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=3,254 NCAAF N=960 TENNIS_WTA N=668 TENNIS_ATP N=567 MMA_MIXED_MARTIAL_ARTS N=522 LALIGA N=219 AMERICANFOOTBALL_NFL N=187 SERIEA N=171 EPL N=169 NHL N=162 LIGUE1 N=147 NFL_PRESEASON N=145 BUNDESLIGA N=128 UCL N=71 CRICKET_IPL N=27 NCAAB N=11 NBA N=9

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 = 147 · mean predicted 46.4% · actual win rate 38.1% · gap -8.3pp · Brier 0.242 · log-loss 0.677

Calibration by predicted-probability bucket.
Predicted-prob bucket	N	Mean predicted	Actual win rate	Gap (actual − predicted)	Brier
<50%	89	38.3%	37.1%	-1.2pp	0.218
50-55%	21	52.6%	33.3%	-19.2pp	0.260
55-60%	16	57.1%	50.0%	-7.1pp	0.255
60-65%	5	61.8%	40.0%	-21.8pp	0.294
65-70%	16	67.4%	37.5%	-29.9pp	0.327

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.