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=laliga

Overall: N = 302 · mean predicted 44.5% · actual win rate 36.8% · gap -7.7pp · Brier 0.229 · log-loss 0.643

Calibration by predicted-probability bucket.
Predicted-prob bucket	N	Mean predicted	Actual win rate	Gap (actual − predicted)	Brier
<50%	186	36.8%	36.6%	-0.2pp	0.202
50-55%	50	52.1%	32.0%	-20.1pp	0.254
55-60%	38	57.0%	36.8%	-20.2pp	0.277
60-65%	14	61.9%	64.3%	+2.4pp	0.235
65-70%	14	68.0%	28.6%	-39.4pp	0.359

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.