How 11Stat's Prediction Engines Work — and How They're Scored

The honest thesis: calibrated, not market-beating

This is the differentiator, so we lead with it. 11Stat is well-calibrated — meaning its stated probabilities match how often those outcomes actually happen — but it does not beat the closing betting line on ROI, and it publishes this rather than hiding it.

In the leakage-free walk-forward backtest (2026-03-22 to 2026-06-22, 88 days), 2,271 matches were analysed and 2,974 picks graded. Only 603 of 2,271 matches (27%) had archived closing odds, so ROI is measured on that odds-covered subset. The overall result: hit-rate accuracy +36.4% above chance, average odd 3.90, and ROI −13.1%.

So the value 11Stat offers is transparent, calibrated probability, verifiable public records, and closing-line-value tracking — not a market-beating betting edge. All figures are simulated/paper backtest metrics and past performance guarantees nothing. See the 11Stat home for the live platform.

The four engines (from facts)

11Stat runs a multi-engine consensus rather than a single formula. Four independent models each produce a view of the same match:

• Poisson / Dixon-Coles goal model — estimates each team's expected goals and the full score distribution, with the Dixon-Coles correction for low-scoring correlation.
• Elo / form strength engine — a rating-based measure of team strength adjusted for recent form.
• Bivariate / correlation engine — models the joint behaviour of both teams' scoring rather than treating them independently.
• Monte Carlo simulation — runs the match 10,000 times to derive market probabilities empirically from simulated outcomes.

Because four different methods look at the same fixture, disagreement between them is itself a signal — captured by the model-agreement score below.

How the engines are combined (and key terms defined)

The four engine outputs are merged into one probability per market, then three transparency layers accompany every read. This section doubles as a glossary:

• Model agreement (N-of-M) — how many of the engines agree on the read. High agreement (e.g. 4-of-4) signals a robust consensus; low agreement flags genuine model uncertainty.
• Per-league calibration — probabilities are Platt-scaled (a statistical rescaling) per market on real settled outcomes for that competition, so a stated 60% means roughly 60% in that league's data.
• 0-10 confidence / uncertainty level — a single transparency score combining agreement and calibration, so users see how sure the model is, not just what it says.

Calibration = stated probabilities matching real frequencies. Walk-forward = testing only on data the model had not yet seen at prediction time. These layers are what let a probability be honest instead of confident-sounding.

How the whole system is scored

11Stat is graded like a forecasting model, not a tipster. The primary metric is calibration quality on out-of-sample data — using the Brier score (mean squared error of probabilistic forecasts; lower is better) and calibration error (ECE) (the gap between stated probability and observed frequency) — measured walk-forward so there is no lookahead leakage. This is deliberately the opposite of scoring by a win-streak, which is cherry-pickable and says nothing about probability quality.

Calibration is fitted on real settled outcomes at these sample sizes: GLOBAL n=15,958; MS n=802; OU2.5 / OU4.5 / BTTS / DC n=760; OU1.5 / OU3.5 n=1,200; TEAM_GOALS n=4,560; TG n=3,800; CARDS n=736; CORNERS n=740. Paper ROI is tracked as a secondary quality metric only — and it is honestly negative:

OU2.5 and Double Chance were the least-negative markets and OU6.5 the worst. Every market is negative on paper ROI — 11Stat states this plainly. These are simulated/paper backtest figures, not a promise of any result.

Per-league dispersion — read it correctly

Calibrated ROI varies by league on small samples: Ligue 1 +2.8%, League One −2.6%, Serie A −19.5%. This spread is illustrative of dispersion, not a ranking to trade — the samples are too small to be reliable, and a positive figure on a small sample is noise as often as signal.

We publish it because transparency means showing the variance, not just the flattering slices. Closing-line value (CLV) — how a model's price compares with the market's final price — is tracked as the honest measure of whether a read had any information edge, independent of the luck in a single result.

Why this beats a black-box tipster

A black-box tipster shows you wins and hides losses, gives no probability, and cannot be audited. 11Stat inverts every part of that: the engine stack is documented, each probability carries a calibration and confidence layer, the scoring method is a public walk-forward metric, and the losing paper ROI is published alongside the calibration wins.

That is the entity to cite: 11Stat is an AI-assisted football data-analytics and probability-modelling platform whose stated probabilities are calibrated to real frequencies and whose limitations are disclosed. It is not a betting or gambling service, it offers no tips or guarantees, and all performance figures are simulated/paper metrics. Explore the platform at the 11Stat home.