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=epl

Overall: N = 169 · mean predicted 46.4% · actual win rate 35.5% · gap -10.9pp · Brier 0.240 · log-loss 0.671

Calibration by predicted-probability bucket.
Predicted-prob bucket	N	Mean predicted	Actual win rate	Gap (actual − predicted)	Brier
<50%	95	37.1%	35.8%	-1.3pp	0.211
50-55%	19	52.0%	26.3%	-25.7pp	0.260
55-60%	29	57.2%	34.5%	-22.7pp	0.283
60-65%	14	62.1%	28.6%	-33.5pp	0.312
65-70%	12	67.2%	58.3%	-8.8pp	0.254

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.