explicitly clean (0 defects, 1 informational note). No structural-audit evidence attributes the score error to saved-artifact corruption.
Probability contract/provenance: The full pick-side lineage reports raw 0.5713 → calibrated 0.5482 → governor input 0.550827 → governor output 0.5482 → score-coherence probability 0.548241 → published 0.548241. The calibration sample size is 699; the governor is marked applied; recency_discount_factor is null and recency_guardrail_applied false. Published-minus-calibrated is +0.000041, with transformation described as publication matching the calibrated/governed value and unexplained_material_delta=false. The trace independently calls 0.550827 the raw pick probability, in conflict with lineage’s 0.5713 raw-model value. Thus, the final published value is accounted for by the recorded downstream transformation, and this is not a demonstrated material publication bypass; the upstream raw-field semantics/contract still need reconciliation.

7. Cross-slate persistence

Raw structural evidence:

Margin distribution: One current row: predicted MIL margin 2, actual margin 1, signed margin error +1. No actual margin was at least 4; one predicted margin was at most 2. The data is too small to infer a broad margin shape from this slate alone.
Pipeline transitions: Across the current slate, no games changed winner direction; the score-metric tradeoffs are detailed in section 4. Transition counts are one-game observations, not a persistent-stage estimate.
Calibration monitoring: One settled game; bucket 0.50–0.55 has average confidence 0.5482, hit rate 1.0, signed calibration error −0.4518, and weighted absolute error 0.4518. This is highly unstable as a single-game estimate.
High-raw cluster: Threshold 0.65; 0 games, 0 hits. There is no high-raw-cluster sample on this slate.
Simulation control: One observed game; 0 flip candidates and 0 published flips; status diagnostic_only_without_independent_support, priority P2.
Run-model validation: 2,415 rows, schema historical_game_temporal_v11, trained 2026-09-27; holdout R² 0.002143 home / 0.030936 away / 0.002415 total; walk-forward accuracy 0.570149, Brier 0.242204; validation winner accuracy 0.600414, Brier 0.239378. The near-zero run-target signal is a model-development concern, not something a one-game score fix resolves.
Cross-slate lineup bias: The actionable finding reports lineup actual-minus-projected runs mean −0.751, same sign on 7/10 slates, standard deviation 1.116, z = −2.13; direction is negative (lineups scored below projection). Its recommendation is to inspect the run environment before touching variance, and to treat score.variance_floor only as a spread lever. Starter ERA error is negative on 6/10 slates, mean −0.345, z = −0.83, and is explicitly on hold as not separable from noise. The current slate’s mean lineup delta is −1.5 across two sides and mean starter ERA error −0.84.
Cross-slate metric trends: margin_bias improving (mean −1.0); mean_team_run_error_on_losses improving (mean 2.5); weighted_calibration_error flat (mean 0.4528); winner_accuracy improving (mean 0.8056). “Improving” is the recorded direction, not a claim that all metrics are already good.
Top-confidence-pick loss streak: 0. Recorded per-slate outcomes include hits on 20261001, 20261003, 20261004, 20261006, 20261008, 20261010, and 20261011; misses on 20261005 and 20261007; and two unknown-date/unknown-result entries. The current top pick was a hit.

Every finding_streaks entry:

Finding	Consecutive-slate count	Present now?	Context
`pipeline_winner_instability`	0	No	Incidence 1 in an 11-slate window; no target lever.
`score_margin_compression`	0	No	Incidence 2; target levers `score.variance_multiplier`, `score.variance_floor`.
`simulation_inert_on_slate`	11	Yes	Incidence 11/11; targets `simulation.allow_score`; target metrics team-run and total-run MAE.
`single_slate_calibration_instability`	2	Yes	Incidence 9; targets `confidence.empirical_max_weight`, `confidence.base_cap`, `confidence.moderate_cap`.
`weak_run_model_signal`	2	Yes	Incidence 9; targets training XGBoost estimator count, depth, and learning rate; this finding is marked not parameter-actionable.

Escalated finding—established failure, not single-slate noise: simulation_inert_on_slate is explicitly escalated at 11 consecutive slates. Treat it as an established simulation/process failure pattern. The self-assessment gives a concrete suspected mechanism: the same draws are used both to search for a target-realizing play-by-play and to form the win-probability distribution, with early exit after finding a realization (default draw count around 2). It says this can make the win-rate/agreement signal vacuous. That is a supplied diagnosis, not an independently verified code execution result. The specified action is to confirm the module runs rather than silently no-oping, then separate target-score realization from an independent, unsteered win-probability batch (or a closed-form distribution), and feed a meaningful win-rate to the final policy.

8. Adjustments for today

Recommendations are ranked by evidence strength, persistence, scope, expected metric, affected games, and regression risk. These are proposed changes or shadow tests only; no changes have been applied.

Persistent, escalated simulation failure — code/diagnostic action, not a forced parameter move.

Registered target: simulation.allow_score; target metrics team_run_mae, total_run_mae.
Bounded action: Do not change the parameter based on this record: the finding is marked param_actionable=false, and the self-assessment specifically calls for an operator/code fix. In shadow, instrument whether the simulation is actually invoked, its draw count, early-exit reason, and whether an independent unsteered distribution is produced. Implement the stated separation of score-target realization and win-probability estimation, with batch size registered; compare a bounded 200–500 independent draws against a closed-form alternative as an isolated diagnostic.
Expected metric/direction: Establish that simulation contributes non-vacuous distribution information; then measure team-run and total-run MAE and winner-probability agreement across rolling slates. No numerical improvement is promised from this one slate.
Lever-tension games: None recorded as helped or hurt. Current relevant game, LAD@MIL, had no winner flip; its eventual score error does not prove the simulation caused it.
Risk: Moderate code/integration risk; shadow comparison and a rollback monitor are preferable to changing live behavior blindly.

Persistent lineup/run-environment overprojection — bounded shadow test, not a variance fix.

Evidence: The actionable cross-slate bias is negative on 7/10 slates, mean −0.751, z −2.13; current game’s total was over-projected by 3 and the two-side lineup delta averaged −1.5.
Registered lever: recent.* / season.* run-mean weighting is the available lever-tension entry targeting team-run MAE, total-run MAE, and accuracy. A small 2% reduction in the run-mean contribution of the currently over-projecting component, evaluated in shadow only, is a bounded test—not a claim that the JSON identifies which component is at fault. Do not use score.variance_floor to lower the mean: the bias register explicitly calls that a spread lever and says to check the run environment first.
Expected metric/direction: Team-run MAE and total-run MAE should decline if the run-mean overprojection is genuine; monitor winner accuracy as a guardrail.
Lever-tension games: The entry lists no helped or hurt games. LAD@MIL is consistent with the overprojection symptom, but is not a counterfactual. Regression risk is uncertain because the register supplies no game-level tension sample.
Gate: Require rolling, same-slate shadow comparison and rollback if winner accuracy worsens or run-error improvement fails to repeat.

Single-slate calibration anomaly — hold calibration parameters today.

Registered targets: confidence.empirical_max_weight, confidence.base_cap, confidence.moderate_cap.
Evidence and metric: Current weighted absolute calibration error 0.4518 on one settled game; cross-slate weighted calibration trend is flat, mean 0.4528. The finding has a two-slate consecutive streak, but the current bucket still has only one observation.
Recommendation: No live cap/weight change from this evidence. Record bucket-level sample counts and evaluate those exact registered parameters in a rolling shadow test; the current causal issue itself recommends waiting for the rolling bucket window.
Expected metric/direction: Brier score and calibration error should improve if a change is supported by adequate rolling data; guard against degraded winner accuracy.
Lever-tension games: None of the listed lever-tension entries concerns calibration caps, and none names helped/hurt games. LAD@MIL was a hit at 0.548, so it is not a calibration-loss example.

Weak run-model signal — prioritize feature/target work over nightly tuning.

Registered targets: training.xgb.n_estimators, training.xgb.max_depth, training.xgb.learning_rate, but the finding is marked param_actionable=false.
Recommendation: No bounded hyperparameter move. Follow the supplied operator action: improve features/target (including properly source-labelled market data when available, park/weather run environment, bullpen rest/fatigue, and platoon-adjusted lineup quality), then run an isolated same-fingerprint training backtest and bump the model schema version if feature semantics change.
Expected metric/direction: Improve holdout run-target R² and team-/total-run MAE in a controlled backtest. Current R² is near zero; a one-slate parameter change is not a sound remedy.
Lever-tension games: The current recent.* / season.* tension row lists none helped or hurt. LAD@MIL’s score error is one example, not proof that any of the training parameters will fix it.
Risk: Feature-contract and retraining changes have broader scope; require isolated validation before promotion.

No starter-weight change.

Starter ERA bias is explicitly on hold: 6/10 same sign, z −0.83, not separable from noise. The starter lever-tension entry lists no helped/hurt games. LAD@MIL’s starter outcomes were mixed. Preserve this as a diagnostic watch item rather than forcing a pitcher-weight adjustment.

9. Bottom line

Known: the only reviewed game, LAD@MIL, was a correct MIL winner call but a wrong score forecast (2–4 predicted versus 1–2 actual). The published probability was about 54.8%, with a 0.0965 home-minus-away edge; the pick hit. The total was over-projected by 3. Starter evidence is mixed, the per-game structural audit passed, and no artifact defect is identified as the cause.

Persistent evidence is more actionable than the one-game result: the run model has near-zero holdout R², lineup runs have been below projection on 7/10 slates, and the simulation-inert finding has persisted 11 consecutive slates and is escalated. Calibration error is flat across the reported trend window, but this game’s calibration bucket contains only one outcome. The upstream raw-probability fields conflict, even though the final published value matches the governed value and no material publication bypass is recorded.

Uncertain: which run-mean component caused this particular overprojection; whether any specific hitter, pitcher, or calibration adjustment would improve future slates; and whether the diagnosed simulation mechanism is the live code path without operator verification.

Safest next action for the very next slate: instrument and shadow-audit the simulation’s independent probability-distribution path—confirm it runs, record draw count and early-exit behavior, and do not change live confidence or variance parameters on this one-game result.

Full supporting reports: deep report (standalone / printable) · ultra player audit · detailed report · structural audit · post-fix revision audit.

Starting-pitcher projection quality

Metric	Value	Reading
Earned-run MAE	1	average earned runs off per starter
Earned-run bias	1	positive = projected more ER than allowed
Outs MAE	3.5	workload error, in outs
Identity match rate	100.0%	2 starters compared

Root-cause attribution of the worst run misses

Game	Total pred vs actual	Starting pitchers (projected vs actual ER)	Primary driver
LAD@MIL	6 vs 3	LAD Tarik Skubal proj 4 ER / actual 2 ER; MIL Jacob Misiorowski proj 1 ER / actual 1 ER	Offence / bullpen

Root-cause findings

Overall

No dominant failure pattern on this slate.

Losses were spread across causes with no single systematic driver.

Production is running candidate-668e4305abca59bc. This configuration was applied under operator-enabled direct mode and remains under the recorded rollback-monitor lifecycle.

Applied production change

Applied at 2026-10-07T09:21:41.221033+00:00. Active now: yes. The stored baseline and paired rollback predictions are retained for review.

Parameter	Before	After
`confidence.empirical_max_weight`	0.5	0.45

Proof of the value currently loaded by production

Parameter	Applied	Current live value	Verification
`confidence.empirical_max_weight`	0.45	0.45	verified live

Observed influence on predictions

paired_effect_observed · live predictions 7 · paired previous-config predictions 7 · published scores changed 0.

Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.

Game	Result	Live values recorded	Paired control	Live score ← previous config
20261007_CLE@CHW	hit	{'confidence.empirical_max_weight': 0.45}	yes	{'away_runs': 5.0, 'home_runs': 4.0} ← {'away_runs': 5.0, 'home_runs': 4.0}
20261007_LAD@ATL	hit	{'confidence.empirical_max_weight': 0.45}	yes	{'away_runs': 5.0, 'home_runs': 4.0} ← {'away_runs': 5.0, 'home_runs': 4.0}
20261007_MIL@SD	hit	{'confidence.empirical_max_weight': 0.45}	yes	{'away_runs': 4.0, 'home_runs': 5.0} ← {'away_runs': 4.0, 'home_runs': 5.0}
20261007_TB@NYY	miss	{'confidence.empirical_max_weight': 0.45}	yes	{'away_runs': 3.0, 'home_runs': 4.0} ← {'away_runs': 3.0, 'home_runs': 4.0}
20261008_CLE@CHW	hit	{'confidence.empirical_max_weight': 0.45}	yes	{'away_runs': 5.0, 'home_runs': 4.0} ← {'away_runs': 5.0, 'home_runs': 4.0}
20261010_CHW@CLE	hit	{'confidence.empirical_max_weight': 0.45}	yes	{'away_runs': 5.0, 'home_runs': 6.0} ← {'away_runs': 5.0, 'home_runs': 6.0}
20261011_LAD@MIL	hit	{'confidence.empirical_max_weight': 0.45}	yes	{'away_runs': 3.0, 'home_runs': 4.0} ← {'away_runs': 3.0, 'home_runs': 4.0}

Observed metric movement after application

This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.

Metric	At apply	Latest window	Delta	Direction
Brier score	0.245566	0.244879	-0.000687	improved
Winner accuracy	0.5877	0.6195	0.0318	improved
Team run MAE	2.4474	2.4159	-0.0315	improved
Total run MAE	3.6316	3.6637	0.0321	regressed
Calibration error	0.0992	0.1324	0.0332	regressed
Starter ER MAE	1.6795	1.6226	-0.0569	improved
Starter outs MAE	4.2393	4.184	-0.0553	improved

Suggested change history and decisions

This is rebuilt from the append-only candidate and application records on every publication, so later runs cannot erase an earlier proposal or production change.

Proposed UTC	Provider	Exact change	Decision	Measured outcome	Rationale
2026-08-25T06:01:43	gemini (legacy)	confidence.sparse_sample_max: 10 → 12	reject	DID NOT WORK: rejected (zero measured effect). Measured over 16 settled games: brier_score flat (0.0); accuracy flat (0.0); calibration_error flat (0.0); team_run_mae flat (0.0); total_run_mae flat (0.0).	Why and evidence Increasing confidence.sparse_sample_max from 10 to 15 may improve calibration for sparsely sampled games, as indicated by the underconfidence in the 0.60-0.65 bucket and the overconfidence in the 0.80-1.00 bucket, while staying within the allowed bounds and daily delta. { "evaluation_mode": "artifact_replay", "coverage": { "paired_games": 16, "distinct_slates": 2 }, "failed_gates": [ "minimum_paired_games", "minimum_distinct_slates" ], "metric_effects": { "brier_score": { "champion": 0.297161, "candidate": 0.297161, "difference": 0.0, "improvement": 0.0, "candidate_better": null }, "accuracy": { "champion": 0.3125, "candidate": 0.3125, "difference": 0.0, "improvement": null, "candidate_better": null }, "team_run_mae": { "champion": 2.5625, "candidate": 2.5625, "difference": 0.0, "improvement": null, "candidate_better": null }, "total_run_mae": { "champion": 4.125, "candidate": 4.125, "difference": 0.0, "improvement": null, "candidate_better": null }, "calibration_error": { "champion": 0.28026444, "candidate": 0.28026444, "difference": 0.0, "improvement": null, "candidate_better": null } } }
2026-08-27T06:02:02	openai	confidence.moderate_cap: 0.6 → 0.58	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence Reducing confidence.moderate_cap from 0.6 to 0.58 may improve overall calibration, as suggested by the errors in observed hit rates in moderate confidence buckets. { "evaluation_mode": "enhanced_shadow", "coverage": { "paired_games": 22, "distinct_slates": 2 }, "failed_gates": [ "minimum_paired_games", "minimum_distinct_slates" ], "metric_effects": { "brier_score": { "champion": 0.25333323, "candidate": 0.25333323, "difference": 0.0, "improvement": 0.0, "candidate_better": null }, "accuracy": { "champion": 0.54545455, "candidate": 0.54545455, "difference": 0.0, "improvement": null, "candidate_better": null }, "team_run_mae": { "champion": 2.52272727, "candidate": 2.52272727, "difference": 0.0, "improvement": null, "candidate_better": null }, "total_run_mae": { "champion": 3.40909091, "candidate": 3.40909091, "difference": 0.0, "improvement": null, "candidate_better": null }, "calibration_error": { "champion": 0.10832609, "candidate": 0.10832609, "difference": 0.0, "improvement": null, "candidate_better": null } } }
2026-09-04T06:03:08	openai	confidence.base_cap: 0.6 → 0.58	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence Lowering the default confidence cap modestly should reduce overconfidence in the 0.55-0.70 buckets and improve the primary Brier score, while limiting exposure to the recent high-conviction miss pattern. {}
2026-09-07T06:03:40	openai	score.variance_multiplier: 1.65 → 1.75	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence Increasing score variance modestly should reduce under-dispersed run margins and improve total-run error, while the shadow gate limits risk to winner calibration and run-model guardrails. {}
2026-09-12T09:04:27	openai	score.variance_multiplier: 1.75 → 1.85	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence A further small increase in score variance should reduce persistent margin compression and improve run-model error, building on the previously promoted variance increase while keeping the change within the registered daily bound. {}
2026-09-15T09:04:57	openai	score.variance_multiplier: 1.85 → 1.95	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence Increasing score variance modestly should reduce persistent run-margin compression and lower total-run error, building on two prior variance increases that worked and the current deep-slate evidence that wider tails helped the high-confidence BAL@NYM miss. {}
2026-09-18T09:05:32	openai	score.variance_multiplier: 1.95 → 2.05	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence Increasing score variance modestly from 1.95 to 2.05 may further reduce persistent margin compression and improve run-model error, supported by the regressing margin bias, the run-model loss incidence, and prior variance increases that worked. The shadow gate should detect any degradation in Brier score or calibration. {}
2026-09-21T09:05:52	openai	score.variance_multiplier: 2.05 → 2.15	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence Increasing score variance modestly from 2.05 to 2.15 may further reduce under-dispersed run margins and improve team-run error, supported by the leading run-model loss incidence, current team and total run MAE regressions, deep-slate evidence that wider tails helped two loss cases versus one hurt case, and the prior sequence of successful variance increases. The shadow gate should detect degradation in Brier score, calibration, or run guardrails. {}
2026-09-24T09:06:53	openai	confidence.base_cap: 0.58 → 0.56	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence Lowering the default confidence cap modestly should reduce persistent overconfidence in the 0.50-0.55 and 0.60-0.65 buckets and improve winner calibration and Brier score, while the shadow gate protects against worsening the underconfident 0.55-0.60 bucket. {}
2026-09-27T09:06:54	openai	score.variance_multiplier: 2.15 → 2.25	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence Increasing score variance modestly from 2.15 to 2.25 should reduce persistent run-margin compression and improve total-run error. This extends a sequence of previously successful variance increases and targets the currently regressing run-model metrics, while the shadow gate protects against degradation in Brier score, calibration, and run-error guardrails. {}
2026-10-07T09:21:29	openai	confidence.empirical_max_weight: 0.5 → 0.45	applied_to_production	APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.	Why and evidence Reduce the maximum weight assigned to empirical confidence from 0.50 to 0.45 to increase shrinkage, as the escalated calibration-instability finding recommends. This bounded shadow test targets the regressing calibration error; the mixed calibration bucket results make the direction uncertain. {}

AI proposal engine (OpenAI Luna)

Each night the verified evidence is sent to OpenAI (the Luna model). The provider may only propose one small, bounded change to a registered parameter, or abstain. It cannot promote, run commands, or write files.

This run: skipped open rollback monitor. Paused: a promoted change is still under rollback monitoring.

System recommendations sent to the provider

Recommended action	Reason
inspect_evidence_and_propose_or_abstain	minimum evidence gates are satisfied

Provider activity

Most recent recorded action: candidate promoted at 2026-10-07T09:21:41 UTC.

Full audit log (12 entries, newest first)

When (UTC)	Provider	Event	Detail
2026-10-07T09:21:41		candidate promoted
2026-10-07T09:21:29	openai	candidate created	Reduce the maximum weight assigned to empirical confidence from 0.50 to 0.45 to increase shrinkage, as the escalated calibration-instability finding recommends. This bounded shadow test targets the re
2026-09-27T09:07:01		candidate promoted
2026-09-27T09:06:54	openai	candidate created	Increasing score variance modestly from 2.15 to 2.25 should reduce persistent run-margin compression and improve total-run error. This extends a sequence of previously successful variance increases an
2026-09-24T09:07:04		candidate promoted
2026-09-24T09:06:53	openai	candidate created	Lowering the default confidence cap modestly should reduce persistent overconfidence in the 0.50-0.55 and 0.60-0.65 buckets and improve winner calibration and Brier score, while the shadow gate protec
2026-09-21T09:06:01		candidate promoted
2026-09-21T09:05:52	openai	candidate created	Increasing score variance modestly from 2.05 to 2.15 may further reduce under-dispersed run margins and improve team-run error, supported by the leading run-model loss incidence, current team and tota
2026-09-18T09:05:40		candidate promoted
2026-09-18T09:05:32	openai	candidate created	Increasing score variance modestly from 1.95 to 2.05 may further reduce persistent margin compression and improve run-model error, supported by the regressing margin bias, the run-model loss incidence
2026-09-15T09:05:08		candidate promoted
2026-09-15T09:04:57	openai	candidate created	Increasing score variance modestly should reduce persistent run-margin compression and lower total-run error, building on two prior variance increases that worked and the current deep-slate evidence t

Evidence: eligible True · settled games 113 · distinct slates 17

Adaptive tuning engine

Below is the background experiment that tests small tuning changes before any of them can reach production.

Production champion

candidate-668e4305abca59bc

sha256:02895b398be5e25b874cb24573f6272d7b4e2c440e5dad0b021995c628ed5ed3

Applied candidate

candidate-668e4305abca59bc

live · rollback armed

Decision

pending

0 gate(s) unmet

AI proposal

skipped open rollback monitor

Evidence coverage

A candidate needs this much matched history before it can be promoted or rejected.

Requirement	Have	Need	Progress
Paired shadow games	0	40
Distinct game days	0	4

Verified production performance

Measured on settled games with official finals. This is the champion actually in use.

Metric	Value	Sample	Direction
Brier score	0.2449	113	lower is better
Winner accuracy	62.0%	113	higher is better
Team run MAE	2.4159	113	lower is better
Total run MAE	3.6637	113	lower is better
Calibration error	0.1324	113	lower is better
Starter ER MAE	1.6226	212	lower is better
Starter outs MAE	4.184	212	lower is better

Candidate vs champion

No paired evaluation rows are available yet.

Unmet promotion gates

All gates satisfied.

Result ingestion (this run)

Stream	New	Already stored	Skipped
Game finals	1	112	0
Starter finals	2	216	2 unsettled

“New” is zero when every final for the day was already recorded on an earlier run.

Rollback monitor

{
  "status": "monitoring",
  "coverage": {
    "paired_games": 7,
    "distinct_slates": 4
  },
  "minimum_paired_games": 30,
  "minimum_distinct_slates": 3
}

Safety guarantees

✓ Production change recorded✓ Direct mode operator-enabled✓ Rollback monitor armed✓ data/ not modified✓ AI has no file/command authority

Raw status JSON (for debugging)

{
  "schema_version": "adaptive_status_report_v2",
  "date": "20261011",
  "generated_at_utc": "2026-10-12T09:21:33.063795+00:00",
  "champion": {
    "config_id": "candidate-668e4305abca59bc",
    "config_hash": "sha256:02895b398be5e25b874cb24573f6272d7b4e2c440e5dad0b021995c628ed5ed3"
  },
  "active_candidate": null,
  "ingestion": {
    "appended": 1,
    "repeated": 112,
    "errors": [],
    "skipped_unverifiable_pregame": 0,
    "starter_actuals": {
      "appended": 2,
      "repeated": 216,
      "skipped": 0,
      "skipped_unsettled": 2,
      "errors": []
    }
  },
  "evaluation": {
    "candidate": {
      "status": "no_active_candidate"
    },
    "rollback_monitoring": {
      "status": "monitoring",
      "coverage": {
        "paired_games": 7,
        "distinct_slates": 4
      },
      "minimum_paired_games": 30,
      "minimum_distinct_slates": 3
    }
  },
  "proposal": {
    "status": "skipped_open_rollback_monitor"
  },
  "proposal_eligibility": {
    "eligible": true,
    "settled_games": 113,
    "distinct_slates": 17,
    "reasons": []
  },
  "recommended_actions": [
    {
      "action": "inspect_evidence_and_propose_or_abstain",
      "reason": "minimum evidence gates are satisfied"
    }
  ],
  "proposal_history": [
    {
      "occurred_at_utc": "2026-09-15T09:04:57.841906+00:00",
      "provider": "openai",
      "event": "candidate_created",
      "detail": "Increasing score variance modestly should reduce persistent run-margin compression and lower total-run error, building on two prior variance increases that worked and the current deep-slate evidence t"
    },
    {
      "occurred_at_utc": "2026-09-15T09:05:08.617033+00:00",
      "provider": "",
      "event": "candidate_promoted",
      "detail": ""
    },
    {
      "occurred_at_utc": "2026-09-18T09:05:32.063728+00:00",
      "provider": "openai",
      "event": "candidate_created",
      "detail": "Increasing score variance modestly from 1.95 to 2.05 may further reduce persistent margin compression and improve run-model error, supported by the regressing margin bias, the run-model loss incidence"
    },
    {
      "occurred_at_utc": "2026-09-18T09:05:40.012512+00:00",
      "provider": "",
      "event": "candidate_promoted",
      "detail": ""
    },
    {
      "occurred_at_utc": "2026-09-21T09:05:52.468019+00:00",
      "provider": "openai",
      "event": "candidate_created",
      "detail": "Increasing score variance modestly from 2.05 to 2.15 may further reduce under-dispersed run margins and improve team-run error, supported by the leading run-model loss incidence, current team and tota"
    },
    {
      "occurred_at_utc": "2026-09-21T09:06:01.333346+00:00",
      "provider": "",
      "event": "candidate_promoted",
      "detail": ""
    },
    {
      "occurred_at_utc": "2026-09-24T09:06:53.755755+00:00",
      "provider": "openai",
      "event": "candidate_created",
      "detail": "Lowering the default confidence cap modestly should reduce persistent overconfidence in the 0.50-0.55 and 0.60-0.65 buckets and improve winner calibration and Brier score, while the shadow gate protec"
    },
    {
      "occurred_at_utc": "2026-09-24T09:07:04.378558+00:00",
      "provider": "",
      "event": "candidate_promoted",
      "detail": ""
    },
    {
      "occurred_at_utc": "2026-09-27T09:06:54.240008+00:00",
      "provider": "openai",
      "event": "candidate_created",
      "detail": "Increasing score variance modestly from 2.15 to 2.25 should reduce persistent run-margin compression and improve total-run error. This extends a sequence of previously successful variance increases an"
    },
    {
      "occurred_at_utc": "2026-09-27T09:07:01.435173+00:00",
      "provider": "",
      "event": "candidate_promoted",
      "detail": ""
    },
    {
      "occurred_at_utc": "2026-10-07T09:21:29.935210+00:00",
      "provider": "openai",
      "event": "candidate_created",
      "detail": "Reduce the maximum weight assigned to empirical confidence from 0.50 to 0.45 to increase shrinkage, as the escalated calibration-instability finding recommends. This bounded shadow test targets the re"
    },
    {
      "occurred_at_utc": "2026-10-07T09:21:41.221033+00:00",
      "provider": "",
      "event": "candidate_promoted",
      "detail": ""
    }
  ],
  "evaluation_thresholds": {
    "minimum_paired_games": 40,
    "minimum_distinct_slates": 4
  },
  "slate_review": {
    "schema_version": "adaptive_slate_review_v3",
    "date": "20261011",
    "games_reviewed": 1,
    "hits": 1,
    "misses": 0,
    "winner_accuracy": 1.0,
    "mean_brier_score": 0.2041,
    "coin_flip_baseline_brier": 0.25,
    "mean_run_error": 3.0,
    "total_run_bias_pred_minus_actual": 3.0,
    "total_run_mae": 3.0,
    "near_coin_flip_games": 0,
    "strong_conviction_games": 0,
    "max_conviction": 0.0965,
    "loss_reason_counts": {},
    "metric_incidence": {
      "losses": 0,
      "by_dominant_metric": {},
      "leading_metric": null,
      "mean_total_run_error_on_losses": null,
      "mean_team_run_error_on_losses": null,
      "mean_winner_brier_on_losses": null,
      "mean_starter_er_error_on_losses": null,
      "headline": null
    },
    "starter_quality": {
      "starters": 2,
      "identity_match_rate": 1.0,
      "er_mae": 1.0,
      "er_bias_pred_minus_actual": 1.0,
      "outs_mae": 3.5
    },
    "worst_run_misses": [
      {
        "game_id": "20261011_LAD@MIL",
        "matchup": "LAD@MIL",
        "predicted_total": 6.0,
        "actual_total": 3.0,
        "run_error": 3.0,
        "starters": [
          {
            "team": "LAD",
            "name": "Tarik Skubal",
            "predicted_er": 4.0,
            "actual_er": 2.0,
            "er_miss": -2.0,
            "predicted_outs": 19.0,
            "actual_outs": 15.0
          },
          {
            "team": "MIL",
            "name": "Jacob Misiorowski",
            "predicted_er": 1.0,
            "actual_er": 1.0,
            "er_miss": 0.0,
            "predicted_outs": 16.0,
            "actual_outs": 13.0
          }
        ],
        "primary_driver": "offence_or_bullpen"
      }
    ],
    "games": [
      {
        "game_id": "20261011_LAD@MIL",
        "matchup": "LAD@MIL",
        "pick": "MIL",
        "winner": "MIL",
        "outcome": "hit",
        "home_win_probability": 0.5482,
        "conviction": 0.0965,
        "predicted_away_runs": 2.0,
        "predicted_home_runs": 4.0,
        "actual_away_runs": 1.0,
        "actual_home_runs": 2.0,
        "total_predicted": 6.0,
        "total_actual": 3.0,
        "run_error": 3.0,
        "brier_score": 0.204086,
        "loss_reason": null,
        "metric_errors": {
          "winner_brier": 0.204086,
          "team_run_error": 3.0,
          "total_run_error": 3.0,
          "starter_er_error": 2.0
        },
        "dominant_metric": null
      }
    ],
    "findings": [
      {
        "area": "Overall",
        "title": "No dominant failure pattern on this slate.",
        "detail": "Losses were spread across causes with no single systematic driver."
      }
    ],
    "priority_actions": [],
    "deep_diagnosis": "<rendered above; omitted from debug dump>",
    "deep_report_path": "/app/output/reports/deep_slate_report_20261011.html",
    "luna_analysis_status": "received",
    "luna_analysis_quality": {
      "complete": true,
      "characters": 30302,
      "minimum_characters": 6000,
      "sections_present": [
        1,
        2,
        3,
        4,
        5,
        6,
        7,
        8,
        9
      ],
      "sections_required": [
        1,
        2,
        3,
        4,
        5,
        6,
        7,
        8,
        9
      ],
      "games_present": 1,
      "games_required": 1,
      "missing_games": [],
      "causes_present": 4,
      "causes_required": 4,
      "missing_causes": [],
      "sources_present": 7,
      "sources_required": 7,
      "missing_sources": [],
      "coverage_score": 125
    },
    "luna_analysis": "<rendered above; omitted from debug dump>",
    "change_impact": "<rendered above; omitted from debug dump>"
  },
  "metrics": {
    "brier_score": {
      "status": "measured",
      "value": 0.244879,
      "sample_size": 113,
      "baseline_value": 0.24498,
      "difference": -0.000101,
      "direction": "improved",
      "minimum_required_sample": 20,
      "confidence_interval": null,
      "evidence_ids": [
        "metric.brier_score"
      ]
    },
    "accuracy": {
      "status": "measured",
      "value": 0.6195,
      "sample_size": 113,
      "baseline_value": 0.6195,
      "difference": 0.0,
      "direction": "stable",
      "minimum_required_sample": 20,
      "confidence_interval": null,
      "evidence_ids": [
        "summary.settled_results"
      ]
    },
    "team_run_mae": {
      "status": "measured",
      "value": 2.4159,
      "sample_size": 113,
      "baseline_value": 2.4292,
      "difference": -0.0133,
      "direction": "improved",
      "minimum_required_sample": 20,
      "confidence_interval": null,
      "evidence_ids": [
        "pipeline.stage.published_final"
      ]
    },
    "total_run_mae": {
      "status": "measured",
      "value": 3.6637,
      "sample_size": 113,
      "baseline_value": 3.6549,
      "difference": 0.0088,
      "direction": "regressed",
      "minimum_required_sample": 20,
      "confidence_interval": null,
      "evidence_ids": [
        "pipeline.stage.published_final"
      ]
    },
    "calibration_error": {
      "status": "measured",
      "value": 0.1324,
      "sample_size": 113,
      "baseline_value": 0.1325,
      "difference": -0.0001,
      "direction": "improved",
      "minimum_required_sample": 20,
      "confidence_interval": null,
      "evidence_ids": [
        "calibration.weighted_absolute_error"
      ]
    },
    "starter_er_mae": {
      "status": "measured",
      "value": 1.6226,
      "sample_size": 212,
      "baseline_value": 1.6435,
      "difference": -0.0209,
      "direction": "improved",
      "minimum_required_sample": 40,
      "confidence_interval": null,
      "evidence_ids": [
        "starter.baseline.er_mae"
      ]
    },
    "starter_outs_mae": {
      "status": "measured",
      "value": 4.184,
      "sample_size": 212,
      "baseline_value": 4.2222,
      "difference": -0.0382,
      "direction": "improved",
      "minimum_required_sample": 40,
      "confidence_interval": null,
      "evidence_ids": [
        "starter.baseline.outs_mae"
      ]
    }
  },
  "previous_experiments": [
    {
      "experiment_id": "candidate-c7e288f75d707709",
      "proposed_at_utc": "2026-08-25T06:01:43.820174+00:00",
      "provider": "gemini",
      "hypothesis": "Increasing confidence.sparse_sample_max from 10 to 15 may improve calibration for sparsely sampled games, as indicated by the underconfidence in the 0.60-0.65 bucket and the overconfidence in the 0.80-1.00 bucket, while staying within the allowed bounds and daily delta.",
      "changes": [
        {
          "parameter": "confidence.sparse_sample_max",
          "old_value": 10,
          "new_value": 12,
          "expected_metric": "calibration_error",
          "expected_direction": "decrease",
          "evidence_ids": [
            "calibration.bucket.0.60-0.65",
            "calibration.bucket.0.80-1.00"
          ],
          "risk_tier": "low",
          "evaluation_mode": "artifact_replay"
        }
      ],
      "decision": "reject",
      "reason": "zero measured effect",
      "decided_at_utc": "2026-08-26T22:29:59.797443+00:00",
      "measured_effect": {
        "evaluation_mode": "artifact_replay",
        "coverage": {
          "paired_games": 16,
          "distinct_slates": 2
        },
        "failed_gates": [
          "minimum_paired_games",
          "minimum_distinct_slates"
        ],
        "metric_effects": {
          "brier_score": {
            "champion": 0.297161,
            "candidate": 0.297161,
            "difference": 0.0,
            "improvement": 0.0,
            "candidate_better": null
          },
          "accuracy": {
            "champion": 0.3125,
            "candidate": 0.3125,
            "difference": 0.0,
            "improvement": null,
            "candidate_better": null
          },
          "team_run_mae": {
            "champion": 2.5625,
            "candidate": 2.5625,
            "difference": 0.0,
            "improvement": null,
            "candidate_better": null
          },
          "total_run_mae": {
            "champion": 4.125,
            "candidate": 4.125,
            "difference": 0.0,
            "improvement": null,
            "candidate_better": null
          },
          "calibration_error": {
            "champion": 0.28026444,
            "candidate": 0.28026444,
            "difference": 0.0,
            "improvement": null,
            "candidate_better": null
          }
        }
      },
      "outcome": "DID NOT WORK: rejected (zero measured effect). Measured over 16 settled games: brier_score flat (0.0); accuracy flat (0.0); calibration_error flat (0.0); team_run_mae flat (0.0); total_run_mae flat (0.0).",
      "worked": false
    },
    {
      "experiment_id": "candidate-31a39524087cf556",
      "proposed_at_utc": "2026-08-27T06:02:02.124992+00:00",
      "provider": "openai",
      "hypothesis": "Reducing confidence.moderate_cap from 0.6 to 0.58 may improve overall calibration, as suggested by the errors in observed hit rates in moderate confidence buckets.",
      "changes": [
        {
          "parameter": "confidence.moderate_cap",
          "old_value": 0.6,
          "new_value": 0.58,
          "expected_metric": "calibration_error",
          "expected_direction": "decrease",
          "evidence_ids": [
            "calibration.bucket.0.55-0.60",
            "calibration.bucket.0.60-0.65",
            "calibration.bucket.0.65-0.70"
          ],
          "risk_tier": "low",
          "evaluation_mode": "enhanced_shadow"
        }
      ],
      "decision": "applied_to_production",
      "reason": "Operator-enabled direct apply; rollback monitoring remains authoritative.",
      "decided_at_utc": "2026-08-29T06:02:24.658142+00:00",
      "measured_effect": {
        "evaluation_mode": "enhanced_shadow",
        "coverage": {
          "paired_games": 22,
          "distinct_slates": 2
        },
        "failed_gates": [
          "minimum_paired_games",
          "minimum_distinct_slates"
        ],
        "metric_effects": {
          "brier_score": {
            "champion": 0.25333323,
            "candidate": 0.25333323,
            "difference": 0.0,
            "improvement": 0.0,
            "candidate_better": null
          },
          "accuracy": {
            "champion": 0.54545455,
            "candidate": 0.54545455,
            "difference": 0.0,
            "improvement": null,
            "candidate_better": null
          },
          "team_run_mae": {
            "champion": 2.52272727,
            "candidate": 2.52272727,
            "difference": 0.0,
            "improvement": null,
            "candidate_better": null
          },
          "total_run_mae": {
            "champion": 3.40909091,
            "candidate": 3.40909091,
            "difference": 0.0,
            "improvement": null,
            "candidate_better": null
          },
          "calibration_error": {
            "champion": 0.10832609,
            "candidate": 0.10832609,
            "difference": 0.0,
            "improvement": null,
            "candidate_better": null
          }
        }
      },
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
      "application": {
        "date": "20260828",
        "applied_at_utc": "2026-08-29T06:02:24.658142+00:00",
        "candidate_id": "candidate-31a39524087cf556",
        "changes": [
          {
            "parameter": "confidence.moderate_cap",
            "old_value": 0.6,
            "new_value": 0.58
          }
        ],
        "config_before": "default-v1",
        "config_after": "candidate-31a39524087cf556",
        "metric_baseline_at_apply": {
          "brier_score": 0.241798,
          "accuracy": 0.5833,
          "team_run_mae": 2.4306,
          "total_run_mae": 3.713,
          "calibration_error": 0.1004,
          "starter_er_mae": 1.7854,
          "starter_outs_mae": 3.1073
        },
        "status": "applied_to_production",
        "previous_config_id": "default-v1",
        "new_config_id": "candidate-31a39524087cf556",
        "currently_active": false,
        "live_parameter_checks": [
          {
            "parameter": "confidence.moderate_cap",
            "old_value": 0.6,
            "applied_value": 0.58,
            "current_live_value": 0.58,
            "currently_matches_applied_value": true
          }
        ],
        "metric_observation": {
          "status": "observed_later_rolling_window",
          "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
          "metrics": [
            {
              "metric": "brier_score",
              "baseline_at_apply": 0.241798,
              "latest_window": 0.244879,
              "delta": 0.003081,
              "direction": "regressed",
              "sample_size": 113
            },
            {
              "metric": "accuracy",
              "baseline_at_apply": 0.5833,
              "latest_window": 0.6195,
              "delta": 0.0362,
              "direction": "improved",
              "sample_size": 113
            },
            {
              "metric": "team_run_mae",
              "baseline_at_apply": 2.4306,
              "latest_window": 2.4159,
              "delta": -0.0147,
              "direction": "improved",
              "sample_size": 113
            },
            {
              "metric": "total_run_mae",
              "baseline_at_apply": 3.713,
              "latest_window": 3.6637,
              "delta": -0.0493,
              "direction": "improved",
              "sample_size": 113
            },
            {
              "metric": "calibration_error",
              "baseline_at_apply": 0.1004,
              "latest_window": 0.1324,
              "delta": 0.032,
              "direction": "regressed",
              "sample_size": 113
            },
            {
              "metric": "starter_er_mae",
              "baseline_at_apply": 1.7854,
              "latest_window": 1.6226,
              "delta": -0.1628,
              "direction": "improved",
              "sample_size": 212
            },
            {
              "metric": "starter_outs_mae",
              "baseline_at_apply": 3.1073,
              "latest_window": 4.184,
              "delta": 1.0767,
              "direction": "regressed",
              "sample_size": 212
            }
          ]
        },
        "prediction_influence": {
          "status": "paired_effect_observed",
          "live_predictions_recorded": 66,
          "paired_previous_config_predictions": 43,
          "changed_score_predictions": 0,
          "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
          "games": [
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              "date": "20260831",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
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              "live_home_win_probability": 0.6655,
              "previous_home_win_probability": 0.6655,
              "home_probability_delta": 0.0,
              "live_predicted_score": {
                "away_runs": 3.0,
                "home_runs": 5.0
              },
              "previous_predicted_score": {
                "away_runs": 3.0,
                "home_runs": 5.0
              },
              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260831_NYY@LAA",
              "date": "20260831",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              "paired_previous_config": true,
              "live_home_win_probability": 0.411987,
              "previous_home_win_probability": 0.411987,
              "home_probability_delta": 0.0,
              "live_predicted_score": {
                "away_runs": 5.0,
                "home_runs": 4.0
              },
              "previous_predicted_score": {
                "away_runs": 5.0,
                "home_runs": 4.0
              },
              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260831_OAK@TEX",
              "date": "20260831",
              "outcome": "hit",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              "paired_previous_config": true,
              "live_home_win_probability": 0.546614,
              "previous_home_win_probability": 0.546614,
              "home_probability_delta": 0.0,
              "live_predicted_score": {
                "away_runs": 4.0,
                "home_runs": 5.0
              },
              "previous_predicted_score": {
                "away_runs": 4.0,
                "home_runs": 5.0
              },
              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260831_PHI@ARI",
              "date": "20260831",
              "outcome": "hit",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
                "confidence.moderate_cap": 0.58
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              "all_changed_values_verified": true,
              "paired_previous_config": true,
              "live_home_win_probability": 0.453386,
              "previous_home_win_probability": 0.453386,
              "home_probability_delta": 0.0,
              "live_predicted_score": {
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                "home_runs": 4.0
              },
              "previous_predicted_score": {
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                "home_runs": 4.0
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260831_SD@CIN",
              "date": "20260831",
              "outcome": "hit",
              "live_config_id": "candidate-31a39524087cf556",
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              "live_predicted_score": {
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                "home_runs": 4.0
              },
              "previous_predicted_score": {
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                "home_runs": 4.0
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260831_SEA@BOS",
              "date": "20260831",
              "outcome": "hit",
              "live_config_id": "candidate-31a39524087cf556",
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              "all_changed_values_verified": true,
              "paired_previous_config": true,
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              "home_probability_delta": 0.0,
              "live_predicted_score": {
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                "home_runs": 3.0
              },
              "previous_predicted_score": {
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                "home_runs": 3.0
              },
              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260831_SF@ATL",
              "date": "20260831",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
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              "live_predicted_score": {
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                "home_runs": 5.0
              },
              "previous_predicted_score": {
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                "home_runs": 5.0
              },
              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260901_ATL@WAS",
              "date": "20260901",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              "live_predicted_score": {
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                "home_runs": 5.0
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            {
              "game_id": "20260901_BAL@COL",
              "date": "20260901",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              "live_predicted_score": {
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                "home_runs": 4.0
              },
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            },
            {
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              "date": "20260901",
              "outcome": "hit",
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              "live_predicted_score": {
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                "home_runs": 5.0
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            {
              "game_id": "20260901_DET@MIN",
              "date": "20260901",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              "live_predicted_score": {
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                "home_runs": 3.0
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            },
            {
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              "date": "20260901",
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              "live_predicted_score": {
                "away_runs": 3.0,
                "home_runs": 5.0
              },
              "previous_predicted_score": null,
              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260901_MIL@CHC",
              "date": "20260901",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              "live_predicted_score": {
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                "home_runs": 8.0
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              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260901_NYM@TB",
              "date": "20260901",
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              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              "live_predicted_score": {
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                "home_runs": 6.0
              },
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            },
            {
              "game_id": "20260901_NYY@LAA",
              "date": "20260901",
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              "live_predicted_score": {
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              },
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              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260901_OAK@TEX",
              "date": "20260901",
              "outcome": "hit",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              },
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              "live_predicted_score": {
                "away_runs": 4.0,
                "home_runs": 5.0
              },
              "previous_predicted_score": null,
              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260901_PHI@ARI",
              "date": "20260901",
              "outcome": "hit",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
                "confidence.moderate_cap": 0.58
              },
              "all_changed_values_verified": true,
              "paired_previous_config": false,
              "live_home_win_probability": null,
              "previous_home_win_probability": null,
              "home_probability_delta": null,
              "live_predicted_score": {
                "away_runs": 4.0,
                "home_runs": 3.0
              },
              "previous_predicted_score": null,
              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260901_SD@CIN",
              "date": "20260901",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
                "confidence.moderate_cap": 0.58
              },
              "all_changed_values_verified": true,
              "paired_previous_config": false,
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              "live_predicted_score": {
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                "home_runs": 4.0
              },
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              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260901_SEA@BOS",
              "date": "20260901",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              },
              "all_changed_values_verified": true,
              "paired_previous_config": false,
              "live_home_win_probability": null,
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              "home_probability_delta": null,
              "live_predicted_score": {
                "away_runs": 3.0,
                "home_runs": 5.0
              },
              "previous_predicted_score": null,
              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260901_SF@PIT",
              "date": "20260901",
              "outcome": "hit",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              "all_changed_values_verified": true,
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              "live_predicted_score": {
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                "home_runs": 6.0
              },
              "previous_predicted_score": null,
              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260901_STL@LAD",
              "date": "20260901",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              "all_changed_values_verified": true,
              "paired_previous_config": false,
              "live_home_win_probability": null,
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              "home_probability_delta": null,
              "live_predicted_score": {
                "away_runs": 5.0,
                "home_runs": 7.0
              },
              "previous_predicted_score": null,
              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260901_TOR@CLE",
              "date": "20260901",
              "outcome": "hit",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
                "confidence.moderate_cap": 0.58
              },
              "all_changed_values_verified": true,
              "paired_previous_config": false,
              "live_home_win_probability": null,
              "previous_home_win_probability": null,
              "home_probability_delta": null,
              "live_predicted_score": {
                "away_runs": 4.0,
                "home_runs": 5.0
              },
              "previous_predicted_score": null,
              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260902_ATL@WAS",
              "date": "20260902",
              "outcome": "hit",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
                "confidence.moderate_cap": 0.58
              },
              "all_changed_values_verified": true,
              "paired_previous_config": false,
              "live_home_win_probability": null,
              "previous_home_win_probability": null,
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              "live_predicted_score": {
                "away_runs": 5.0,
                "home_runs": 4.0
              },
              "previous_predicted_score": null,
              "score_changed_vs_previous_config": null
            },
            {
              "game_id": "20260902_BAL@COL",
              "date": "20260902",
              "outcome": "miss",
              "live_config_id": "candidate-31a39524087cf556",
              "parameter_values_recorded_in_prediction": {
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              },
              "all_changed_values_verified": true,
              "paired_previous_config": false,
              "live_home_win_probability": null,
              "previous_home_win_probability": null,
              "home_probability_delta": null,
              "live_predicted_score": {
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                "home_runs": 5.0
              },
              "previous_predicted_score": null,
              "score_changed_vs_previous_config": null
            },
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    },
    {
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          ]
        },
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      }
    },
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            },
            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            },
            {
              "game_id": "20260908_WAS@SD",
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              "previous_predicted_score": {
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          ]
        },
        "measurement_status": "paired_effect_observed",
        "rollback_monitor": {
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          "coverage": {
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            "distinct_slates": 4
          },
          "minimum_paired_games": 30,
          "minimum_distinct_slates": 3
        }
      }
    },
    {
      "experiment_id": "candidate-ed18fee8af33cf7e",
      "proposed_at_utc": "2026-09-12T09:04:27.150731+00:00",
      "provider": "openai",
      "hypothesis": "A further small increase in score variance should reduce persistent margin compression and improve run-model error, building on the previously promoted variance increase while keeping the change within the registered daily bound.",
      "changes": [
        {
          "parameter": "score.variance_multiplier",
          "old_value": 1.75,
          "new_value": 1.85,
          "expected_metric": "total_run_mae",
          "expected_direction": "decrease",
          "evidence_ids": [
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            "pipeline.largest_team_run_regression",
            "errors.miss_reasons"
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          "risk_tier": "low",
          "evaluation_mode": "artifact_replay"
        }
      ],
      "decision": "applied_to_production",
      "reason": "Operator-enabled direct apply; rollback monitoring remains authoritative.",
      "decided_at_utc": "2026-09-12T09:04:36.607786+00:00",
      "measured_effect": null,
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
      "application": {
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        "applied_at_utc": "2026-09-12T09:04:36.607786+00:00",
        "candidate_id": "candidate-ed18fee8af33cf7e",
        "changes": [
          {
            "parameter": "score.variance_multiplier",
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            "new_value": 1.85
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        ],
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        "config_after": "candidate-ed18fee8af33cf7e",
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          "team_run_mae": 2.4417,
          "total_run_mae": 3.7167,
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          "starter_er_mae": 1.8716,
          "starter_outs_mae": 3.1284
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        "status": "applied_to_production",
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        "new_config_id": "candidate-ed18fee8af33cf7e",
        "currently_active": false,
        "live_parameter_checks": [
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            "old_value": 1.75,
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            "current_live_value": 2.25,
            "currently_matches_applied_value": false
          }
        ],
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          "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
          "metrics": [
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            },
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              "sample_size": 113
            },
            {
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            },
            {
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              "latest_window": 3.6637,
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              "direction": "improved",
              "sample_size": 113
            },
            {
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              "latest_window": 0.1324,
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            },
            {
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              "latest_window": 1.6226,
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              "sample_size": 212
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            {
              "metric": "starter_outs_mae",
              "baseline_at_apply": 3.1284,
              "latest_window": 4.184,
              "delta": 1.0556,
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              "sample_size": 212
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          "paired_previous_config_predictions": 40,
          "changed_score_predictions": 0,
          "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
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            },
            {
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            {
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            },
            {
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            },
            {
              "game_id": "20260912_TEX@ARI",
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              "previous_predicted_score": {
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260913_BAL@TOR",
              "date": "20260913",
              "outcome": "hit",
              "live_config_id": "candidate-ed18fee8af33cf7e",
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              "previous_predicted_score": {
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            {
              "game_id": "20260913_CHW@STL",
              "date": "20260913",
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              "all_changed_values_verified": true,
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              "home_probability_delta": -4.6e-05,
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260913_CIN@MIL",
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          "paired_previous_config_predictions": 39,
          "changed_score_predictions": 0,
          "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
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            {
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            {
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            {
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            },
            {
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            },
            {
              "game_id": "20260916_ATL@CHC",
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            },
            {
              "game_id": "20260916_BAL@NYM",
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            {
              "game_id": "20260916_BOS@TEX",
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            },
            {
              "game_id": "20260916_CHW@CLE",
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            {
              "game_id": "20260916_DET@TOR",
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            {
              "game_id": "20260916_KC@HOU",
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            },
            {
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              "all_changed_values_verified": true,
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              "previous_predicted_score": {
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260917_SD@COL",
              "date": "20260917",
              "outcome": "hit",
              "live_config_id": "candidate-57e651c7f36832cf",
              "parameter_values_recorded_in_prediction": {
                "score.variance_multiplier": 1.95
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              "paired_previous_config": true,
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              "previous_predicted_score": {
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          ]
        },
        "measurement_status": "paired_effect_observed",
        "rollback_monitor": {
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          "coverage": {
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            "distinct_slates": 4
          },
          "minimum_paired_games": 30,
          "minimum_distinct_slates": 3
        }
      }
    },
    {
      "experiment_id": "candidate-f4c3ec3952acc919",
      "proposed_at_utc": "2026-09-18T09:05:32.063728+00:00",
      "provider": "openai",
      "hypothesis": "Increasing score variance modestly from 1.95 to 2.05 may further reduce persistent margin compression and improve run-model error, supported by the regressing margin bias, the run-model loss incidence, and prior variance increases that worked. The shadow gate should detect any degradation in Brier score or calibration.",
      "changes": [
        {
          "parameter": "score.variance_multiplier",
          "old_value": 1.95,
          "new_value": 2.05,
          "expected_metric": "total_run_mae",
          "expected_direction": "decrease",
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            "metric.total_run_mae",
            "pipeline.largest_team_run_regression",
            "errors.miss_reasons"
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          "risk_tier": "low",
          "evaluation_mode": "artifact_replay"
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      "decision": "applied_to_production",
      "reason": "Operator-enabled direct apply; rollback monitoring remains authoritative.",
      "decided_at_utc": "2026-09-18T09:05:40.012512+00:00",
      "measured_effect": null,
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
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        "applied_at_utc": "2026-09-18T09:05:40.012512+00:00",
        "candidate_id": "candidate-f4c3ec3952acc919",
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          "total_run_mae": 3.5333,
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          "starter_outs_mae": 3.2984
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        "live_parameter_checks": [
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            {
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            {
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          ]
        },
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          "minimum_distinct_slates": 3
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      }
    },
    {
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      "proposed_at_utc": "2026-09-21T09:05:52.468019+00:00",
      "provider": "openai",
      "hypothesis": "Increasing score variance modestly from 2.05 to 2.15 may further reduce under-dispersed run margins and improve team-run error, supported by the leading run-model loss incidence, current team and total run MAE regressions, deep-slate evidence that wider tails helped two loss cases versus one hurt case, and the prior sequence of successful variance increases. The shadow gate should detect degradation in Brier score, calibration, or run guardrails.",
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          "new_value": 2.15,
          "expected_metric": "team_run_mae",
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            "errors.miss_reasons",
            "pipeline.largest_team_run_regression",
            "metric.brier_score"
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          "risk_tier": "low",
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      "decision": "applied_to_production",
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      "decided_at_utc": "2026-09-21T09:06:01.333346+00:00",
      "measured_effect": null,
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
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        ],
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            },
            {
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            },
            {
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            },
            {
              "game_id": "20260923_CIN@ATL",
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            },
            {
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            },
            {
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            {
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            },
            {
              "game_id": "20260923_MIL@PHI",
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            {
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            {
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            {
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            },
            {
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            },
            {
              "game_id": "20260923_TOR@BAL",
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            },
            {
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            },
            {
              "game_id": "20260923_WAS@DET",
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              "outcome": "hit",
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          ]
        },
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        "rollback_monitor": {
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          "coverage": {
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          },
          "minimum_paired_games": 30,
          "minimum_distinct_slates": 3
        }
      }
    },
    {
      "experiment_id": "candidate-c1d5650d9262b3dc",
      "proposed_at_utc": "2026-09-24T09:06:53.755755+00:00",
      "provider": "openai",
      "hypothesis": "Lowering the default confidence cap modestly should reduce persistent overconfidence in the 0.50-0.55 and 0.60-0.65 buckets and improve winner calibration and Brier score, while the shadow gate protects against worsening the underconfident 0.55-0.60 bucket.",
      "changes": [
        {
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          "new_value": 0.56,
          "expected_metric": "calibration_error",
          "expected_direction": "decrease",
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            "calibration.bucket.0.60-0.65",
            "metric.calibration_error",
            "metric.brier_score",
            "errors.miss_reasons"
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          "risk_tier": "low",
          "evaluation_mode": "enhanced_shadow"
        }
      ],
      "decision": "applied_to_production",
      "reason": "Operator-enabled direct apply; rollback monitoring remains authoritative.",
      "decided_at_utc": "2026-09-24T09:07:04.378558+00:00",
      "measured_effect": null,
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
      "application": {
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        "applied_at_utc": "2026-09-24T09:07:04.378558+00:00",
        "candidate_id": "candidate-c1d5650d9262b3dc",
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          {
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        ],
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        "metric_baseline_at_apply": {
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          "team_run_mae": 2.5168,
          "total_run_mae": 3.3697,
          "calibration_error": 0.101,
          "starter_er_mae": 1.756,
          "starter_outs_mae": 3.572
        },
        "status": "applied_to_production",
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        "new_config_id": "candidate-c1d5650d9262b3dc",
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        "live_parameter_checks": [
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            "currently_matches_applied_value": true
          }
        ],
        "metric_observation": {
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          "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
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            },
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            },
            {
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            },
            {
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              "latest_window": 3.6637,
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              "direction": "regressed",
              "sample_size": 113
            },
            {
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              "latest_window": 0.1324,
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            },
            {
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              "latest_window": 1.6226,
              "delta": -0.1334,
              "direction": "improved",
              "sample_size": 212
            },
            {
              "metric": "starter_outs_mae",
              "baseline_at_apply": 3.572,
              "latest_window": 4.184,
              "delta": 0.612,
              "direction": "regressed",
              "sample_size": 212
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          ]
        },
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          "paired_previous_config_predictions": 42,
          "changed_score_predictions": 0,
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            },
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            },
            {
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              },
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            },
            {
              "game_id": "20260925_BAL@NYY_2",
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              },
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            },
            {
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              "live_config_id": "candidate-c1d5650d9262b3dc",
              "parameter_values_recorded_in_prediction": {
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              "home_probability_delta": 0.0,
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              "previous_predicted_score": {
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              },
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            },
            {
              "game_id": "20260925_CHC@BOS_2",
              "date": "20260925",
              "outcome": "hit",
              "live_config_id": "candidate-c1d5650d9262b3dc",
              "parameter_values_recorded_in_prediction": {
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              "home_probability_delta": 0.0,
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            {
              "metric": "total_run_mae",
              "baseline_at_apply": 3.8559,
              "latest_window": 3.6637,
              "delta": -0.1922,
              "direction": "improved",
              "sample_size": 113
            },
            {
              "metric": "calibration_error",
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              "sample_size": 113
            },
            {
              "metric": "starter_er_mae",
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              "delta": -0.1669,
              "direction": "improved",
              "sample_size": 212
            },
            {
              "metric": "starter_outs_mae",
              "baseline_at_apply": 3.9123,
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              "delta": 0.2717,
              "direction": "regressed",
              "sample_size": 212
            }
          ]
        },
        "prediction_influence": {
          "status": "paired_effect_observed",
          "live_predictions_recorded": 33,
          "paired_previous_config_predictions": 31,
          "changed_score_predictions": 0,
          "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
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              "previous_predicted_score": {
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              },
              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260927_CLE@KC",
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              "outcome": "miss",
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              "previous_predicted_score": {
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              },
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            },
            {
              "game_id": "20260927_COL@CHW",
              "date": "20260927",
              "outcome": "hit",
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              },
              "previous_predicted_score": {
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            },
            {
              "game_id": "20260927_HOU@OAK",
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              "parameter_values_recorded_in_prediction": {
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            },
            {
              "game_id": "20260927_LAA@SEA",
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              },
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              },
              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260927_LAD@SF",
              "date": "20260927",
              "outcome": "hit",
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              },
              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260927_NYM@WAS",
              "date": "20260927",
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              },
              "previous_predicted_score": {
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260927_PIT@DET",
              "date": "20260927",
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              },
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            },
            {
              "game_id": "20260927_STL@MIL",
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            },
            {
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            },
            {
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              "home_probability_delta": 7.4e-05,
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              },
              "previous_predicted_score": {
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            },
            {
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            {
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            {
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            },
            {
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            },
            {
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            {
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            {
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            },
            {
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            },
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              },
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            {
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      }
    },
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        {
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            "calibration.bucket.0.60-0.65",
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      ],
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          {
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        ],
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        ],
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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  ],
  "safety": {
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    "llm_has_file_or_command_authority": false
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      "changes": [
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      "outcome": "DID NOT WORK: rejected (zero measured effect). Measured over 16 settled games: brier_score flat (0.0); accuracy flat (0.0); calibration_error flat (0.0); team_run_mae flat (0.0); total_run_mae flat (0.0).",
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          "games": [
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            },
            {
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              "sample_size": 212
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          "paired_previous_config_predictions": 46,
          "changed_score_predictions": 0,
          "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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          ]
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    },
    {
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              "previous_predicted_score": {
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260908_PIT@CHW",
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            },
            {
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              "previous_predicted_score": {
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260908_TB@ATL",
              "date": "20260908",
              "outcome": "hit",
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              "parameter_values_recorded_in_prediction": {
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260908_TEX@SEA",
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              "outcome": "hit",
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              "parameter_values_recorded_in_prediction": {
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            },
            {
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              "previous_predicted_score": {
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260908_WAS@SD",
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              "live_config_id": "candidate-11bc4bc2dfc7264a",
              "parameter_values_recorded_in_prediction": {
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              "previous_predicted_score": {
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          ]
        },
        "measurement_status": "paired_effect_observed",
        "rollback_monitor": {
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          "coverage": {
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            "distinct_slates": 4
          },
          "minimum_paired_games": 30,
          "minimum_distinct_slates": 3
        }
      }
    },
    {
      "experiment_id": "candidate-ed18fee8af33cf7e",
      "proposed_at_utc": "2026-09-12T09:04:27.150731+00:00",
      "provider": "openai",
      "hypothesis": "A further small increase in score variance should reduce persistent margin compression and improve run-model error, building on the previously promoted variance increase while keeping the change within the registered daily bound.",
      "changes": [
        {
          "parameter": "score.variance_multiplier",
          "old_value": 1.75,
          "new_value": 1.85,
          "expected_metric": "total_run_mae",
          "expected_direction": "decrease",
          "evidence_ids": [
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            "pipeline.largest_team_run_regression",
            "errors.miss_reasons"
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          "risk_tier": "low",
          "evaluation_mode": "artifact_replay"
        }
      ],
      "decision": "applied_to_production",
      "reason": "Operator-enabled direct apply; rollback monitoring remains authoritative.",
      "decided_at_utc": "2026-09-12T09:04:36.607786+00:00",
      "measured_effect": null,
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
      "application": {
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        "applied_at_utc": "2026-09-12T09:04:36.607786+00:00",
        "candidate_id": "candidate-ed18fee8af33cf7e",
        "changes": [
          {
            "parameter": "score.variance_multiplier",
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            "new_value": 1.85
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        ],
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        "config_after": "candidate-ed18fee8af33cf7e",
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          "total_run_mae": 3.7167,
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          "starter_er_mae": 1.8716,
          "starter_outs_mae": 3.1284
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        "status": "applied_to_production",
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        "new_config_id": "candidate-ed18fee8af33cf7e",
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        "live_parameter_checks": [
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            "currently_matches_applied_value": false
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        ],
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          "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
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            },
            {
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            {
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            },
            {
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              "latest_window": 0.1324,
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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            {
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          ]
        },
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      }
    },
    {
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      "provider": "openai",
      "hypothesis": "Increasing score variance modestly should reduce persistent run-margin compression and lower total-run error, building on two prior variance increases that worked and the current deep-slate evidence that wider tails helped the high-confidence BAL@NYM miss.",
      "changes": [
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            {
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          ]
        },
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          "minimum_paired_games": 30,
          "minimum_distinct_slates": 3
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      }
    },
    {
      "experiment_id": "candidate-f4c3ec3952acc919",
      "proposed_at_utc": "2026-09-18T09:05:32.063728+00:00",
      "provider": "openai",
      "hypothesis": "Increasing score variance modestly from 1.95 to 2.05 may further reduce persistent margin compression and improve run-model error, supported by the regressing margin bias, the run-model loss incidence, and prior variance increases that worked. The shadow gate should detect any degradation in Brier score or calibration.",
      "changes": [
        {
          "parameter": "score.variance_multiplier",
          "old_value": 1.95,
          "new_value": 2.05,
          "expected_metric": "total_run_mae",
          "expected_direction": "decrease",
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            "pipeline.largest_team_run_regression",
            "errors.miss_reasons"
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          "risk_tier": "low",
          "evaluation_mode": "artifact_replay"
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      ],
      "decision": "applied_to_production",
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      "decided_at_utc": "2026-09-18T09:05:40.012512+00:00",
      "measured_effect": null,
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
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        "applied_at_utc": "2026-09-18T09:05:40.012512+00:00",
        "candidate_id": "candidate-f4c3ec3952acc919",
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          {
            "parameter": "score.variance_multiplier",
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            "new_value": 2.05
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        ],
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          "team_run_mae": 2.5333,
          "total_run_mae": 3.5333,
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          "starter_er_mae": 1.7326,
          "starter_outs_mae": 3.2984
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        "status": "applied_to_production",
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        "new_config_id": "candidate-f4c3ec3952acc919",
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        "live_parameter_checks": [
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            "currently_matches_applied_value": false
          }
        ],
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          "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
          "metrics": [
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            },
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              "sample_size": 113
            },
            {
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            {
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            {
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            },
            {
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              "delta": -0.11,
              "direction": "improved",
              "sample_size": 212
            },
            {
              "metric": "starter_outs_mae",
              "baseline_at_apply": 3.2984,
              "latest_window": 4.184,
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              "sample_size": 212
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          "paired_previous_config_predictions": 46,
          "changed_score_predictions": 0,
          "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
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              "home_probability_delta": -8.5e-05,
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            },
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              },
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          ]
        },
        "measurement_status": "paired_effect_observed",
        "rollback_monitor": {
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          "coverage": {
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            "distinct_slates": 4
          },
          "minimum_paired_games": 30,
          "minimum_distinct_slates": 3
        }
      }
    },
    {
      "experiment_id": "candidate-982561b127534866",
      "proposed_at_utc": "2026-09-21T09:05:52.468019+00:00",
      "provider": "openai",
      "hypothesis": "Increasing score variance modestly from 2.05 to 2.15 may further reduce under-dispersed run margins and improve team-run error, supported by the leading run-model loss incidence, current team and total run MAE regressions, deep-slate evidence that wider tails helped two loss cases versus one hurt case, and the prior sequence of successful variance increases. The shadow gate should detect degradation in Brier score, calibration, or run guardrails.",
      "changes": [
        {
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          "old_value": 2.05,
          "new_value": 2.15,
          "expected_metric": "team_run_mae",
          "expected_direction": "decrease",
          "evidence_ids": [
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            "metric.total_run_mae",
            "errors.miss_reasons",
            "pipeline.largest_team_run_regression",
            "metric.brier_score"
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          "risk_tier": "low",
          "evaluation_mode": "artifact_replay"
        }
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      "decision": "applied_to_production",
      "reason": "Operator-enabled direct apply; rollback monitoring remains authoritative.",
      "decided_at_utc": "2026-09-21T09:06:01.333346+00:00",
      "measured_effect": null,
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
      "application": {
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        "applied_at_utc": "2026-09-21T09:06:01.333346+00:00",
        "candidate_id": "candidate-982561b127534866",
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          "starter_outs_mae": 3.4839
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            },
            {
              "game_id": "20260923_NYM@TEX",
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            },
            {
              "game_id": "20260923_SD@LAD",
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              "outcome": "miss",
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            },
            {
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            },
            {
              "game_id": "20260923_TB@NYY",
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              "outcome": "miss",
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            },
            {
              "game_id": "20260923_TOR@BAL",
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            },
            {
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              "score_changed_vs_previous_config": false
            },
            {
              "game_id": "20260923_WAS@DET",
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              "parameter_values_recorded_in_prediction": {
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          ]
        },
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        "rollback_monitor": {
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          "coverage": {
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          },
          "minimum_paired_games": 30,
          "minimum_distinct_slates": 3
        }
      }
    },
    {
      "experiment_id": "candidate-c1d5650d9262b3dc",
      "proposed_at_utc": "2026-09-24T09:06:53.755755+00:00",
      "provider": "openai",
      "hypothesis": "Lowering the default confidence cap modestly should reduce persistent overconfidence in the 0.50-0.55 and 0.60-0.65 buckets and improve winner calibration and Brier score, while the shadow gate protects against worsening the underconfident 0.55-0.60 bucket.",
      "changes": [
        {
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          "new_value": 0.56,
          "expected_metric": "calibration_error",
          "expected_direction": "decrease",
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            "calibration.bucket.0.60-0.65",
            "metric.calibration_error",
            "metric.brier_score",
            "errors.miss_reasons"
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          "risk_tier": "low",
          "evaluation_mode": "enhanced_shadow"
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      ],
      "decision": "applied_to_production",
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      "decided_at_utc": "2026-09-24T09:07:04.378558+00:00",
      "measured_effect": null,
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
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          "total_run_mae": 3.3697,
          "calibration_error": 0.101,
          "starter_er_mae": 1.756,
          "starter_outs_mae": 3.572
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        "status": "applied_to_production",
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        "live_parameter_checks": [
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            "currently_matches_applied_value": true
          }
        ],
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          "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
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            },
            {
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            {
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              "direction": "regressed",
              "sample_size": 113
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              "latest_window": 0.1324,
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            {
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              "latest_window": 1.6226,
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            {
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            {
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            {
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            {
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            {
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            {
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          ]
        },
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          "minimum_paired_games": 30,
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      }
    },
    {
      "experiment_id": "candidate-8133e99fc8d4e4c7",
      "proposed_at_utc": "2026-09-27T09:06:54.240008+00:00",
      "provider": "openai",
      "hypothesis": "Increasing score variance modestly from 2.15 to 2.25 should reduce persistent run-margin compression and improve total-run error. This extends a sequence of previously successful variance increases and targets the currently regressing run-model metrics, while the shadow gate protects against degradation in Brier score, calibration, and run-error guardrails.",
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        {
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          "new_value": 2.25,
          "expected_metric": "total_run_mae",
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            "pipeline.largest_team_run_regression",
            "errors.miss_reasons"
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          "evaluation_mode": "artifact_replay"
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      ],
      "decision": "applied_to_production",
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      "decided_at_utc": "2026-09-27T09:07:01.435173+00:00",
      "measured_effect": null,
      "outcome": "APPLIED: the registered values were written to the live champion. Measurement status: paired_effect_observed.",
      "worked": null,
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          "starter_outs_mae": 3.9123
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          }
        ],
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            {
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            {
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            {
              "metric": "starter_outs_mae",
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              "delta": 0.2717,
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            {
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            {
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            {
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    },
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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            },
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          },
          {
            "game_id": "20260906_WAS@LAD",
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            },
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            "score_changed_vs_previous_config": false
          }
        ]
      },
      "measurement_status": "paired_effect_observed",
      "rollback_monitor": {
        "status": "monitoring",
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          "paired_games": 7,
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        },
        "minimum_paired_games": 30,
        "minimum_distinct_slates": 3
      }
    },
    {
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        {
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      ],
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        "team_run_mae": 2.6083,
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      "live_parameter_checks": [
        {
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          "current_live_value": 2.25,
          "currently_matches_applied_value": false
        }
      ],
      "metric_observation": {
        "status": "observed_later_rolling_window",
        "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
        "metrics": [
          {
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          {
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            "sample_size": 113
          },
          {
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            "sample_size": 113
          },
          {
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          },
          {
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          },
          {
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          },
          {
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        ]
      },
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        "paired_previous_config_predictions": 26,
        "changed_score_predictions": 0,
        "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
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          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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          },
          {
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            "home_probability_delta": -6.3e-05,
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          },
          {
            "game_id": "20260907_STL@SF",
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          },
          {
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            },
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            },
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          },
          {
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            },
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          },
          {
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          },
          {
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          },
          {
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            "date": "20260908",
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          },
          {
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            },
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          },
          {
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            },
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          },
          {
            "game_id": "20260908_HOU@PHI",
            "date": "20260908",
            "outcome": "hit",
            "live_config_id": "candidate-11bc4bc2dfc7264a",
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            },
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            },
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          },
          {
            "game_id": "20260908_LAA@BOS",
            "date": "20260908",
            "outcome": "miss",
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            "home_probability_delta": -0.002176,
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260908_MIN@DET",
            "date": "20260908",
            "outcome": "miss",
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            },
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          },
          {
            "game_id": "20260908_NYM@MIA",
            "date": "20260908",
            "outcome": "hit",
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            },
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          },
          {
            "game_id": "20260908_PIT@CHW",
            "date": "20260908",
            "outcome": "miss",
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260908_STL@SF",
            "date": "20260908",
            "outcome": "miss",
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260908_TB@ATL",
            "date": "20260908",
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260908_TEX@SEA",
            "date": "20260908",
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            },
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260908_TOR@OAK",
            "date": "20260908",
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            },
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260908_WAS@SD",
            "date": "20260908",
            "outcome": "hit",
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            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            "home_probability_delta": -0.000109,
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            },
            "previous_predicted_score": {
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              "home_runs": 5.0
            },
            "score_changed_vs_previous_config": false
          }
        ]
      },
      "measurement_status": "paired_effect_observed",
      "rollback_monitor": {
        "status": "monitoring",
        "coverage": {
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        },
        "minimum_paired_games": 30,
        "minimum_distinct_slates": 3
      }
    },
    {
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        {
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          "new_value": 1.85
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      ],
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      "config_after": "candidate-ed18fee8af33cf7e",
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        "team_run_mae": 2.4417,
        "total_run_mae": 3.7167,
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        "starter_outs_mae": 3.1284
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      "status": "applied_to_production",
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      "live_parameter_checks": [
        {
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          "current_live_value": 2.25,
          "currently_matches_applied_value": false
        }
      ],
      "metric_observation": {
        "status": "observed_later_rolling_window",
        "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
        "metrics": [
          {
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          },
          {
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            "sample_size": 113
          },
          {
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            "delta": -0.0258,
            "direction": "improved",
            "sample_size": 113
          },
          {
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            "latest_window": 3.6637,
            "delta": -0.053,
            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "calibration_error",
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            "latest_window": 0.1324,
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            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "starter_er_mae",
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            "latest_window": 1.6226,
            "delta": -0.249,
            "direction": "improved",
            "sample_size": 212
          },
          {
            "metric": "starter_outs_mae",
            "baseline_at_apply": 3.1284,
            "latest_window": 4.184,
            "delta": 1.0556,
            "direction": "regressed",
            "sample_size": 212
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        ]
      },
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        "paired_previous_config_predictions": 40,
        "changed_score_predictions": 0,
        "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
        "games": [
          {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260912_PIT@CHC",
            "date": "20260912",
            "outcome": "hit",
            "live_config_id": "candidate-ed18fee8af33cf7e",
            "parameter_values_recorded_in_prediction": {
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            "previous_predicted_score": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260912_SD@SF",
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            "outcome": "hit",
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            "live_predicted_score": {
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260912_SEA@OAK",
            "date": "20260912",
            "outcome": "hit",
            "live_config_id": "candidate-ed18fee8af33cf7e",
            "parameter_values_recorded_in_prediction": {
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            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260912_TEX@ARI",
            "date": "20260912",
            "outcome": "miss",
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            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.581582,
            "previous_home_win_probability": 0.583173,
            "home_probability_delta": -0.001591,
            "live_predicted_score": {
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              "home_runs": 5.0
            },
            "previous_predicted_score": {
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              "home_runs": 5.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260913_BAL@TOR",
            "date": "20260913",
            "outcome": "hit",
            "live_config_id": "candidate-ed18fee8af33cf7e",
            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.534848,
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            "home_probability_delta": -0.0001,
            "live_predicted_score": {
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            },
            "previous_predicted_score": {
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              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260913_CHW@STL",
            "date": "20260913",
            "outcome": "hit",
            "live_config_id": "candidate-ed18fee8af33cf7e",
            "parameter_values_recorded_in_prediction": {
              "score.variance_multiplier": 1.85
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.511703,
            "previous_home_win_probability": 0.511749,
            "home_probability_delta": -4.6e-05,
            "live_predicted_score": {
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              "home_runs": 6.0
            },
            "previous_predicted_score": {
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              "home_runs": 6.0
            },
            "score_changed_vs_previous_config": false
          },
          {
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          },
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          {
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          },
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          },
          {
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            "date": "20260913",
            "outcome": "miss",
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          },
          {
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          },
          {
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          },
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          },
          {
            "game_id": "20260914_ATL@CHC",
            "date": "20260914",
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          },
          {
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          },
          {
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            "outcome": "miss",
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          },
          {
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          },
          {
            "game_id": "20260914_LAD@CIN",
            "date": "20260914",
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          },
          {
            "game_id": "20260914_MIA@ARI",
            "date": "20260914",
            "outcome": "miss",
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260914_NYY@MIN",
            "date": "20260914",
            "outcome": "hit",
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260914_SD@COL",
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            "outcome": "hit",
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            },
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260914_SEA@LAA",
            "date": "20260914",
            "outcome": "hit",
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            },
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            "home_probability_delta": -5.5e-05,
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              "home_runs": 6.0
            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260914_SF@STL",
            "date": "20260914",
            "outcome": "hit",
            "live_config_id": "candidate-ed18fee8af33cf7e",
            "parameter_values_recorded_in_prediction": {
              "score.variance_multiplier": 1.85
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.507604,
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            "home_probability_delta": -6.7e-05,
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              "away_runs": 4.0,
              "home_runs": 5.0
            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          }
        ]
      },
      "measurement_status": "paired_effect_observed",
      "rollback_monitor": {
        "status": "monitoring",
        "coverage": {
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        },
        "minimum_paired_games": 30,
        "minimum_distinct_slates": 3
      }
    },
    {
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      "applied_at_utc": "2026-09-15T09:05:08.617033+00:00",
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        {
          "parameter": "score.variance_multiplier",
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          "new_value": 1.95
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      ],
      "config_before": "candidate-ed18fee8af33cf7e",
      "config_after": "candidate-57e651c7f36832cf",
      "metric_baseline_at_apply": {
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        "team_run_mae": 2.5083,
        "total_run_mae": 3.8167,
        "calibration_error": 0.1244,
        "starter_er_mae": 1.7778,
        "starter_outs_mae": 3.2644
      },
      "status": "applied_to_production",
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      "new_config_id": "candidate-57e651c7f36832cf",
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      "live_parameter_checks": [
        {
          "parameter": "score.variance_multiplier",
          "old_value": 1.85,
          "applied_value": 1.95,
          "current_live_value": 2.25,
          "currently_matches_applied_value": false
        }
      ],
      "metric_observation": {
        "status": "observed_later_rolling_window",
        "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
        "metrics": [
          {
            "metric": "brier_score",
            "baseline_at_apply": 0.24114,
            "latest_window": 0.244879,
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            "sample_size": 113
          },
          {
            "metric": "accuracy",
            "baseline_at_apply": 0.6333,
            "latest_window": 0.6195,
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            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "team_run_mae",
            "baseline_at_apply": 2.5083,
            "latest_window": 2.4159,
            "delta": -0.0924,
            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "total_run_mae",
            "baseline_at_apply": 3.8167,
            "latest_window": 3.6637,
            "delta": -0.153,
            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "calibration_error",
            "baseline_at_apply": 0.1244,
            "latest_window": 0.1324,
            "delta": 0.008,
            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "starter_er_mae",
            "baseline_at_apply": 1.7778,
            "latest_window": 1.6226,
            "delta": -0.1552,
            "direction": "improved",
            "sample_size": 212
          },
          {
            "metric": "starter_outs_mae",
            "baseline_at_apply": 3.2644,
            "latest_window": 4.184,
            "delta": 0.9196,
            "direction": "regressed",
            "sample_size": 212
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        ]
      },
      "prediction_influence": {
        "status": "paired_effect_observed",
        "live_predictions_recorded": 39,
        "paired_previous_config_predictions": 39,
        "changed_score_predictions": 0,
        "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
        "games": [
          {
            "game_id": "20260915_NYY@MIN",
            "date": "20260915",
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            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
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            },
            "previous_predicted_score": {
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              "home_runs": 3.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260915_OAK@TB",
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            },
            "score_changed_vs_previous_config": false
          }
        ]
      },
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      "rollback_monitor": {
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        },
        "minimum_paired_games": 30,
        "minimum_distinct_slates": 3
      }
    },
    {
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      "applied_at_utc": "2026-09-18T09:05:40.012512+00:00",
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        {
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        {
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      ],
      "metric_observation": {
        "status": "observed_later_rolling_window",
        "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
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          {
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          {
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          {
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          {
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        ]
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        "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
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          {
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          },
          {
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          },
          {
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          },
          {
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          },
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          {
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          {
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          {
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          },
          {
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          {
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          {
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          {
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          {
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          },
          {
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          },
          {
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          {
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              "home_runs": 7.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260920_PHI@NYM",
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            "outcome": "miss",
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            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
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              "home_runs": 5.0
            },
            "previous_predicted_score": {
              "away_runs": 4.0,
              "home_runs": 5.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260920_SEA@COL",
            "date": "20260920",
            "outcome": "hit",
            "live_config_id": "candidate-f4c3ec3952acc919",
            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.39974,
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            "live_predicted_score": {
              "away_runs": 8.0,
              "home_runs": 6.0
            },
            "previous_predicted_score": {
              "away_runs": 8.0,
              "home_runs": 6.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260920_SF@LAD",
            "date": "20260920",
            "outcome": "hit",
            "live_config_id": "candidate-f4c3ec3952acc919",
            "parameter_values_recorded_in_prediction": {
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            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.613015,
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            "home_probability_delta": -0.001718,
            "live_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 7.0
            },
            "previous_predicted_score": {
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              "home_runs": 7.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260920_TOR@TEX",
            "date": "20260920",
            "outcome": "miss",
            "live_config_id": "candidate-f4c3ec3952acc919",
            "parameter_values_recorded_in_prediction": {
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            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.55937,
            "previous_home_win_probability": 0.560861,
            "home_probability_delta": -0.001491,
            "live_predicted_score": {
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              "home_runs": 4.0
            },
            "previous_predicted_score": {
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              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260920_WAS@STL",
            "date": "20260920",
            "outcome": "miss",
            "live_config_id": "candidate-f4c3ec3952acc919",
            "parameter_values_recorded_in_prediction": {
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            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.478382,
            "previous_home_win_probability": 0.477836,
            "home_probability_delta": 0.000546,
            "live_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 4.0
            },
            "previous_predicted_score": {
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              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260921_WAS@DET",
            "date": "20260921",
            "outcome": "hit",
            "live_config_id": "candidate-f4c3ec3952acc919",
            "parameter_values_recorded_in_prediction": {
              "score.variance_multiplier": 2.05
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.576597,
            "previous_home_win_probability": 0.578511,
            "home_probability_delta": -0.001914,
            "live_predicted_score": {
              "away_runs": 4.0,
              "home_runs": 5.0
            },
            "previous_predicted_score": {
              "away_runs": 4.0,
              "home_runs": 5.0
            },
            "score_changed_vs_previous_config": false
          }
        ]
      },
      "measurement_status": "paired_effect_observed",
      "rollback_monitor": {
        "status": "monitoring",
        "coverage": {
          "paired_games": 7,
          "distinct_slates": 4
        },
        "minimum_paired_games": 30,
        "minimum_distinct_slates": 3
      }
    },
    {
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      "applied_at_utc": "2026-09-21T09:06:01.333346+00:00",
      "candidate_id": "candidate-982561b127534866",
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        {
          "parameter": "score.variance_multiplier",
          "old_value": 2.05,
          "new_value": 2.15
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      ],
      "config_before": "candidate-f4c3ec3952acc919",
      "config_after": "candidate-982561b127534866",
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        "team_run_mae": 2.5042,
        "total_run_mae": 3.3583,
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        "starter_er_mae": 1.6855,
        "starter_outs_mae": 3.4839
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      "status": "applied_to_production",
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      "live_parameter_checks": [
        {
          "parameter": "score.variance_multiplier",
          "old_value": 2.05,
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          "current_live_value": 2.25,
          "currently_matches_applied_value": false
        }
      ],
      "metric_observation": {
        "status": "observed_later_rolling_window",
        "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
        "metrics": [
          {
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          },
          {
            "metric": "accuracy",
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            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "team_run_mae",
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            "latest_window": 2.4159,
            "delta": -0.0883,
            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "total_run_mae",
            "baseline_at_apply": 3.3583,
            "latest_window": 3.6637,
            "delta": 0.3054,
            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "calibration_error",
            "baseline_at_apply": 0.0885,
            "latest_window": 0.1324,
            "delta": 0.0439,
            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "starter_er_mae",
            "baseline_at_apply": 1.6855,
            "latest_window": 1.6226,
            "delta": -0.0629,
            "direction": "improved",
            "sample_size": 212
          },
          {
            "metric": "starter_outs_mae",
            "baseline_at_apply": 3.4839,
            "latest_window": 4.184,
            "delta": 0.7001,
            "direction": "regressed",
            "sample_size": 212
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        ]
      },
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        "status": "paired_effect_observed",
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        "paired_previous_config_predictions": 33,
        "changed_score_predictions": 0,
        "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
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          {
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            "score_changed_vs_previous_config": false
          },
          {
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          },
          {
            "game_id": "20260922_CLE@BOS",
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260922_HOU@SEA",
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260922_LAA@OAK",
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          },
          {
            "game_id": "20260922_MIA@CHC",
            "date": "20260922",
            "outcome": "miss",
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          },
          {
            "game_id": "20260922_MIL@PHI",
            "date": "20260922",
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          },
          {
            "game_id": "20260922_MIN@SF",
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          },
          {
            "game_id": "20260922_NYM@TEX",
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          },
          {
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            "home_probability_delta": -7.9e-05,
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            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260922_STL@PIT",
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            "previous_predicted_score": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260922_TB@NYY",
            "date": "20260922",
            "outcome": "hit",
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            "home_probability_delta": -7.8e-05,
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            },
            "previous_predicted_score": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260922_TB@NYY_2",
            "date": "20260922",
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            "home_probability_delta": -3.4e-05,
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            "previous_predicted_score": {
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          },
          {
            "game_id": "20260922_WAS@DET",
            "date": "20260922",
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            "all_changed_values_verified": true,
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            "previous_predicted_score": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_ARI@COL",
            "date": "20260923",
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            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
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            "previous_predicted_score": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_CHW@KC",
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            "outcome": "miss",
            "live_config_id": "candidate-982561b127534866",
            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            "previous_predicted_score": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_CIN@ATL",
            "date": "20260923",
            "outcome": "hit",
            "live_config_id": "candidate-982561b127534866",
            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            "home_probability_delta": -0.001431,
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            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_CLE@BOS",
            "date": "20260923",
            "outcome": "miss",
            "live_config_id": "candidate-982561b127534866",
            "parameter_values_recorded_in_prediction": {
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            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            "previous_predicted_score": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_HOU@SEA",
            "date": "20260923",
            "outcome": "hit",
            "live_config_id": "candidate-982561b127534866",
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            "home_probability_delta": -5.3e-05,
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_LAA@OAK",
            "date": "20260923",
            "outcome": "miss",
            "live_config_id": "candidate-982561b127534866",
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            },
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              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_MIA@CHC",
            "date": "20260923",
            "outcome": "miss",
            "live_config_id": "candidate-982561b127534866",
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            },
            "all_changed_values_verified": true,
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            "live_home_win_probability": 0.568568,
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            "home_probability_delta": -0.000701,
            "live_predicted_score": {
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            },
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              "home_runs": 6.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_MIL@PHI",
            "date": "20260923",
            "outcome": "hit",
            "live_config_id": "candidate-982561b127534866",
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            },
            "all_changed_values_verified": true,
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            "live_home_win_probability": 0.358239,
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            },
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            },
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          },
          {
            "game_id": "20260923_MIN@SF",
            "date": "20260923",
            "outcome": "hit",
            "live_config_id": "candidate-982561b127534866",
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            },
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            "home_probability_delta": 4.4e-05,
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_NYM@TEX",
            "date": "20260923",
            "outcome": "hit",
            "live_config_id": "candidate-982561b127534866",
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            "all_changed_values_verified": true,
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_SD@LAD",
            "date": "20260923",
            "outcome": "miss",
            "live_config_id": "candidate-982561b127534866",
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            },
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_STL@PIT",
            "date": "20260923",
            "outcome": "miss",
            "live_config_id": "candidate-982561b127534866",
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            "all_changed_values_verified": true,
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            "live_home_win_probability": 0.577526,
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            "home_probability_delta": -0.001114,
            "live_predicted_score": {
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_TB@NYY",
            "date": "20260923",
            "outcome": "miss",
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.492721,
            "previous_home_win_probability": 0.492545,
            "home_probability_delta": 0.000176,
            "live_predicted_score": {
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_TOR@BAL",
            "date": "20260923",
            "outcome": "hit",
            "live_config_id": "candidate-982561b127534866",
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            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            "home_probability_delta": -0.000176,
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_TOR@BAL_2",
            "date": "20260923",
            "outcome": "miss",
            "live_config_id": "candidate-982561b127534866",
            "parameter_values_recorded_in_prediction": {
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            },
            "all_changed_values_verified": true,
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            "live_home_win_probability": 0.479819,
            "previous_home_win_probability": 0.479757,
            "home_probability_delta": 6.2e-05,
            "live_predicted_score": {
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260923_WAS@DET",
            "date": "20260923",
            "outcome": "hit",
            "live_config_id": "candidate-982561b127534866",
            "parameter_values_recorded_in_prediction": {
              "score.variance_multiplier": 2.15
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.492721,
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            "home_probability_delta": 0.000176,
            "live_predicted_score": {
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              "home_runs": 4.0
            },
            "previous_predicted_score": {
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              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          }
        ]
      },
      "measurement_status": "paired_effect_observed",
      "rollback_monitor": {
        "status": "monitoring",
        "coverage": {
          "paired_games": 7,
          "distinct_slates": 4
        },
        "minimum_paired_games": 30,
        "minimum_distinct_slates": 3
      }
    },
    {
      "date": "20260923",
      "applied_at_utc": "2026-09-24T09:07:04.378558+00:00",
      "candidate_id": "candidate-c1d5650d9262b3dc",
      "changes": [
        {
          "parameter": "confidence.base_cap",
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          "new_value": 0.56
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      ],
      "config_before": "candidate-982561b127534866",
      "config_after": "candidate-c1d5650d9262b3dc",
      "metric_baseline_at_apply": {
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        "accuracy": 0.5546,
        "team_run_mae": 2.5168,
        "total_run_mae": 3.3697,
        "calibration_error": 0.101,
        "starter_er_mae": 1.756,
        "starter_outs_mae": 3.572
      },
      "status": "applied_to_production",
      "previous_config_id": "candidate-982561b127534866",
      "new_config_id": "candidate-c1d5650d9262b3dc",
      "currently_active": false,
      "live_parameter_checks": [
        {
          "parameter": "confidence.base_cap",
          "old_value": 0.58,
          "applied_value": 0.56,
          "current_live_value": 0.56,
          "currently_matches_applied_value": true
        }
      ],
      "metric_observation": {
        "status": "observed_later_rolling_window",
        "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
        "metrics": [
          {
            "metric": "brier_score",
            "baseline_at_apply": 0.244247,
            "latest_window": 0.244879,
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            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "accuracy",
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            "latest_window": 0.6195,
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            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "team_run_mae",
            "baseline_at_apply": 2.5168,
            "latest_window": 2.4159,
            "delta": -0.1009,
            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "total_run_mae",
            "baseline_at_apply": 3.3697,
            "latest_window": 3.6637,
            "delta": 0.294,
            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "calibration_error",
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            "latest_window": 0.1324,
            "delta": 0.0314,
            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "starter_er_mae",
            "baseline_at_apply": 1.756,
            "latest_window": 1.6226,
            "delta": -0.1334,
            "direction": "improved",
            "sample_size": 212
          },
          {
            "metric": "starter_outs_mae",
            "baseline_at_apply": 3.572,
            "latest_window": 4.184,
            "delta": 0.612,
            "direction": "regressed",
            "sample_size": 212
          }
        ]
      },
      "prediction_influence": {
        "status": "paired_effect_observed",
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        "paired_previous_config_predictions": 42,
        "changed_score_predictions": 0,
        "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
        "games": [
          {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_ATL@MIA",
            "date": "20260925",
            "outcome": "miss",
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            "parameter_values_recorded_in_prediction": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_BAL@NYY",
            "date": "20260925",
            "outcome": "miss",
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            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_BAL@NYY_2",
            "date": "20260925",
            "outcome": "hit",
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            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_CHC@BOS",
            "date": "20260925",
            "outcome": "miss",
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            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            },
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          },
          {
            "game_id": "20260925_CHC@BOS_2",
            "date": "20260925",
            "outcome": "hit",
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_CIN@TOR",
            "date": "20260925",
            "outcome": "hit",
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            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_CLE@KC",
            "date": "20260925",
            "outcome": "hit",
            "live_config_id": "candidate-c1d5650d9262b3dc",
            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
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            "previous_predicted_score": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_COL@CHW",
            "date": "20260925",
            "outcome": "hit",
            "live_config_id": "candidate-c1d5650d9262b3dc",
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            "previous_predicted_score": {
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_HOU@OAK",
            "date": "20260925",
            "outcome": "miss",
            "live_config_id": "candidate-c1d5650d9262b3dc",
            "parameter_values_recorded_in_prediction": {
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            "paired_previous_config": true,
            "live_home_win_probability": 0.445431,
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            },
            "previous_predicted_score": {
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              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_LAA@SEA",
            "date": "20260925",
            "outcome": "hit",
            "live_config_id": "candidate-c1d5650d9262b3dc",
            "parameter_values_recorded_in_prediction": {
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            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.475064,
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            "live_predicted_score": {
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              "home_runs": 3.0
            },
            "previous_predicted_score": {
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              "home_runs": 3.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_LAD@SF",
            "date": "20260925",
            "outcome": "hit",
            "live_config_id": "candidate-c1d5650d9262b3dc",
            "parameter_values_recorded_in_prediction": {
              "confidence.base_cap": 0.56
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.422253,
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            "home_probability_delta": 0.0,
            "live_predicted_score": {
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              "home_runs": 3.0
            },
            "previous_predicted_score": {
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              "home_runs": 3.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_NYM@WAS",
            "date": "20260925",
            "outcome": "hit",
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            },
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            },
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          },
          {
            "game_id": "20260925_PIT@DET",
            "date": "20260925",
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            },
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          },
          {
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            "date": "20260925",
            "outcome": "hit",
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            },
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            },
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          },
          {
            "game_id": "20260925_TB@PHI",
            "date": "20260925",
            "outcome": "miss",
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            "live_home_win_probability": 0.540856,
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260925_TEX@MIN",
            "date": "20260925",
            "outcome": "hit",
            "live_config_id": "candidate-c1d5650d9262b3dc",
            "parameter_values_recorded_in_prediction": {
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            },
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            "live_home_win_probability": 0.54972,
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_ARI@SD",
            "date": "20260926",
            "outcome": "miss",
            "live_config_id": "candidate-c1d5650d9262b3dc",
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            },
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            },
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          },
          {
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            "date": "20260926",
            "outcome": "hit",
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            },
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            },
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          },
          {
            "game_id": "20260926_CIN@TOR",
            "date": "20260926",
            "outcome": "miss",
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_CLE@KC",
            "date": "20260926",
            "outcome": "hit",
            "live_config_id": "candidate-c1d5650d9262b3dc",
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            "live_home_win_probability": 0.412303,
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            "live_predicted_score": {
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_COL@CHW",
            "date": "20260926",
            "outcome": "miss",
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            },
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            "live_predicted_score": {
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_HOU@OAK",
            "date": "20260926",
            "outcome": "hit",
            "live_config_id": "candidate-c1d5650d9262b3dc",
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            },
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_LAA@SEA",
            "date": "20260926",
            "outcome": "hit",
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            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_LAD@SF",
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            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_NYM@WAS",
            "date": "20260926",
            "outcome": "miss",
            "live_config_id": "candidate-c1d5650d9262b3dc",
            "parameter_values_recorded_in_prediction": {
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            "live_home_win_probability": 0.508915,
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_PIT@DET",
            "date": "20260926",
            "outcome": "miss",
            "live_config_id": "candidate-c1d5650d9262b3dc",
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            "all_changed_values_verified": true,
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            "live_home_win_probability": 0.484309,
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_STL@MIL",
            "date": "20260926",
            "outcome": "hit",
            "live_config_id": "candidate-c1d5650d9262b3dc",
            "parameter_values_recorded_in_prediction": {
              "confidence.base_cap": 0.56
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.549057,
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_TB@PHI",
            "date": "20260926",
            "outcome": "hit",
            "live_config_id": "candidate-c1d5650d9262b3dc",
            "parameter_values_recorded_in_prediction": {
              "confidence.base_cap": 0.56
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.392435,
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            "live_predicted_score": {
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              "home_runs": 2.0
            },
            "previous_predicted_score": {
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              "home_runs": 2.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260926_TEX@MIN",
            "date": "20260926",
            "outcome": "miss",
            "live_config_id": "candidate-c1d5650d9262b3dc",
            "parameter_values_recorded_in_prediction": {
              "confidence.base_cap": 0.56
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.511743,
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              "home_runs": 5.0
            },
            "previous_predicted_score": {
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              "home_runs": 5.0
            },
            "score_changed_vs_previous_config": false
          }
        ]
      },
      "measurement_status": "paired_effect_observed",
      "rollback_monitor": {
        "status": "monitoring",
        "coverage": {
          "paired_games": 7,
          "distinct_slates": 4
        },
        "minimum_paired_games": 30,
        "minimum_distinct_slates": 3
      }
    },
    {
      "date": "20260926",
      "applied_at_utc": "2026-09-27T09:07:01.435173+00:00",
      "candidate_id": "candidate-8133e99fc8d4e4c7",
      "changes": [
        {
          "parameter": "score.variance_multiplier",
          "old_value": 2.15,
          "new_value": 2.25
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      ],
      "config_before": "candidate-c1d5650d9262b3dc",
      "config_after": "candidate-8133e99fc8d4e4c7",
      "metric_baseline_at_apply": {
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        "accuracy": 0.5932,
        "team_run_mae": 2.6314,
        "total_run_mae": 3.8559,
        "calibration_error": 0.0876,
        "starter_er_mae": 1.7895,
        "starter_outs_mae": 3.9123
      },
      "status": "applied_to_production",
      "previous_config_id": "candidate-c1d5650d9262b3dc",
      "new_config_id": "candidate-8133e99fc8d4e4c7",
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      "live_parameter_checks": [
        {
          "parameter": "score.variance_multiplier",
          "old_value": 2.15,
          "applied_value": 2.25,
          "current_live_value": 2.25,
          "currently_matches_applied_value": true
        }
      ],
      "metric_observation": {
        "status": "observed_later_rolling_window",
        "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
        "metrics": [
          {
            "metric": "brier_score",
            "baseline_at_apply": 0.241266,
            "latest_window": 0.244879,
            "delta": 0.003613,
            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "accuracy",
            "baseline_at_apply": 0.5932,
            "latest_window": 0.6195,
            "delta": 0.0263,
            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "team_run_mae",
            "baseline_at_apply": 2.6314,
            "latest_window": 2.4159,
            "delta": -0.2155,
            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "total_run_mae",
            "baseline_at_apply": 3.8559,
            "latest_window": 3.6637,
            "delta": -0.1922,
            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "calibration_error",
            "baseline_at_apply": 0.0876,
            "latest_window": 0.1324,
            "delta": 0.0448,
            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "starter_er_mae",
            "baseline_at_apply": 1.7895,
            "latest_window": 1.6226,
            "delta": -0.1669,
            "direction": "improved",
            "sample_size": 212
          },
          {
            "metric": "starter_outs_mae",
            "baseline_at_apply": 3.9123,
            "latest_window": 4.184,
            "delta": 0.2717,
            "direction": "regressed",
            "sample_size": 212
          }
        ]
      },
      "prediction_influence": {
        "status": "paired_effect_observed",
        "live_predictions_recorded": 33,
        "paired_previous_config_predictions": 31,
        "changed_score_predictions": 0,
        "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
        "games": [
          {
            "game_id": "20260927_CIN@TOR",
            "date": "20260927",
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            "home_probability_delta": -7e-05,
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260927_CLE@KC",
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            "outcome": "miss",
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            "parameter_values_recorded_in_prediction": {
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            "paired_previous_config": true,
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            },
            "previous_predicted_score": {
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            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260927_COL@CHW",
            "date": "20260927",
            "outcome": "hit",
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              "score.variance_multiplier": 2.25
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            "all_changed_values_verified": true,
            "paired_previous_config": true,
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            "home_probability_delta": -0.003709,
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              "home_runs": 8.0
            },
            "previous_predicted_score": {
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              "home_runs": 8.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260927_HOU@OAK",
            "date": "20260927",
            "outcome": "hit",
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            "parameter_values_recorded_in_prediction": {
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            "all_changed_values_verified": true,
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            "live_home_win_probability": 0.354995,
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            "live_predicted_score": {
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              "home_runs": 6.0
            },
            "previous_predicted_score": {
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              "home_runs": 6.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260927_LAA@SEA",
            "date": "20260927",
            "outcome": "hit",
            "live_config_id": "candidate-8133e99fc8d4e4c7",
            "parameter_values_recorded_in_prediction": {
              "score.variance_multiplier": 2.25
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.584837,
            "previous_home_win_probability": 0.585876,
            "home_probability_delta": -0.001039,
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              "away_runs": 4.0,
              "home_runs": 5.0
            },
            "previous_predicted_score": {
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              "home_runs": 5.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260927_LAD@SF",
            "date": "20260927",
            "outcome": "hit",
            "live_config_id": "candidate-8133e99fc8d4e4c7",
            "parameter_values_recorded_in_prediction": {
              "score.variance_multiplier": 2.25
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.393554,
            "previous_home_win_probability": 0.392458,
            "home_probability_delta": 0.001096,
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            },
            "previous_predicted_score": {
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              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20260927_NYM@WAS",
            "date": "20260927",
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          },
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          },
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          },
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          },
          {
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          },
          {
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            "date": "20261006",
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            },
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          },
          {
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            },
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            "live_predicted_score": {
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            },
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          }
        ]
      },
      "measurement_status": "paired_effect_observed",
      "rollback_monitor": {
        "status": "monitoring",
        "coverage": {
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        },
        "minimum_paired_games": 30,
        "minimum_distinct_slates": 3
      }
    },
    {
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      "applied_at_utc": "2026-10-07T09:21:41.221033+00:00",
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        {
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          "new_value": 0.45
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      ],
      "config_before": "candidate-8133e99fc8d4e4c7",
      "config_after": "candidate-668e4305abca59bc",
      "metric_baseline_at_apply": {
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        "team_run_mae": 2.4474,
        "total_run_mae": 3.6316,
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        "starter_er_mae": 1.6795,
        "starter_outs_mae": 4.2393
      },
      "status": "applied_to_production",
      "previous_config_id": "candidate-8133e99fc8d4e4c7",
      "new_config_id": "candidate-668e4305abca59bc",
      "currently_active": true,
      "live_parameter_checks": [
        {
          "parameter": "confidence.empirical_max_weight",
          "old_value": 0.5,
          "applied_value": 0.45,
          "current_live_value": 0.45,
          "currently_matches_applied_value": true
        }
      ],
      "metric_observation": {
        "status": "observed_later_rolling_window",
        "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
        "metrics": [
          {
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            "baseline_at_apply": 0.245566,
            "latest_window": 0.244879,
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            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "accuracy",
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            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "team_run_mae",
            "baseline_at_apply": 2.4474,
            "latest_window": 2.4159,
            "delta": -0.0315,
            "direction": "improved",
            "sample_size": 113
          },
          {
            "metric": "total_run_mae",
            "baseline_at_apply": 3.6316,
            "latest_window": 3.6637,
            "delta": 0.0321,
            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "calibration_error",
            "baseline_at_apply": 0.0992,
            "latest_window": 0.1324,
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            "direction": "regressed",
            "sample_size": 113
          },
          {
            "metric": "starter_er_mae",
            "baseline_at_apply": 1.6795,
            "latest_window": 1.6226,
            "delta": -0.0569,
            "direction": "improved",
            "sample_size": 212
          },
          {
            "metric": "starter_outs_mae",
            "baseline_at_apply": 4.2393,
            "latest_window": 4.184,
            "delta": -0.0553,
            "direction": "improved",
            "sample_size": 212
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        ]
      },
      "prediction_influence": {
        "status": "paired_effect_observed",
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        "paired_previous_config_predictions": 7,
        "changed_score_predictions": 0,
        "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
        "games": [
          {
            "game_id": "20261007_CLE@CHW",
            "date": "20261007",
            "outcome": "hit",
            "live_config_id": "candidate-668e4305abca59bc",
            "parameter_values_recorded_in_prediction": {
              "confidence.empirical_max_weight": 0.45
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.451379,
            "previous_home_win_probability": 0.451379,
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            "live_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 4.0
            },
            "previous_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20261007_LAD@ATL",
            "date": "20261007",
            "outcome": "hit",
            "live_config_id": "candidate-668e4305abca59bc",
            "parameter_values_recorded_in_prediction": {
              "confidence.empirical_max_weight": 0.45
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.491557,
            "previous_home_win_probability": 0.491557,
            "home_probability_delta": 0.0,
            "live_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 4.0
            },
            "previous_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20261007_MIL@SD",
            "date": "20261007",
            "outcome": "hit",
            "live_config_id": "candidate-668e4305abca59bc",
            "parameter_values_recorded_in_prediction": {
              "confidence.empirical_max_weight": 0.45
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.505,
            "previous_home_win_probability": 0.505,
            "home_probability_delta": 0.0,
            "live_predicted_score": {
              "away_runs": 4.0,
              "home_runs": 5.0
            },
            "previous_predicted_score": {
              "away_runs": 4.0,
              "home_runs": 5.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20261007_TB@NYY",
            "date": "20261007",
            "outcome": "miss",
            "live_config_id": "candidate-668e4305abca59bc",
            "parameter_values_recorded_in_prediction": {
              "confidence.empirical_max_weight": 0.45
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.566481,
            "previous_home_win_probability": 0.566481,
            "home_probability_delta": 0.0,
            "live_predicted_score": {
              "away_runs": 3.0,
              "home_runs": 4.0
            },
            "previous_predicted_score": {
              "away_runs": 3.0,
              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20261008_CLE@CHW",
            "date": "20261008",
            "outcome": "hit",
            "live_config_id": "candidate-668e4305abca59bc",
            "parameter_values_recorded_in_prediction": {
              "confidence.empirical_max_weight": 0.45
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.495,
            "previous_home_win_probability": 0.495,
            "home_probability_delta": 0.0,
            "live_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 4.0
            },
            "previous_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20261010_CHW@CLE",
            "date": "20261010",
            "outcome": "hit",
            "live_config_id": "candidate-668e4305abca59bc",
            "parameter_values_recorded_in_prediction": {
              "confidence.empirical_max_weight": 0.45
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.537156,
            "previous_home_win_probability": 0.537156,
            "home_probability_delta": 0.0,
            "live_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 6.0
            },
            "previous_predicted_score": {
              "away_runs": 5.0,
              "home_runs": 6.0
            },
            "score_changed_vs_previous_config": false
          },
          {
            "game_id": "20261011_LAD@MIL",
            "date": "20261011",
            "outcome": "hit",
            "live_config_id": "candidate-668e4305abca59bc",
            "parameter_values_recorded_in_prediction": {
              "confidence.empirical_max_weight": 0.45
            },
            "all_changed_values_verified": true,
            "paired_previous_config": true,
            "live_home_win_probability": 0.548241,
            "previous_home_win_probability": 0.548241,
            "home_probability_delta": 0.0,
            "live_predicted_score": {
              "away_runs": 3.0,
              "home_runs": 4.0
            },
            "previous_predicted_score": {
              "away_runs": 3.0,
              "home_runs": 4.0
            },
            "score_changed_vs_previous_config": false
          }
        ]
      },
      "measurement_status": "paired_effect_observed",
      "rollback_monitor": {
        "status": "monitoring",
        "coverage": {
          "paired_games": 7,
          "distinct_slates": 4
        },
        "minimum_paired_games": 30,
        "minimum_distinct_slates": 3
      }
    }
  ],
  "applied_change": {
    "date": "20261006",
    "applied_at_utc": "2026-10-07T09:21:41.221033+00:00",
    "candidate_id": "candidate-668e4305abca59bc",
    "changes": [
      {
        "parameter": "confidence.empirical_max_weight",
        "old_value": 0.5,
        "new_value": 0.45
      }
    ],
    "config_before": "candidate-8133e99fc8d4e4c7",
    "config_after": "candidate-668e4305abca59bc",
    "metric_baseline_at_apply": {
      "brier_score": 0.245566,
      "accuracy": 0.5877,
      "team_run_mae": 2.4474,
      "total_run_mae": 3.6316,
      "calibration_error": 0.0992,
      "starter_er_mae": 1.6795,
      "starter_outs_mae": 4.2393
    },
    "status": "applied_to_production",
    "previous_config_id": "candidate-8133e99fc8d4e4c7",
    "new_config_id": "candidate-668e4305abca59bc",
    "currently_active": true,
    "live_parameter_checks": [
      {
        "parameter": "confidence.empirical_max_weight",
        "old_value": 0.5,
        "applied_value": 0.45,
        "current_live_value": 0.45,
        "currently_matches_applied_value": true
      }
    ],
    "metric_observation": {
      "status": "observed_later_rolling_window",
      "reason": "This is an observational before/latest rolling-window comparison. It is not causal because the windows can overlap and other inputs also changed.",
      "metrics": [
        {
          "metric": "brier_score",
          "baseline_at_apply": 0.245566,
          "latest_window": 0.244879,
          "delta": -0.000687,
          "direction": "improved",
          "sample_size": 113
        },
        {
          "metric": "accuracy",
          "baseline_at_apply": 0.5877,
          "latest_window": 0.6195,
          "delta": 0.0318,
          "direction": "improved",
          "sample_size": 113
        },
        {
          "metric": "team_run_mae",
          "baseline_at_apply": 2.4474,
          "latest_window": 2.4159,
          "delta": -0.0315,
          "direction": "improved",
          "sample_size": 113
        },
        {
          "metric": "total_run_mae",
          "baseline_at_apply": 3.6316,
          "latest_window": 3.6637,
          "delta": 0.0321,
          "direction": "regressed",
          "sample_size": 113
        },
        {
          "metric": "calibration_error",
          "baseline_at_apply": 0.0992,
          "latest_window": 0.1324,
          "delta": 0.0332,
          "direction": "regressed",
          "sample_size": 113
        },
        {
          "metric": "starter_er_mae",
          "baseline_at_apply": 1.6795,
          "latest_window": 1.6226,
          "delta": -0.0569,
          "direction": "improved",
          "sample_size": 212
        },
        {
          "metric": "starter_outs_mae",
          "baseline_at_apply": 4.2393,
          "latest_window": 4.184,
          "delta": -0.0553,
          "direction": "improved",
          "sample_size": 212
        }
      ]
    },
    "prediction_influence": {
      "status": "paired_effect_observed",
      "live_predictions_recorded": 7,
      "paired_previous_config_predictions": 7,
      "changed_score_predictions": 0,
      "interpretation": "Paired rows compare the live promoted configuration with the preserved previous configuration on the same pregame inputs. Unpaired rows prove live parameter consumption but cannot isolate the prediction delta.",
      "games": [
        {
          "game_id": "20261007_CLE@CHW",
          "date": "20261007",
          "outcome": "hit",
          "live_config_id": "candidate-668e4305abca59bc",
          "parameter_values_recorded_in_prediction": {
            "confidence.empirical_max_weight": 0.45
          },
          "all_changed_values_verified": true,
          "paired_previous_config": true,
          "live_home_win_probability": 0.451379,
          "previous_home_win_probability": 0.451379,
          "home_probability_delta": 0.0,
          "live_predicted_score": {
            "away_runs": 5.0,
            "home_runs": 4.0
          },
          "previous_predicted_score": {
            "away_runs": 5.0,
            "home_runs": 4.0
          },
          "score_changed_vs_previous_config": false
        },
        {
          "game_id": "20261007_LAD@ATL",
          "date": "20261007",
          "outcome": "hit",
          "live_config_id": "candidate-668e4305abca59bc",
          "parameter_values_recorded_in_prediction": {
            "confidence.empirical_max_weight": 0.45
          },
          "all_changed_values_verified": true,
          "paired_previous_config": true,
          "live_home_win_probability": 0.491557,
          "previous_home_win_probability": 0.491557,
          "home_probability_delta": 0.0,
          "live_predicted_score": {
            "away_runs": 5.0,
            "home_runs": 4.0
          },
          "previous_predicted_score": {
            "away_runs": 5.0,
            "home_runs": 4.0
          },
          "score_changed_vs_previous_config": false
        },
        {
          "game_id": "20261007_MIL@SD",
          "date": "20261007",
          "outcome": "hit",
          "live_config_id": "candidate-668e4305abca59bc",
          "parameter_values_recorded_in_prediction": {
            "confidence.empirical_max_weight": 0.45
          },
          "all_changed_values_verified": true,
          "paired_previous_config": true,
          "live_home_win_probability": 0.505,
          "previous_home_win_probability": 0.505,
          "home_probability_delta": 0.0,
          "live_predicted_score": {
            "away_runs": 4.0,
            "home_runs": 5.0
          },
          "previous_predicted_score": {
            "away_runs": 4.0,
            "home_runs": 5.0
          },
          "score_changed_vs_previous_config": false
        },
        {
          "game_id": "20261007_TB@NYY",
          "date": "20261007",
          "outcome": "miss",
          "live_config_id": "candidate-668e4305abca59bc",
          "parameter_values_recorded_in_prediction": {
            "confidence.empirical_max_weight": 0.45
          },
          "all_changed_values_verified": true,
          "paired_previous_config": true,
          "live_home_win_probability": 0.566481,
          "previous_home_win_probability": 0.566481,
          "home_probability_delta": 0.0,
          "live_predicted_score": {
            "away_runs": 3.0,
            "home_runs": 4.0
          },
          "previous_predicted_score": {
            "away_runs": 3.0,
            "home_runs": 4.0
          },
          "score_changed_vs_previous_config": false
        },
        {
          "game_id": "20261008_CLE@CHW",
          "date": "20261008",
          "outcome": "hit",
          "live_config_id": "candidate-668e4305abca59bc",
          "parameter_values_recorded_in_prediction": {
            "confidence.empirical_max_weight": 0.45
          },
          "all_changed_values_verified": true,
          "paired_previous_config": true,
          "live_home_win_probability": 0.495,
          "previous_home_win_probability": 0.495,
          "home_probability_delta": 0.0,
          "live_predicted_score": {
            "away_runs": 5.0,
            "home_runs": 4.0
          },
          "previous_predicted_score": {
            "away_runs": 5.0,
            "home_runs": 4.0
          },
          "score_changed_vs_previous_config": false
        },
        {
          "game_id": "20261010_CHW@CLE",
          "date": "20261010",
          "outcome": "hit",
          "live_config_id": "candidate-668e4305abca59bc",
          "parameter_values_recorded_in_prediction": {
            "confidence.empirical_max_weight": 0.45
          },
          "all_changed_values_verified": true,
          "paired_previous_config": true,
          "live_home_win_probability": 0.537156,
          "previous_home_win_probability": 0.537156,
          "home_probability_delta": 0.0,
          "live_predicted_score": {
            "away_runs": 5.0,
            "home_runs": 6.0
          },
          "previous_predicted_score": {
            "away_runs": 5.0,
            "home_runs": 6.0
          },
          "score_changed_vs_previous_config": false
        },
        {
          "game_id": "20261011_LAD@MIL",
          "date": "20261011",
          "outcome": "hit",
          "live_config_id": "candidate-668e4305abca59bc",
          "parameter_values_recorded_in_prediction": {
            "confidence.empirical_max_weight": 0.45
          },
          "all_changed_values_verified": true,
          "paired_previous_config": true,
          "live_home_win_probability": 0.548241,
          "previous_home_win_probability": 0.548241,
          "home_probability_delta": 0.0,
          "live_predicted_score": {
            "away_runs": 3.0,
            "home_runs": 4.0
          },
          "previous_predicted_score": {
            "away_runs": 3.0,
            "home_runs": 4.0
          },
          "score_changed_vs_previous_config": false
        }
      ]
    },
    "measurement_status": "paired_effect_observed",
    "rollback_monitor": {
      "status": "monitoring",
      "coverage": {
        "paired_games": 7,
        "distinct_slates": 4
      },
      "minimum_paired_games": 30,
      "minimum_distinct_slates": 3
    }
  },
  "change_trace_integrity": {
    "status": "complete",
    "errors": [],
    "candidate_events_read": 12,
    "applied_changes_read": 10,
    "result_events_read": 1772
  }
}

AI providers may only propose registered parameter values within bounded daily deltas. They cannot promote a candidate, run commands, or write to data/. The deterministic lifecycle alone applies configured promotion policy and preserves rollback state.