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=tennis_atp
| Predicted-prob bucket | N | Mean predicted | Actual win rate | Gap (actual − predicted) | Brier |
|---|---|---|---|---|---|
| 50-55% | 173 | 52.3% | 60.1% | +7.8pp | 0.247 |
| 55-60% | 226 | 57.6% | 60.2% | +2.6pp | 0.241 |
| 60-65% | 97 | 61.4% | 59.8% | -1.6pp | 0.240 |
| 65-70% | 130 | 67.6% | 76.9% | +9.4pp | 0.186 |
| 70-75% | 78 | 71.7% | 65.4% | -6.3pp | 0.228 |
| 75-80% | 115 | 77.3% | 77.4% | +0.1pp | 0.176 |
| 80-90% | 28 | 84.4% | 85.7% | +1.3pp | 0.122 |
| 90%+ | 26 | 93.8% | 88.5% | -5.3pp | 0.103 |