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_wta
| Predicted-prob bucket | N | Mean predicted | Actual win rate | Gap (actual − predicted) | Brier |
|---|---|---|---|---|---|
| 50-55% | 157 | 52.9% | 61.8% | +8.8pp | 0.241 |
| 55-60% | 136 | 56.5% | 54.4% | -2.1pp | 0.248 |
| 60-65% | 83 | 62.0% | 61.4% | -0.6pp | 0.234 |
| 65-70% | 123 | 67.2% | 78.0% | +10.8pp | 0.183 |
| 70-75% | 42 | 72.5% | 78.6% | +6.1pp | 0.174 |
| 75-80% | 133 | 76.3% | 74.4% | -1.8pp | 0.191 |
| 80-90% | 54 | 87.9% | 88.9% | +1.0pp | 0.099 |
| 90%+ | 25 | 94.9% | 96.0% | +1.1pp | 0.036 |