Model calibration measures whether predicted probabilities match real-world frequencies: a well-calibrated model that says “60% chance” will see that outcome happen about 60% of the time over many such predictions. It is the truest test of a prediction model’s honesty.
Calibration vs Accuracy
A single “accuracy” percentage is easy to inflate and often just reflects how predictable a league is. Calibration is far harder to fake: it checks every probability band across every market. A model can look accurate yet be badly calibrated (overconfident) — which is exactly what costs bettors money.
The Reliability Curve
Calibration is shown as a reliability diagram: predicted probability on the x-axis, the rate the outcome actually occurred on the y-axis. A perfectly calibrated model sits on the diagonal, and the distance from it — the mean calibration error — is the headline number.
How TACTIX Applies This
TACTIX leads with calibration rather than a headline accuracy figure — it is calibrated to within about ±1.7% across roughly 18 million settled predictions, and the curve updates live. See the live model scoreboard and the full by-market track record.
Frequently Asked Questions
Why is calibration better than accuracy?
Accuracy can be inflated by predictable outcomes; calibration tests whether the stated probabilities are honest across all markets, which is what actually matters for decisions.
What is a good calibration error?
Lower is better. A mean calibration error of a couple of percent measured across millions of predictions is excellent.
18+. Educational information, not betting advice. Please gamble responsibly — BeGambleAware.org.
This post is generated from TACTIX’s match data by our software and checked automatically. How it works →
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