Model calibration audit

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.

Calibration cohort by sport (click to filter):

All sports MLB N=2,486 NCAAF N=1,487 TENNIS_WTA N=734 TENNIS_ATP N=659 NHL N=591 MMA_MIXED_MARTIAL_ARTS N=526 AMERICANFOOTBALL_NFL N=313 LALIGA N=302 SERIEA N=276 EPL N=253 LIGUE1 N=222 BUNDESLIGA N=206 NBA N=166 UCL N=158 NFL_PRESEASON N=145 CRICKET_IPL N=27

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=ligue1

Overall: N = 222 · mean predicted 45.3% · actual win rate 36.5% · gap -8.8pp · Brier 0.232 · log-loss 0.654

Calibration by predicted-probability bucket.
Predicted-prob bucket	N	Mean predicted	Actual win rate	Gap (actual − predicted)	Brier
<50%	135	37.6%	31.9%	-5.8pp	0.207
50-55%	38	52.1%	44.7%	-7.4pp	0.252
55-60%	26	57.3%	46.2%	-11.2pp	0.258
60-65%	8	61.4%	50.0%	-11.4pp	0.270
65-70%	15	67.4%	33.3%	-34.1pp	0.341

JSON feed · Model CLV · Methodology

Calibration is computed from the prediction_history table. Bucket edges are 5pp wide (50-55%, 55-60%, etc.) with widened tails (<50%, 80-90%, 90%+) where data is sparser. Negative gap is the dangerous direction — actual win rate below stated confidence.