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% | 710 | 44.5% | 41.1% | -3.4pp | 0.242 |
| 50-55% | 1732 | 52.3% | 52.8% | +0.5pp | 0.249 |
| 55-60% | 1196 | 57.2% | 55.0% | -2.1pp | 0.248 |
| 60-65% | 939 | 61.5% | 51.2% | -10.3pp | 0.260 |
| 65-70% | 452 | 67.1% | 69.5% | +2.3pp | 0.212 |
| 70-75% | 156 | 72.0% | 73.7% | +1.7pp | 0.194 |
| 75-80% | 241 | 76.6% | 74.7% | -1.9pp | 0.191 |
| 80-90% | 100 | 85.9% | 89.0% | +3.1pp | 0.099 |
| 90%+ | 57 | 94.5% | 93.0% | -1.6pp | 0.065 |