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=2,748 NCAAF N=1,323 TENNIS_WTA N=746 TENNIS_ATP N=652 MMA_MIXED_MARTIAL_ARTS N=518 NHL N=451 LALIGA N=274 AMERICANFOOTBALL_NFL N=271 SERIEA N=226 EPL N=224 LIGUE1 N=195 BUNDESLIGA N=179 UCL N=158 NFL_PRESEASON N=145 CRICKET_IPL N=27 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=seriea

Overall: N = 226 · mean predicted 45.5% · actual win rate 44.7% · gap -0.9pp · Brier 0.244 · log-loss 0.679

Calibration by predicted-probability bucket.
Predicted-prob bucket	N	Mean predicted	Actual win rate	Gap (actual − predicted)	Brier
<50%	140	38.1%	42.9%	+4.8pp	0.234
50-55%	33	52.8%	42.4%	-10.4pp	0.250
55-60%	29	57.1%	55.2%	-1.9pp	0.246
60-65%	9	61.5%	55.6%	-6.0pp	0.255
65-70%	15	67.3%	40.0%	-27.3pp	0.316

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.