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
| Predicted-prob bucket | N | Mean predicted | Actual win rate | Gap (actual − predicted) | Brier |
|---|---|---|---|---|---|
| <50% | 1072 | 42.8% | 39.8% | -3.0pp | 0.233 |
| 50-55% | 1904 | 52.3% | 51.2% | -1.0pp | 0.250 |
| 55-60% | 1287 | 57.4% | 55.7% | -1.6pp | 0.247 |
| 60-65% | 953 | 61.6% | 52.7% | -9.0pp | 0.257 |
| 65-70% | 535 | 67.2% | 68.0% | +0.9pp | 0.217 |
| 70-75% | 186 | 72.2% | 74.2% | +2.0pp | 0.191 |
| 75-80% | 331 | 76.6% | 75.5% | -1.1pp | 0.186 |
| 80-90% | 152 | 86.2% | 86.8% | +0.7pp | 0.112 |
| 90%+ | 93 | 93.8% | 94.6% | +0.8pp | 0.051 |