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=mma_mixed_martial_arts
| Predicted-prob bucket | N | Mean predicted | Actual win rate | Gap (actual − predicted) | Brier |
|---|---|---|---|---|---|
| <50% | 89 | 40.8% | 36.0% | -4.9pp | 0.217 |
| 50-55% | 75 | 52.7% | 57.3% | +4.6pp | 0.246 |
| 55-60% | 47 | 57.8% | 44.7% | -13.1pp | 0.257 |
| 60-65% | 60 | 61.6% | 60.0% | -1.6pp | 0.242 |
| 65-70% | 55 | 67.1% | 74.5% | +7.4pp | 0.193 |
| 70-75% | 30 | 71.9% | 86.7% | +14.8pp | 0.140 |
| 75-80% | 20 | 77.2% | 75.0% | -2.2pp | 0.189 |
| 80-90% | 19 | 83.2% | 89.5% | +6.3pp | 0.101 |
| 90%+ | 12 | 93.8% | 91.7% | -2.1pp | 0.082 |