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% | 183 | 49.7% | 46.4% | -3.3pp | 0.250 |
| 50-55% | 737 | 51.7% | 53.2% | +1.5pp | 0.248 |
| 55-60% | 182 | 57.1% | 61.0% | +3.9pp | 0.240 |
| 60-65% | 53 | 62.0% | 67.9% | +6.0pp | 0.222 |
| 65-70% | 47 | 67.5% | 61.7% | -5.8pp | 0.236 |
| 70-75% | 39 | 72.8% | 74.4% | +1.6pp | 0.189 |
| 75-80% | 63 | 77.3% | 69.8% | -7.5pp | 0.216 |
| 80-90% | 73 | 84.5% | 90.4% | +5.9pp | 0.086 |
| 90%+ | 110 | 94.3% | 96.4% | +2.1pp | 0.034 |