Findings
The honesty exhibits
These pages make the honest rail concrete. No dollar edge is claimed anywhere; each exhibit either takes a number apart, deflates our own sample, or publishes a result that failed — on purpose.
The six numbers we took back, each with what it was, why it was wrong, and where it now lives only inside its retraction.
We deflate our own row counts: 78,986 within-game MLB rows carry the independent information of at most ~227 games, so confidence intervals must widen accordingly.
The claim families that changed their mind as more data arrived -- each mind-change in sequence, plus how long the retracted claims lived. A flip is the process working.
Descriptive Statcast-derived leaderboards from a fixed 2022-2023 window, published beside the gate nulls that failed -- park factor too unstable to rank, umpire tendency does not move totals.
Momentum is real point-to-point (n=456,383, confirmed) but dies at the game grain; two tiebreak myths come back null; altitude and travel effects are confirmed, altitude replicated across two disjoint year slices.
The honest split: structural fatigue, rest, and clutch effects are confirmed and some replicated, while the individual hot/cold carryover shapes -- even a player's own B2B dip at n=65,103 -- come back null.
The module that honestly refused a clutch number, turned into a descriptive one: per-player Q4-vs-earlier per-36 shifts over 1,231 games -- published with the blowout/garbage-time confound in plain sight, never as a clutch or predictive claim.
Empirical-Bayes shrinkage on the MLB rate leaderboards: small-sample 'leaders' get pulled toward the group mean by how few trials back them, while a huge-sample rate barely moves. The raw-vs-shrunk gap is the honesty.
Calibration reliability diagrams and the Murphy Brier decomposition: our model's Brier trails the market's (0.2377 vs 0.2067 in MLB), and the gap is resolution -- information we don't have -- not miscalibration. We match or trail the close; we never claim to beat it.
How a market absorbs information, twice: pre-game line half-life per sport, then in-game Brier checkpoints where the market stays ahead of our model at every point measured. Descriptive only, same honest gap as reliability.
49,425 international matches split on the real neutral-site flag: the true-home/neutral goal-diff gap grew from 0.22 to 0.50 not because true-home advantage rose (flat) but because the neutral-venue edge collapsed over time.
Proportional-devig Brier on shared-game subsets: Pinnacle is barely the sharpest book on tennis match-winner (0.1975 vs Bet365's 0.1980), while on soccer over/under 2.5 goals the books are a statistical dead heat.
A transparent on/off rim-deterrence leaderboard from real NBA zone splits: Wembanyama (-0.0618) and Gobert (-0.0632) top their seasons, matching the eye test -- published beside the roster confound it can't remove.
A 3-season parity ledger: win-share Gini rose from 0.177 (2023-24) to 0.2004 (2025-26), published beside the partial-season and playoff-pooling caveats that keep it a description, not a trend.
A five-man lineup-synergy ledger: the Grizzlies' Jackson-Morant-Bane-Edey-Wells five topped 2024-25 at +24.32 net-per-48 above expected -- published beside the single-season and small-minutes confounds.
A favorite-longshot bias audit that grades the MARKET, not our model: tennis shows a mild, monotone bias favoring favorites (gap grows from -0.0026 to +0.0176 across five buckets), while MLB moneyline is essentially efficient.