Calibration: why a 65% model prediction must win 65% of the time
The difference between 'predicting well' and 'being calibrated'. Practical explanation of the concept that separates serious models from guessing.
If a model says "this team wins with 80% probability" and they win, is the model good? The correct answer: it depends.
Predicting vs. calibrating
A model can hit a lot but be poorly calibrated. Example: it says 90% for every favorite. Hits 70% of the time because favorites do win more. But the "90%" was really 70%. Overconfidence — disastrous for betting.
A well-calibrated model honors its numbers: the 65% it predicts must materialize as 65% hit rate in the long run. Says 70%, wins 70% of the time. Says 30%, wins 30%.
Why it matters for betting
Book odds imply a probability. Odds of 2.00 imply ~50%. To win long-term you need situations where real probability > implied probability. That's the edge. But an edge computed against a poorly calibrated probability is fiction.
How we calibrate at QuantFut
Without sharing proprietary details: we apply a calibration layer on top of the trained model so probabilities reflect real observed frequencies. That calibration is validated against closing odds from top books (the most efficient market signal) and re-tuned periodically.
Per-market calibration curve is in the Track Record. Empirical proof that 65% means 65%.