Nightly self-improvement · slate 2026-10-08

Generated 2026-10-09T05:00:00.005735-04:00 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

2026-10-08

status completed

Winners

1/1

Brier 0.2450

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 0 of 2.
Hitter matchup calibration	learned	New per-metric scales for SO 0.45, BB 0.25, HR 1.75. Next slate's player effects read them.
KPI model refit	changed	New model 20261008T091425Z_3ccbc4c8.
KPI model rollback	nothing to learn	Kept 20261008T091425Z_3ccbc4c8: 2 settled club totals of its own, needs 30.
Bullpen usage model	nothing to learn	2 club-games settled, recall 0.77. 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
CLE@CHW	5-4	9-5	0.495	hit		Parker Messick: 15 proj / 7 real outs; Hagen Smith: 8 proj / 2 real outs

Day over day

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

Metric	Prior	Current	Change	Result
brier_score	0.2461	0.2453	-0.0008	improved
accuracy	0.5965	0.6106	0.0141	improved
team_run_mae	2.3860	2.3982	0.0122	regressed
total_run_mae	3.5614	3.5929	0.0315	regressed
calibration_error	0.1090	0.1238	0.0148	regressed
starter_er_mae	1.6736	1.6402	-0.0334	improved
starter_outs_mae	4.2066	4.2383	0.0317	regressed

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	0.449	0.085	1818	learned: the interval excludes 1.0
BB	0.250	-0.260	1818	learned: the interval excludes 1.0
HR	1.750	2.361	1818	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	[-0.0003, 0.0001]
luna_pitcher_barrel_rate_vs_rhh_righthanded_starters	shadow	HR	20260923	330/400	5/5	[-0.0005, 0.0058]
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	122/400	4/5	[-0.0030, 0.0022]
luna_pitcher_contact_xwoba_allowed_vs_righthanded_starters_for_strikeouts	shadow	SO	20260924	122/400	4/5	[-0.0000, 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	[-0.0015, 0.0079]
luna_batter_ground_ball_rate_vs_fastball_vs_lhp_for_home_runs	shadow	HR	20260926	86/400	3/5	[-0.0001, 0.0000]
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 (51)

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 210 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	home_x_fastball 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_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 210 hitter-games in 11 days, mean gain -0.0347, interval (-0.05984, -0.00582)	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 234 hitter-games in 11 days, mean gain -0.0148, interval (-0.01867, -0.01212)	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 922 hitter-games in 13 days, mean gain -0.0095, interval (-0.01697, -0.00262)	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 271 hitter-games in 8 days, mean gain -0.0054, interval (-0.0069, -0.00353)	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 822 hitter-games in 13 days, mean gain -0.0032, interval (-0.00605, -1e-05)	review this KPI's weight in the matchup multiplier; it is an operator change to the recipe
medium	KPI recipe	pitcher_walk_rate (average) on BB: adjustments made BB worse over 880 hitter-games in 13 days, mean gain -0.0007, interval (-0.00159, -3e-05)	review this KPI's weight in the matchup multiplier; it is an operator change to the recipe
medium	KPI model	ATL: KPI blend worse than the baseline over 11 games (MAE 2.462 vs 2.455, bias +0.299)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	HOU: KPI blend worse than the baseline over 8 games (MAE 2.902 vs 1.875, bias -2.073)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	MIA: KPI blend worse than the baseline over 8 games (MAE 1.74 vs 1.5, bias +0.134)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	ARI: KPI blend worse than the baseline over 8 games (MAE 3.175 vs 3.125, bias -1.975)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	COL: KPI blend worse than the baseline over 7 games (MAE 3.165 vs 2.857, bias +0.775)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	SF: KPI blend worse than the baseline over 8 games (MAE 1.861 vs 1.75, bias +1.054)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	LAD: KPI blend worse than the baseline over 10 games (MAE 2.776 vs 2.3, bias +1.513)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	WAS: KPI blend worse than the baseline over 6 games (MAE 2.081 vs 2.0, bias -0.588)	check the wrong-way features listed for this club and its data coverage
medium	KPI model	STL: KPI blend worse than the baseline over 7 games (MAE 1.834 vs 1.571, bias +0.569)	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 recipe	pitcher_home_run_rate_allowed on HR (pitcher): harm survives leaving 1 worst players out, gain -0.0130, interval (-0.01672, -0.011)	the KPI itself misleads; Luna replacement candidates are scored in shadow
low	KPI model recipe	away_off_discipline pushed the KPI forecast away from the result in 69% of 61 scored club totals, net -8.1 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 69 scored club totals, net -5.8 runs	candidate for removal or rework in the next kpi_model version
low	KPI readings	Yuki Matsui (SD): KPI adjustments hurt BB (gain -0.72 over 7 games, bias +0.57), HR (gain -0.01 over 7 games, bias +0.16), H (gain -0.12 over 6 games, bias +0.44)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
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	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	Shay Whitcomb (SF): KPI adjustments hurt SO (gain -0.57 over 6 games, bias +0.19)	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	Manny Machado (SD): KPI adjustments hurt SO (gain -0.53 over 11 games, bias +0.14), H (gain -0.13 over 10 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 -0.52 over 11 games, bias +0.14)	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 SO (gain -0.48 over 8 games, bias -0.02), H (gain -0.17 over 7 games, bias -0.40)	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 -0.48 over 7 games, bias -0.20)	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 -0.42 over 8 games, bias +0.52)	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	Mike Trout (LAA): KPI adjustments hurt SO (gain -0.42 over 7 games, bias +0.34), BB (gain -0.25 over 7 games, bias -0.55), HR (gain -0.01 over 7 games, bias +0.03)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Jac Caglianone (KC): KPI adjustments hurt SO (gain -0.41 over 6 games, bias +0.40), H (gain -0.13 over 5 games, bias -0.91)	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	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	Ozzie Albies (ATL): KPI adjustments hurt SO (gain -0.36 over 11 games, bias -0.34), BB (gain -0.19 over 11 games, bias -0.06)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Cam Smith (HOU): KPI adjustments hurt SO (gain -0.35 over 8 games, bias -0.16), HR (gain -0.04 over 8 games, bias +0.06), H (gain -0.08 over 7 games, bias +0.05)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Anthony Volpe (NYY): KPI adjustments hurt SO (gain -0.34 over 6 games, bias +0.14), H (gain -0.02 over 5 games, bias -0.17)	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	Kyle Isbel (KC): KPI adjustments hurt SO (gain -0.33 over 5 games, bias -0.24)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Adrian Morejon (SD): KPI adjustments hurt SO (gain -0.32 over 5 games, bias +0.83), HR (gain -0.03 over 5 games, bias -0.09)	his KPI reading or its opponent pairing misleads; watch whether it persists before any change
low	KPI readings	Luke Keaschall (MIN): KPI adjustments hurt SO (gain -0.31 over 7 games, bias +0.31)	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 are present and marked `loaded`:**

| Source | Bytes | Purpose |
|---|---:|---|
| prediction review — `/app/output/reports/prediction_review_20261008.json` | 17,218 | Official outcomes and scoring errors |
| forensic deep audit — `/app/output/reports/PREDICTION_SYSTEM_DEEP_AUDIT_20261008.json` | 45,923 | Probability, provenance, starter and decision contracts |
| continuous learning — `/app/output/learning/continuous_learning_daily_20261008.json` | 245,469 | Pipeline stages, calibration, probability lineage, score distribution and starter comparisons |
| detailed report — `/app/output/reports/detailed_20261008.html` | 58,372 | Game and simulation rendering |
| ultra detailed player audit — `/app/output/reports/20261008_ultra_detailed_player_audit.html` | 3,404,020 | Player-level prediction trace |
| structural audit — `/app/output/reports/audit_report_20261008.html` | 244,042 | Invariant findings |
| integrity revision audit — `/app/output/reports/audit_report_20261008_integrity_revision.html` | 126,889 | Structural audit of post-start revisions |

**Game-level review:** one game is supplied in `games[]`: **CLE@CHW**. Its official result is CLE 9, CHW 5. The game record says `games_reviewed: 1`, `hits: 1`, `misses: 0`; the continuous-learning summary says one settled game, also one hit and zero misses. Thus the available game-level result is internally consistent.

**Coverage-count contradiction:** the sam