Prediction accuracy

Models compared
ModelScoredWinner acc BrierLog lossRun-margin MAE
Run margin 2274 54.5% 0.2492 0.6926 3.60
Expected runs (xwOBA) 2274 52.9% 0.2500 0.6934 3.57

Lower Brier / log loss = better-calibrated probabilities; accuracy is a coarse tiebreak. Judge model changes by walk-forward backtest.

Detail — xwoba model

2274
Games scored
52.9%
Winner accuracy
0.250
Brier score
0.693
Log loss
3.57
Run-margin abs error
+30.98u
Bet P&L (7.5% ROI)
Is it beating "always pick the home team"?
+0.2pp over 2274 scored games
50% 55% 60% 65% May Jun Jul Aug Sep 52.9% 52.8%
model, cumulative home team every game ±2 SE on the model 2026-04-02 → 2026-09-24

Cumulative, not rolling — a 20-game window on a ~54% signal has an ~11pp standard error, so it only ever showed noise. The first 29 games are omitted while the running mean settles. The baseline still runs inside the model's ±2 SE band — at 2274 games this gap is not yet separable from chance. Live results are one sample; judge model changes by walk-forward backtest.

Calibration
Model home win %GamesPredicted avg Actual home win rateGap
0.00–0.4031 29.7% 58.1% +28.3pp
0.40–0.4521 43.1% 47.6% +4.5pp
0.45–0.50252 48.5% 48.4% -0.1pp
0.50–0.551640 52.7% 52.7% +0.0pp
0.55–0.60295 56.4% 56.9% +0.5pp
0.60–0.6515 61.6% 46.7% -14.9pp
0.65–1.0020 71.3% 55.0% -16.3pp

Well-calibrated buckets have small gaps. Persistent one-directional gaps suggest re-examining the win-probability conversion.