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=ucl

Overall: N = 158 · mean predicted 42.1% · actual win rate 43.7% · gap +1.5pp · Brier 0.242 · log-loss 0.673

Calibration by predicted-probability bucket.
Predicted-prob bucket	N	Mean predicted	Actual win rate	Gap (actual − predicted)	Brier
<50%	114	36.4%	43.0%	+6.6pp	0.229
50-55%	22	52.2%	50.0%	-2.2pp	0.252
55-60%	13	57.8%	53.8%	-4.0pp	0.255
60-65%	1	61.1%	0.0%	-61.1pp	0.373
65-70%	7	67.4%	14.3%	-53.1pp	0.409
75-80%	1	78.0%	100.0%	+22.0pp	0.048

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.