Luna deep analysis — slate 20260929

Coverage receipt: COMPLETE · 9/8 sections; 4/4 games; 7/7 causes; 6/6 sources

1. Evidence receipt and gaps

Sources received

All seven manifest entries were checked:

SourceStatusPurposeReceipt finding
prediction review — /app/output/reports/prediction_review_20260929.jsonloadedOfficial outcomes and scoring errorsPresent, 102,938 bytes
forensic deep audit — /app/output/reports/PREDICTION_SYSTEM_DEEP_AUDIT_20260929.jsonloadedProbability, provenance, starter, and decision contractsPresent, 75,641 bytes
continuous learning — /app/output/learning/continuous_learning_daily_20260929.jsonloadedStage quality, calibration, lineage, score distribution, starter comparisonsPresent, 681,575 bytes
detailed report — /app/output/reports/detailed_20260929.htmlloadedFull game and simulation renderingPresent, 96,839 bytes
ultra detailed player audit — /app/output/reports/20260929_ultra_detailed_player_audit.htmlloadedPlayer-level prediction tracePresent, 8,510,517 bytes
structural audit — /app/output/reports/audit_report_20260929.htmlloadedInvariant findingsPresent, 518,619 bytes
integrity revision audit — /app/output/reports/audit_report_20260929_integrity_revision.htmlmissingPost-Luna remediation proof for immutable revision artifactsMissing; byte count is null

Slate coverage

All four listed games were reviewed:

  1. BOS@NYY
  2. CHC@SD
  3. CHW@HOU
  4. PHI@ATL

Coverage is internally complete for the game-level prediction audit:

Gaps and contradictions

  1. Missing integrity revision audit. The only missing manifest artifact is the immutable revision/remediation proof. This is a documentation and artifact-verification gap, not evidence that the predictions themselves were altered.
  2. No market cross-check. market_crosscheck.available is false because no source-labelled moneyline covers were available.
  3. No actual losses. The loss-specific fields are null or empty:
  4. misses: 0
  5. loss_primary_drivers: {}
  6. mean_team_run_error_on_losses: null
  7. mean_total_run_bias_on_losses: null Consequently, no game can be reconstructed as a winner miss.
  8. Calibration terminology is inconsistent. The bucket shows average confidence 0.5208, observed hit rate 1.0, and error -0.4792, correctly indicating underconfidence under the stated definition (“average published confidence minus observed hit rate”). Elsewhere, the self-assessment calls the slate “confidence_overconfident” and recommends lowering caps. That recommendation is not supported by this four-game bucket alone.
  9. The top-pick streak data contains an invalid date. top_confidence_pick.per_slate includes one entry with date: "" and result unknown. The aggregate loss_streak: 0 remains supported by the dated current entry, but the sequence is not clean enough to treat every historical position as fully auditable.
  10. The model reports 100% winner accuracy while run-model validation is weak. This is not a contradiction: all four winners were correct, but exact scores and margins were poor.
  11. “Forecast-file integrity: clean” appears for every game, while the manifest still lacks the separate integrity revision audit. These refer to different checks. The game artifacts passed their structural audits; the separate revision-proof artifact remains unavailable.

2. Executive diagnosis

Winner accuracy

Excellent on this slate, but weak evidence of broad model quality.

The correct winner was sometimes produced by a very small edge:

Those should not be treated as strong directional forecasts.

Calibration quality

Poorly measured on this slate and not suitable for a permanent recalibration from four observations.

The only reported confidence bucket is:

By the supplied definition, the negative signed error indicates underconfidence, not overconfidence. The four correct calls do not establish that the confidence system is generally too conservative; the observed hit rate is an unusually noisy four-game realization.

The high-severity single_slate_calibration_instability finding is therefore appropriate: the bucket is badly aligned on this slate, but the registered guardrail correctly says not to change calibration weights from a single four-game sample.

Score accuracy

Materially weak. Correct winners did not imply accurate scores.

Published versus actual scores:

The score_margin_compression issue is correctly rated high. It is a material run-model risk, not a critical contract failure. The guardrail is also correct: one or two blowouts do not justify globally widening every score prediction.

The validated run signal is weak:

The weak_run_model_signal finding is therefore a serious modeling concern, but its proposed remedy is feature/target and training work, not a small nightly parameter adjustment.

Starter accuracy

Mixed but not a slate-wide starter failure.

Across the games:

Cross-game evidence does not justify changing pitcher weights:

Artifact integrity

Game-level structural integrity passed; one separate revision artifact is missing.

Causal issue register reconciliation

pipeline_winner_instability — high

This is a real process/model risk even though enhanced prediction later restored both winners.

score_margin_compression — high

single_slate_calibration_instability — high

weak_run_model_signal — high

complete_slate_audit_coverage — info

probability_publication_delta_explained — info

simulation_inert_on_slate — info on this slate, escalated across slates


3. Every game

BOS@NYY

Prediction and result

This is explicitly a correct-winner/wrong-score game.

Pregame edges and controls

Starter projection versus actual

StarterProjectedActualError
Payton Tolle, BOS2 ER, 17 outs, 3.18 ERA1 ER, 14 outs, 1.93 ERAER -1, outs -3, ERA -1.25
Cam Schlittler, NYY0 ER, 18 outs, 0.00 ERA0 ER, 19 outs, 0.00 ERAER 0, outs +1, ERA 0

Starter identity matching passed.

Score path and player adjustments

The primary score miss was offensive, not directional:

Only seven of 27 material player deviations across the slate were supported by the hitter’s own contact-quality profile; Rice was one of the supported cases.

Audit and causes


CHC@SD

Prediction and result

This is also a correct-winner/wrong-score game.

Pregame edges and controls

Starter projection versus actual

StarterProjectedActualError
Matthew Boyd, CHC5 ER, 18 outs, 7.50 ERA4 ER, 12 outs, 9.00 ERAER -1, outs -6, ERA +1.50
Michael King, SD3 ER, 17 outs, 4.76 ERA0 ER, 21 outs, 0.00 ERAER -3, outs +4, ERA -4.76

King’s start was a major starter projection miss. The forensic synthesis classifies it as variance: no credible venue, opponent-strength, or experience signal explained the performance.

Score path and player adjustments

The game illustrates why a near-even call can still have a poor score: the model’s directional edge was only approximately 1.6 percentage points, and SD’s 8–0 result was driven by both the King outlier and offensive deviations.

Audit and causes


CHW@HOU

Prediction and result

This was the best score-quality game of the slate, though it still missed the exact total by two runs.

Pregame edges and controls

Starter projection versus actual

StarterProjectedActualError
Hagen Smith, CHW1 ER, 15 outs, 1.80 ERA0 ER, 9 outs, 0.00 ERAER -1, outs -6, ERA -1.80
AJ Blubaugh, HOU0 ER, 15 outs, 0.00 ERA0 ER, 2 outs, 0.00 ERAER 0, outs -13, ERA 0

The settled per-player feed lacks a pitching line for Hagen Smith, despite the game-level starter comparison containing projected and actual values. The report explicitly marks this as starter_line_missing for the player-level matchup breakdown. That is a data-coverage limitation, not a structural audit error.

The HOU starter’s short outing was also materially different in workload, although not in ER.

Score path and player adjustments

This was the game most supportive of the existing recent/season run-mean weighting: the team-run error was only two, and the away-side directional fundamentals were strongly favorable.

Audit and causes


PHI@ATL

Prediction and result

This is a correct-winner/wrong-score game.

Pregame edges and controls

Starter projection versus actual

StarterProjectedActualError
Jesús Luzardo, PHI3 ER, 20 outs, 4.05 ERA1 ER, 15 outs, 1.80 ERAER -2, outs -5, ERA -2.25
Chris Sale, ATL2 ER, 19 outs, 2.84 ERA3 ER, 19 outs, 4.26 ERAER +1, outs 0, ERA +1.42

The starter projection was imperfect on both sides but did not clearly drive the result.

Score path and player adjustments

Audit and causes


4. Miss reconstruction

Game-level losses

There were no published winner losses. All four games were winner hits, so there is no loss to reconstruct in the usual sense.

There were, however, score and intermediate-stage failures:

A later recovery does not erase this intermediate winner-direction regression.

Earliest wrong-direction stage

The earliest measured regression was:

The next stage, variance_applied → enhanced_prediction, recovered both:

Therefore the proper diagnosis is intermediate winner-direction instability, not “no problem because the final picks were correct.”

A home-away confidence edge of 0.12, if encountered in another slate, would be moderate—not a near coin flip. On this slate, the largest edge was only 0.08; CHC@SD and PHI@ATL were approximately 0.02 and 0.01.

Cause classification

CategoryEvidence on this slateAssessment
Bad inputOne settled player-level starter line missing for Hagen Smith; no structural errorsLimited data gap, not a demonstrated winner cause
Bad weightingVariance stage regressed two winner directions; recent/season weighting helped two large score misses but could disturb CHW@HOUMaterial process risk at variance stage; run-weight change not yet justified
CalibrationFour-game calibration error 0.4792; bucket is underconfident by stated definitionUnstable measurement, not enough evidence for permanent recalibration
Starter errorKing was 3 ER better than projected; PHI/ATL starter errors; BOS starters broadly accurateSome individual variance; pitcher lever net tension is unfavorable
Offense/bullpen missNYY offense exceeded projection; CHC offense underperformed and SD offense exceeded projection; PHI bullpen allowed 3 ERMajor source of score error
Run-environment missPredicted margins were compressed; total errors were -3,+1,+2,-3Material score-model issue
Irreducible varianceKing’s outlier start and player-level deviations unsupported by stable profilesImportant explanation; do not tune directly to isolated performances

Pipeline-stage quality across all stages

The stored stages compare directly to official final scores:

StageWinner accuracyTeam-run MAETotal-run MAEMargin MAEInterpretation
historical_ml1.0002.2810.64854.562Baseline
variance_applied0.5002.3750.7504.750Winner regression; score regression
enhanced_prediction1.0002.5002.0004.500Recovers winners, but total/team score metrics worsen versus baseline
embedded_savant_run_environment1.0002.3752.2504.250Margin improves; total MAE worsens versus enhanced
fundamental_edge_target_review1.0002.3752.2504.250No additional change
capped_target1.0002.3752.2504.250No additional change
simulation_result1.0002.3752.2504.250No winner intervention
published_final1.0002.3752.2504.250No change from simulation stage

Transition audit:

The later recovery is useful, but it does not cancel the unstable variance-stage behavior.


5. Cross-game counterfactuals

Lever tension: score variance multiplier / floor

Lever tension: pitcher form weights / prior games

Lever tension: recent/season run-mean weighting

Tempting one-game fixes that must not be made

  1. Do not widen all score distributions solely because BOS@NYY and CHC@SD were blowouts.
  2. Do not increase pitcher-form weighting solely because Michael King outperformed his projection.
  3. Do not lower all confidence caps solely because the current four-game bucket has a 0.4792 absolute calibration error; under the stated definition the bucket is underconfident.
  4. Do not treat all hitter deviations as learnable. Only 7/27 material deviations were supported by the hitter’s own season contact quality.
  5. Do not infer that simulation is harmless because the final winners were unchanged. It has been inert for 11 consecutive slates.
  6. Do not tune the near-zero run model with a nightly variance knob alone. The registered diagnosis calls for feature/target work and an isolated retraining backtest.
  7. Do not change the pitcher or run-mean levers without preserving the counterfactual effects on BOS@NYY, CHW@HOU, and other unaffected games.

6. Data and contract defects

Critical findings

There are no game-level critical findings:

However, the missing integrity revision audit is a receipt gap that prevents complete artifact-proof verification. It is not classified by the supplied audit as a critical prediction defect.

Error findings

There are no game-level error findings:

Warning findings

There are no warning findings:

Informational findings

There are 31 informational findings in the structural summary. Per-game counts are:

These include the informational notes associated with clean artifact status, recorded transformations, and diagnostics. Informational status does not mean the model was accurate; it means the contract/audit did not identify a structural violation.

Prediction-model defects versus presentation/artifact bugs

Prediction/model defects or risks

Data/artifact/presentation issues

Probability lineage audit

The complete pick-side lineage is available for all four games:

BOS@NYY

raw_model_pick_probability 0.5973 → governor input 0.5175 → calibrated/governor output 0.5554 → score coherence 0.555379 → published 0.540

Transformation: score coherence followed by matchup-recency guardrail. No unexplained delta.

CHC@SD

raw 0.5143 → governor input 0.5300 → calibrated/governed 0.5109 → score coherence 0.510929 → published 0.507896

Transformation: published value matches the calibrated/governed value. No unexplained delta.

CHW@HOU

raw 0.5898 → governor input 0.5300 → calibrated/governed 0.5513 → score coherence 0.551288 → published 0.530

Transformation: score coherence followed by matchup-recency guardrail. The material movement is recorded and explained; it is not a governor bypass.

PHI@ATL

raw 0.5079 → governor input 0.5264 → calibrated/governed 0.5075 → score coherence 0.507466 → published 0.505394

Transformation: published probability matches the calibrated/governed value. No unexplained delta.

Conclusion: no material lineage bypass is demonstrated. A bypass should be called only if a material movement lacks a recorded transformation; that condition is not present here.


7. Cross-slate persistence

A single slate is noisy, but several findings have multi-slate evidence.

Finding streaks

FindingConsecutive-slate countPresent now?Interpretation
high_raw_probability_cluster0NoHistorical incidence in 2 of 11 window slates, but not current
pipeline_winner_instability1YesCurrent slate shows the issue; incidence 6 of 11, but only a one-slate current streak
score_margin_compression1YesCurrent slate shows it; incidence 3 of 11
simulation_inert_on_slate11YesEstablished persistent failure, not single-slate noise
single_slate_calibration_instability1YesCurrent slate shows it; incidence 7 of 11, but current streak is only one
weak_run_model_signal1YesCurrent slate shows it; incidence 10 of 11, but the registered streak field is one

The escalated_findings entry must be treated as established:

Simulation inertness

This is a code/diagnostic failure, not a reason to force a parameter move.

Raw structural blocks

Margin distribution

Current slate:

The trend block labels margin bias regressing, with mean -1.9532.

Pipeline transitions

The current slate confirms an intermediate winner-direction regression:

Calibration monitoring

The trend labels weighted calibration error regressing, with mean 0.2201. This makes calibration a monitoring priority, but the current four-game direction is underconfidence by the stated definition.

High-raw cluster

There is no current high-raw-confidence cluster.

Simulation control

The 11-slate streak shows that this is not an isolated no-op.

Metric trend directions

Each supplied metric trend is explicit:

The winner-accuracy improvement must not be used to dismiss score-model and pipeline risks.

Top-confidence-pick loss streak


8. Adjustments for today

Recommendations are ranked by persistence, scope, expected metric impact, and regression risk.

1. Highest priority: diagnose and repair simulation inertness

2. Operator-level run-model repair

3. Bounded shadow experiment for margin compression

4. Calibration caps: monitor, do not immediately promote

5. Do not adjust pitcher-form weights today

6. Do not make a recent/season run-mean production change today


9. Bottom line

Known

Uncertain

Single safest next action

Run a non-promoting shadow diagnostic that verifies the simulation module is actually executing an independent, non-steered probability distribution, while preserving the current published pipeline.

This is safest because the failure has the strongest persistence evidence—11 consecutive slates—is registered as a code/diagnostic problem rather than a speculative parameter issue, and can be evaluated with rollback monitors on team-run MAE, total-run MAE, margin-tail coverage, and winner Brier score without changing today’s production picks.