evidence / Calibration & market
In-Game Conditioning -- the one measured calibration win
Pregame, the market is efficient: across 4 sports and 6 independent corpora my leak-free forecaster MATCHES the Shin-devigged closing line within noise and beats nothing. The one place the machinery measurably sharpens is IN-GAME -- fusing the pregame rating prior with the realized mid-game state improves win-probability calibration. A live book sees the score too, so this is a CALIBRATION result, not a claim of beating anyone. Every number below is Brier / MAE / row-count with its source artifact. The single truth-source for any figure is docs/JOB_EVIDENCE_PACKET.md; edge_claimed = False throughout, and no dollar, ROI, or edge figure appears anywhere on this page.
strongest single receipt
the claim
The honest question is not "can I beat the close" -- I proved against myself that I cannot, pregame -- but "does the system's mid-game conditioning actually sharpen the forecast." It does, and the win is measured, not asserted: conditioning the win-probability forecaster on the realized in-game state improves calibration (Brier) on real out-of-sample corpora in two sports. This is forecaster quality. A live book also sees the score, so no beat-the-market test applies to an in-game number and none is claimed.
receipts
| claim | committed artifact |
|---|---|
| NBA static->conditional Brier 0.209 -> 0.159 | scripts/platformkit/proof_nba/ingame_accuracy.py |
| MLB static->conditional Brier 0.241 -> 0.126 | scripts/platformkit/proof_mlb/ingame_accuracy.py |
| Cross-sport roll-up | scripts/platformkit/ingame_scoreboard.py |
| end_q1 MARKET_SHARPER delta -0.0084, halftime/end_q3/q4 UNDERPOWERED, n~1593 | scripts/platformkit/benchmarks/crps_market/last_run_ingame_nba_winprob_ALLGAMES_v3.json |
| Same, pre-registered ledger row | [RECEIPTS.md](../../RECEIPTS.md) (batch 6eba592d3a77, nba end_q1 row) |
| ~46% pooled / ~26% learned-head MAE lift | leak-clean 14-feature schema, two walk-forward harnesses; see [JOB_EVIDENCE_PACKET s3](../JOB_EVIDENCE_PACKET.md) |
| Full receipts write-up | [docs/INGAME_PROOF.md](../INGAME_PROOF.md) |
why this matters
The in-game conditioning result is small, honest, and load-bearing for exactly that reason. The static -> conditional Brier improvement is a real calibration gain, most of it mechanical, with the model's own prior contributing a measured ~0.014 in NBA. Benchmarked against the live market, the conditional forecaster is sharper nowhere it is powered: the market wins the one resolvable checkpoint and ties everywhere else, the loss stated as plainly as any win. The hire signal is not the metric. It is a forecaster whose author scores it against the market, publishes the checkpoint where the market wins, and refuses to convert a real calibration improvement into an edge claim it did not earn.
edge_claimed = False everywhere. Every number is Brier / MAE / row-count vs a real out-of-sample corpus, never a dollar figure. Retracted measurement artifacts appear only in [JOB_EVIDENCE_PACKET.md](../JOB_EVIDENCE_PACKET.md), never on this page.
reproduce
# NBA + MLB in-game calibration scoreboard (real corpus if present)
python -m scripts.platformkit.ingame_scoreboard
# the per-sport proof harnesses directly
python -m scripts.platformkit.proof_nba.ingame_accuracy
python -m scripts.platformkit.proof_mlb.ingame_accuracy
# the leak property test (truncation invariance) -- runs on any clone
python -m pytest tests/test_ingame_leak_free.py -qThis site reports calibration and sharpness only, never a dollar edge, ROI, or bankroll result. An honest null is a success.