Brier score
0.207
Validation set · N=—
Lower is better · vs 0.222 uniform baseline
Model Track Record
MatchMind publishes how its calibrated football model actually performs against real outcomes. This page is public — no signup required — because trust in a probability model has to be earned with numbers, not promises.
Log-loss
1.034
Validation set · N=—
Lower is better · vs 1.099 uniform baseline
Calibration error (ECE)
—
Validation set
Lower is better
Model version
—
Active champion
Calibration — Reliability Diagram (home-win 1×2)
Need ≥ 50 pre-match evaluations to plot calibration. Currently 0 evaluations recorded.
When the live sample reaches the threshold this page will display the actual curve. Until then, the validation-set metrics above are the honest reference.
Each dot is a probability bin — x-position is the average predicted home-win probability, y-position is how often the home team actually won. The dashed diagonal is perfect calibration. Dots above = model is under-confident in that range; below = over-confident.
How to read this page
- Brier score measures the squared error of probabilistic predictions. Perfect = 0; predicting 1/3 each every time = 0.222. The model has to beat that baseline to be useful.
- Log-loss penalises confident wrong calls more heavily. Predicting 1/3 each gives log-loss = 1.099.
- ECE (Expected Calibration Error) measures how close stated probabilities are to observed frequencies. Lower means a stated 60% actually means 60%.
- Live vs validation: when we have fewer than 50 live pre-match evaluations, this page shows training-validation metrics. The card above tells you which source the numbers come from.
Analytical estimates only.The numbers on this page describe the model's historical performance — they do not guarantee future outcomes, and MatchMind does not provide betting tips, picks, or guaranteed predictions. Past calibration is the best evidence available for trusting probabilities, but it is not a promise.