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=seriea

Overall: N = 276 · mean predicted 45.2% · actual win rate 43.1% · gap -2.0pp · Brier 0.238 · log-loss 0.665

Calibration by predicted-probability bucket.
Predicted-prob bucket	N	Mean predicted	Actual win rate	Gap (actual − predicted)	Brier
<50%	172	37.9%	40.1%	+2.2pp	0.227
50-55%	46	52.6%	43.5%	-9.1pp	0.250
55-60%	32	57.1%	53.1%	-4.0pp	0.249
60-65%	9	61.5%	55.6%	-6.0pp	0.255
65-70%	16	67.2%	43.8%	-23.5pp	0.303
70-75%	1	70.7%	100.0%	+29.3pp	0.086

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.