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=3,353 TENNIS_WTA N=753 TENNIS_ATP N=748 MMA_MIXED_MARTIAL_ARTS N=407 LIGUE1 N=89 NFL_PRESEASON N=65 NBA N=42 NHL N=33 LALIGA N=26 SERIEA N=19 EPL N=15 UCL N=13 NCAAB N=11 CRICKET_IPL N=6 BUNDESLIGA N=3

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=ncaab

Overall: N = 11 · mean predicted 60.1% · actual win rate 45.5% · gap -14.6pp · Brier 0.225 · log-loss 0.638

Calibration by predicted-probability bucket.
Predicted-prob bucket	N	Mean predicted	Actual win rate	Gap (actual − predicted)	Brier
50-55%	3	52.8%	0.0%	-52.8pp	0.279
55-60%	4	57.6%	25.0%	-32.6pp	0.305
60-65%	1	60.7%	100.0%	+39.3pp	0.155
65-70%	2	68.6%	100.0%	+31.4pp	0.099
70-75%	1	74.6%	100.0%	+25.4pp	0.064

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.