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% | 20 | 49.1% | 50.0% | +0.9pp | 0.247 |
| 50-55% | 89 | 51.8% | 50.6% | -1.2pp | 0.250 |
| 55-60% | 29 | 57.6% | 62.1% | +4.5pp | 0.237 |
| 60-65% | 24 | 62.2% | 62.5% | +0.3pp | 0.235 |
| 65-70% | 6 | 67.4% | 66.7% | -0.7pp | 0.226 |
| 70-75% | 6 | 72.7% | 83.3% | +10.7pp | 0.148 |
| 75-80% | 9 | 77.2% | 55.6% | -21.6pp | 0.303 |
| 80-90% | 4 | 85.4% | 100.0% | +14.6pp | 0.022 |