For every bucket of predicted probability, what did the model actually hit? A well-calibrated model has actual win rate ≈ predicted prob. A negative gap means the model is overconfident in that bucket (dangerous); positive gap means it's underpredicting (safe). Rows turn red when |gap| > 5pp AND N ≥ 10.
Combined "all sports" is rarely meaningful — sports differ in market efficiency, signal availability, and base rates. Use the chips to drill into a single sport. N<50 (red) means the calibration is brittle; N≥200 (green) is trustworthy.
Filter: window=90d · sport=nba
| Predicted-prob bucket | N | Mean predicted | Actual win rate | Gap (actual − predicted) | Brier |
|---|---|---|---|---|---|
| <50% | 9 | 49.0% | 44.4% | -4.6pp | 0.259 |
| 50-55% | 21 | 52.6% | 47.6% | -5.0pp | 0.252 |
| 55-60% | 7 | 56.6% | 85.7% | +29.1pp | 0.209 |
| 60-65% | 3 | 63.7% | 0.0% | -63.7pp | 0.406 |
| 65-70% | 1 | 68.0% | 0.0% | -68.0pp | 0.462 |
| 70-75% | 1 | 72.0% | 0.0% | -72.0pp | 0.518 |