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=ncaaf
| Predicted-prob bucket | N | Mean predicted | Actual win rate | Gap (actual − predicted) | Brier |
|---|---|---|---|---|---|
| <50% | 158 | 49.8% | 46.8% | -2.9pp | 0.250 |
| 50-55% | 647 | 51.8% | 54.3% | +2.4pp | 0.248 |
| 55-60% | 169 | 57.0% | 60.9% | +3.9pp | 0.240 |
| 60-65% | 51 | 61.9% | 66.7% | +4.7pp | 0.226 |
| 65-70% | 42 | 67.5% | 61.9% | -5.5pp | 0.234 |
| 70-75% | 33 | 72.8% | 81.8% | +9.1pp | 0.155 |
| 75-80% | 54 | 77.2% | 72.2% | -5.0pp | 0.203 |
| 80-90% | 60 | 84.7% | 91.7% | +7.0pp | 0.077 |
| 90%+ | 109 | 94.3% | 96.3% | +2.0pp | 0.035 |