Nightly self-improvement · slate 2026-10-10

Generated 2026-10-11T05:00:00.007223-04:00 ET from official finals (mlb-api). Every learning stage is listed with what it did. "Blocked" means it wanted to act and a safety rule stopped it; "nothing to learn" means it had no eligible evidence.

Slate

2026-10-10

status completed

Winners

1/1

Brier 0.2239

Learned or changed

of 12 stages

Blocked

wanted to act and could not

Release

v15.28.3

consistent

Audit Detailed KPI Learning KPI Failures Luna KPI Review KPI Executive Odds Impact Odds Intelligence Deep Slate Review Luna Post-mortem Daily Learning Adaptive Status

What each learning stage did

Stage	Outcome	Detail
KPI settlement	learned	2 club totals scored stage by stage; layer moved toward the result on 2 of 2.
Hitter matchup calibration	learned	New per-metric scales for SO 0.45, BB 0.30, HR 1.75. Next slate's player effects read them.
KPI model refit	changed	New model 20261010T091315Z_e1040db9.
KPI model rollback	nothing to learn	Kept 20261010T091315Z_e1040db9: 2 settled club totals of its own, needs 30.
Bullpen usage model	nothing to learn	2 club-games settled, recall 0.80. Refit: challenger did not beat the current model.
Luna KPI review	blocked	Luna was not asked (skipped no pregame evidence): 3176 rows were local replays and none were verified pregame forecasts.
Luna KPI promotion	nothing to learn	10 candidates in shadow; none has enough prospective evidence yet (see below).
KPI winner-flip gate	nothing to learn	0/0 qualified flips over 0 slates; needs 30 flips over 10 slates with an interval above 0.5
Matchup Luna policy	nothing to learn	Settlement: complete; 0 prospective games over 0 dates; decision pending.
Adaptive parameter proposal (Luna)	nothing to learn	skipped_open_rollback_monitor:
Auto-apply	nothing to learn	no_promotable_candidate
Code agent	nothing to learn	disabled_by_config: code changes are made on the development host, never in the container.

Games

Game	Predicted	Actual	Home win prob	Result	Main driver	Starters
CHW@CLE	5-6	0-2	0.527	hit		Sean Burke: 13 proj / 15 real outs; Gavin Williams: 14 proj / 17 real outs

Day over day

Rolling evidence window against the prior night. Associations, not causal proof.

Metric	Prior	Current	Change	Result
brier_score	0.2453	0.2450	-0.0003	improved
accuracy	0.6106	0.6195	0.0089	improved
team_run_mae	2.3982	2.4292	0.0310	regressed
total_run_mae	3.5929	3.6549	0.0620	regressed
calibration_error	0.1238	0.1325	0.0087	regressed
starter_er_mae	1.6402	1.6435	0.0033	regressed
starter_outs_mae	4.2383	4.2222	-0.0161	improved

Calibration produced for the next slate

Metric	Scale used	Measured	Rows	Status
H	1.000	1.372	1662	measured scale confirmed: the interval includes 1.0
SO	0.447	0.048	1662	learned: the interval excludes 1.0
BB	0.304	-0.199	1662	learned: the interval excludes 1.0
HR	1.750	2.538	1662	learned: the interval excludes 1.0

Luna KPI candidates and their promotion gate

Promotion needs 400+ prospective rows over 5+ slates after the proposal, with a gain interval above zero. Only verified pregame forecasts count; local replays never do.

KPI	Status	Target	Proposed	Rows	Slates	Gain interval
luna_pitcher_release_speed_vs_righthanded_starters	shadow	SO	20260923	330/400	5/5	[-0.0016, 0.0007]
luna_pitcher_barrel_rate_vs_rhh_righthanded_starters	shadow	HR	20260923	330/400	5/5	[-0.0005, 0.0066]
luna_batter_contact_rate_vs_breaking_righthanded_starters	shadow	H	20260923	330/400	5/5	[-0.0045, 0.0044]
luna_batter_contact_rate_vs_breaking_left_handed_starters_for_strikeouts	shadow	SO	20260924	86/400	3/5	[-0.0035, 0.0005]
luna_pitcher_contact_xwoba_allowed_vs_righthanded_starters_for_strikeouts	shadow	SO	20260924	122/400	4/5	[-0.0001, 0.0001]
luna_pitcher_whiff_rate_breaking_vs_left_handed_hitters_for_hits	shadow	H	20260924	122/400	4/5	[-0.0056, -0.0011]
luna_pitcher_home_run_rate_vs_righthanded_starters	shadow	HR	20260925	122/400	4/5	[-0.0017, 0.0088]
luna_batter_ground_ball_rate_vs_fastball_vs_lhp_for_home_runs	shadow	HR	20260926	0/400	0/5	[–, –]
luna_pitcher_whiff_rate_vs_right_handed_starters_for_batter_strikeouts	shadow	SO	20260929	51/400	2/5	[–, –]
luna_batter_whiff_rate_vs_breaking_right_handed_starters_for_hits	shadow	H	20260929	51/400	2/5	[–, –]
luna_batter_walk_rate_vs_righthanded_pitchers_for_home_runs	retired	HR	20260924	0/400	0/5	[–, –]

Operator actions the cycle asked for (47)

Changes to code or a model recipe are never made by the nightly cycle. These are the measured findings that need one.

Priority	Owner	Finding	Action
medium	model	starter length biased by +2.1 outs over 191 starts	recalibrate the starter workload projection toward real outs per start
medium	KPI model recipe	away_x_fastball was beyond ±3 standard deviations of its training range in 7 club totals	the live feature is built differently from the training rows; align the two constructions and retrain
medium	KPI model recipe	away_x_suppress was beyond ±3 standard deviations of its training range in 8 club totals	the live feature is built differently from the training rows; align the two constructions and retrain
medium	KPI model recipe	away_off_power_spread was beyond ±3 standard deviations of its training range in 8 club totals	the live feature is built differently from the training rows; align the two constructions and retrain
medium	KPI model recipe	away_x_ground was beyond ±3 standard deviations of its training range in 6 club totals	the live feature is built differently from the training rows; align the two constructions and retrain
medium	KPI model recipe	away_x_breaking was beyond ±3 standard deviations of its training range in 6 club totals	the live feature is built differently from the training rows; align the two constructions and retrain
medium	KPI recipe	pitcher_strikeout_rate (elite) on SO: adjustments made SO worse over 188 hitter-games in 10 days, mean gain -0.0327, interval (-0.05982, -0.00164)	review this KPI's weight in the matchup multiplier; it is an operator change to the recipe
medium	KPI recipe	pitcher_home_run_rate_allowed (poor) on HR: adjustments made HR worse over 218 hitter-games in 10 days, mean gain -0.0148, interval (-0.01888, -0.01168)	review this KPI's weight in the matchup multiplier; it is an operator change to the recipe
medium	KPI recipe	batter_strikeout_rate (average) on SO: adjustments made SO worse over 846 hitter-games in 12 days, mean gain -0.0089, interval (-0.01766, -0.00174)	review this KPI's weight in the matchup multiplier; it is an operator change to the recipe
medium	KPI recipe	pitcher_home_run_rate_allowed (weak) on HR: adjustments made HR worse over 248 hitter-games in 7 days, mean gain -0.0058, interval (-0.00747, -0.00414)	review this KPI's weight in the matchup multiplier; it is an operator change to the recipe
medium	KPI recipe	pitcher_strikeout_rate (average) on SO: adjustments made SO worse over 767 hitter-games in 12 days, mean gain -0.0037, interval (-0.00669, -0.00083)	review this KPI's weight in the matchup multiplier; it is an operator change to the recipe
medium	KPI model	MIA: KPI blend worse than the baseline over 7 games (MAE 1.697 vs 1.571, bias +0.444)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	ARI: KPI blend worse than the baseline over 7 games (MAE 3.219 vs 3.143, bias -1.847)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	SF: KPI blend worse than the baseline over 7 games (MAE 1.861 vs 1.714, bias +1.47)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	LAD: KPI blend worse than the baseline over 9 games (MAE 2.631 vs 2.111, bias +2.135)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	COL: KPI blend worse than the baseline over 6 games (MAE 3.673 vs 3.333, bias +0.924)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	BOS: KPI blend worse than the baseline over 7 games (MAE 2.185 vs 1.714, bias +2.185)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	HOU: KPI blend worse than the baseline over 7 games (MAE 3.176 vs 2.143, bias -2.509)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	SEA: KPI blend worse than the baseline over 6 games (MAE 2.39 vs 2.333, bias -0.345)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	STL: KPI blend worse than the baseline over 6 games (MAE 1.884 vs 1.667, bias +0.919)	check the wrong-way features listed for this club and its data coverage
low	KPI model recipe	away_off_discipline pushed the KPI forecast away from the result in 68% of 56 scored club totals, net -6.7 runs	candidate for removal or rework in the next kpi_model version
low	KPI model recipe	home_platoon_barrel pushed the KPI forecast away from the result in 62% of 64 scored club totals, net -4.6 runs	candidate for removal or rework in the next kpi_model version
low	KPI readings	Jeremiah Jackson (BAL): KPI adjustments hurt SO (gain -0.69 over 5 games, bias -0.29)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Yuki Matsui (SD): KPI adjustments hurt H (gain -0.12 over 6 games, bias +0.44), BB (gain -0.69 over 6 games, bias +0.63), HR (gain -0.02 over 6 games, bias +0.15)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Trea Turner (PHI): KPI adjustments hurt SO (gain -0.61 over 8 games, bias +0.34)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Kyle Schwarber (PHI): KPI adjustments hurt SO (gain -0.58 over 8 games, bias +0.91), BB (gain -0.17 over 8 games, bias -0.25)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Jonny DeLuca (TB): KPI adjustments hurt SO (gain -0.55 over 6 games, bias +0.06)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Coby Mayo (BAL): KPI adjustments hurt H (gain -0.13 over 5 games, bias -0.20), SO (gain -0.55 over 5 games, bias +0.20), BB (gain -0.05 over 5 games, bias -0.27)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Jordan Walker (STL): KPI adjustments hurt SO (gain -0.53 over 6 games, bias +0.18)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Christian Walker (HOU): KPI adjustments hurt SO (gain -0.45 over 7 games, bias +0.47)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Tim Tawa (ARI): KPI adjustments hurt H (gain -0.42 over 7 games, bias +0.10)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Ty France (SD): KPI adjustments hurt SO (gain -0.42 over 10 games, bias +0.03)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Ketel Marte (ARI): KPI adjustments hurt H (gain -0.41 over 7 games, bias +0.05)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Colson Montgomery (CHW): KPI adjustments hurt H (gain -0.28 over 6 games, bias +0.64), SO (gain -0.40 over 6 games, bias -0.66)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Jac Caglianone (KC): KPI adjustments hurt H (gain -0.13 over 5 games, bias -0.91), SO (gain -0.39 over 5 games, bias +0.20)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Drake Baldwin (ATL): KPI adjustments hurt SO (gain -0.38 over 9 games, bias +0.51), HR (gain -0.01 over 9 games, bias +0.05)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Yandy Díaz (TB): KPI adjustments hurt H (gain -0.21 over 5 games, bias -0.52), SO (gain -0.38 over 5 games, bias +0.88)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	George Lombard Jr. (NYY): KPI adjustments hurt H (gain -0.17 over 7 games, bias -0.40), SO (gain -0.36 over 7 games, bias +0.02)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Heriberto Hernández (MIA): KPI adjustments hurt H (gain -0.10 over 7 games, bias +0.19), SO (gain -0.36 over 7 games, bias +0.43), BB (gain -0.12 over 7 games, bias -0.03)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Ozzie Albies (ATL): KPI adjustments hurt SO (gain -0.35 over 10 games, bias -0.34), BB (gain -0.19 over 10 games, bias -0.10)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Bryce Harper (PHI): KPI adjustments hurt SO (gain -0.33 over 8 games, bias +0.05)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Cam Smith (HOU): KPI adjustments hurt H (gain -0.08 over 7 games, bias +0.05), SO (gain -0.32 over 7 games, bias -0.05), HR (gain -0.04 over 7 games, bias +0.05)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Corbin Carroll (ARI): KPI adjustments hurt H (gain -0.30 over 7 games, bias +0.19)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Danny Jansen (TEX): KPI adjustments hurt SO (gain -0.29 over 6 games, bias +0.40)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Christian Moore (LAA): KPI adjustments hurt SO (gain -0.28 over 7 games, bias +0.06), HR (gain -0.02 over 7 games, bias -0.03)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Carson Benge (NYM): KPI adjustments hurt H (gain -0.13 over 6 games, bias -0.64), SO (gain -0.28 over 6 games, bias +0.04)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Taylor Trammell (HOU): KPI adjustments hurt H (gain -0.10 over 5 games, bias +0.71), SO (gain -0.28 over 5 games, bias -0.42), HR (gain -0.03 over 5 games, bias +0.12)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change

Luna post-mortem

Luna (gpt-6-luna) post-mortem, status received. Full analysis.

## 1. Evidence receipt and gaps

**All seven `source_manifest` entries were marked `loaded`:**

1. **prediction review** — `/app/output/reports/prediction_review_20261010.json`, 11,425 bytes; official outcomes and scoring errors.
2. **forensic deep audit** — `/app/output/reports/PREDICTION_SYSTEM_DEEP_AUDIT_20261010.json`, 30,956 bytes; probability, provenance, starter, and decision contracts.
3. **continuous learning** — `/app/output/learning/continuous_learning_daily_20261010.json`, 185,336 bytes; pipeline-stage quality, calibration, probability lineage, score distribution, and official starter comparisons.
4. **detailed report** — `/app/output/reports/detailed_20261010.html`, 33,426 bytes; full game and simulation rendering.
5. **ultra detailed player audit** — `/app/output/reports/20261010_ultra_detailed_player_audit.html`, 1,811,791 bytes; player-level prediction trace.
6. **structural audit** — `/app/output/reports/audit_report_20261010.html`, 128,231 bytes; invariant findings.
7. **integrity revision audit** — `/app/output/reports/audit_report_20261010_integrity_revision.html`, 365,249 bytes; structural audit of three post-start revision artifacts.

**Slate coverage:** one game was expected, reviewed, structurally audited, included in continuous learning, and given a starter comparison: each corresponding `coverage_matrix` count is 1/1. The sole game, **CHW@CLE**, was a hit on the winner, not a loss. Full coverage does not remove the game-level run-score and model-qual