If you're building your own handicapping model, evaluating its quality requires the same rigor as any analytical project. Gut feel isn't enough.
What Makes a Model Good
Calibration: When your model says Team A has a 60% chance of winning, does Team A actually win ~60% of the time? Track model predictions vs. actual outcomes.
Log loss / Brier score: Statistical measures of probability model accuracy that weight confident wrong predictions more severely than uncertain ones.
Closing line value: Does betting your model's output consistently beat the closing line? CLV is the gold standard test of model quality.
Common Model Mistakes
Overfitting: Your model was built on the same data it's being tested on. It fits historical patterns perfectly but fails on new data. Always use out-of-sample testing.
Ignoring market context: A model that doesn't incorporate current market prices will systematically recommend betting mispriced sides — but on the wrong side if the market has more information.
Not accounting for vig: A model might find 2% edges before juice, but -110 vig requires 2.4% gross edge just to break even.
The Simplest Test
Run 500+ model predictions. Track the CLV on all of them. If average CLV is consistently positive after 500 bets, you have something real.
[Oddible helps you benchmark model predictions vs. closing line →]
