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% | 163 | 52.4% | 59.5% | +7.2pp | 0.247 |
| 55-60% | 188 | 57.4% | 58.0% | +0.6pp | 0.244 |
| 60-65% | 84 | 61.6% | 58.3% | -3.3pp | 0.242 |
| 65-70% | 111 | 67.3% | 77.5% | +10.2pp | 0.185 |
| 70-75% | 77 | 71.7% | 66.2% | -5.5pp | 0.225 |
| 75-80% | 87 | 77.0% | 74.7% | -2.3pp | 0.191 |
| 80-90% | 18 | 83.8% | 88.9% | +5.0pp | 0.099 |
| 90%+ | 20 | 94.6% | 90.0% | -4.6pp | 0.091 |