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=americanfootball_nfl
| Predicted-prob bucket | N | Mean predicted | Actual win rate | Gap (actual − predicted) | Brier |
|---|---|---|---|---|---|
| <50% | 35 | 49.0% | 42.9% | -6.1pp | 0.248 |
| 50-55% | 129 | 51.6% | 48.1% | -3.5pp | 0.251 |
| 55-60% | 40 | 57.5% | 62.5% | +5.0pp | 0.235 |
| 60-65% | 28 | 62.2% | 60.7% | -1.5pp | 0.238 |
| 65-70% | 11 | 67.3% | 72.7% | +5.4pp | 0.202 |
| 70-75% | 9 | 72.9% | 77.8% | +4.9pp | 0.173 |
| 75-80% | 13 | 77.2% | 69.2% | -8.0pp | 0.226 |
| 80-90% | 6 | 85.0% | 66.7% | -18.4pp | 0.252 |