EdgeK.ai
Calibration
Calibration

Probability vs Reality

When the model says 60%, does the bet actually win 60% of the time? Below: each point is a decile bucket. Perfect calibration sits on the diagonal.
ECE
13.17%
weighted bucket gap · <5% is good
Calibration Slope
0.85
model is over-confident
Avg Confidence
53.1%
aggressive (marginal edges)
Settled Bets
2665
Brier 0.2327 · <0.25 beats coin flip
Model vs Market
Our model's probability vs the market's closing line probability
Each dot is a settled bet; the market prob is de-vigged (the book's juice removed). Recolor to see where our disagreements with the market actually paid off.
Each dot is one settled bet (1739with a consensus close): x = our model's probability, y = the market's de-vigged consensus closing probability. Click a pill (or drag the line sliders) to isolate a group — the rest grey out. Our model is closer to the actual result on 42% of bets · Brier 0.241 (model) vs 0.220 (market), lower = better predictor.
Model vs Market vs Reality
By line — what we say, what the market says (de-vigged), what happens
Win probability at each line. "Model over" is how far our number sits above the actual rate — our overconfidence at that line.
LineBetsModel saysMarket saysActualModel over
2.53578.1%63.7%57.1%+21.0
3.526266.9%53.4%51.5%+15.4
4.547860.1%48.6%47.5%+12.7
5.542954.8%42.7%42.2%+12.6
6.530447.4%35.2%36.5%+10.9
7.515142.9%30.6%29.1%+13.8
8.55732.2%20.7%15.8%+16.4
9.51824.6%15.2%5.6%+19.1
Model vs Market vs Reality
By pitcher class — where the overconfidence concentrates
Win probability by pitcher talent tier (strikeouts per start). "Model over" is how far our number sits above reality for that class — the model runs hottest on mid-tier arms, not the aces.
ClassBetsModel saysMarket saysActualModel over
Scrub< 3.98 K17953.9%44.0%35.2%+18.7
Average3.98–4.64 K38051.6%42.7%40.5%+11.0
Good4.64–5.36 K66851.2%43.0%35.9%+15.3
Elite5.36+ K85856.1%43.2%44.1%+12.0
Team Strikeout Rate
Model K% vs actual K%, by opponent (2026)
All 2139 starts this season, not just bets — where the model misreads how often a lineup strikes out.
model over-expects Ks (overs trap / unders value)model under-expects Ks (overs value)
Reliability Diagram
Predicted probability → actual win rate
Bets per bucket