Which Football Leagues Are Most (and Least) Predictable?

Do some leagues really differ in predictability?

Yes — league predictability is not uniform, and 11Stat's own records show it. In our leakage-free walk-forward backtest, calibrated paper ROI on three sample leagues came out as follows:

Important honesty note: these are small samples. The spread from +2.8% to −19.5% illustrates dispersion — the fact that model accuracy and result variance are not the same everywhere — not a ranking you should trade on. A single quarter of results in one league cannot tell you where a durable edge lives. What it does tell us is that a one-size-fits-all probability model would be miscalibrated league by league, which is why 11Stat fits calibration per league.

Why do football leagues differ in predictability?

Several structural factors push a league toward or away from the favourite winning as the model expects:

• Competitiveness (talent gap). When a few clubs dominate, favourites convert more often and outcomes look more predictable. When quality is compressed across the table, upsets rise and dispersion grows.
• Scoring variance. Low-scoring, tight leagues produce more coin-flip results (draws, one-goal games), which inflate variance around goals markets like Over/Under and Both Teams To Score.
• Home advantage. The size and stability of home advantage differs by league and era; where it is large and steady, models fit better, where it has drifted, historical priors mislead.
• Data quality and sample depth. Leagues with deeper, cleaner match and odds histories calibrate more reliably; thin or noisy data widens uncertainty regardless of the underlying football.

Because these forces vary, the same engine can look well-tuned in one competition and shaky in another — the argument for per-league calibration rather than a single global curve.

What 11Stat's walk-forward backtest actually shows

Our headline evidence is a walk-forward, point-in-time backtest — meaning every prediction is scored using only information available before kick-off, so no future data leaks in. Over the window 22 Mar 2026 to 22 Jun 2026 (88 days) it analysed 2,271 matches and graded 2,974 picks. Only 603 of 2,271 matches (27%) had archived closing odds, so ROI is measured on that odds-covered subset.

Overall: hit-rate accuracy was +36.4% above chance, average odd 3.90, and paper ROI −13.1%. Every market was ROI-negative on this subset — Over/Under 2.5 and Double Chance were the least-negative and Over/Under 6.5 the worst:

These are simulated/paper metrics on a limited odds-covered subset; they describe past model behaviour and guarantee nothing about the future.

How 11Stat models and calibrates each league

Each read is a multi-engine consensus, not a single black box: a Poisson/Dixon-Coles goal model, an Elo/form strength engine, a bivariate/correlation engine, and a 10,000-run Monte Carlo simulation. Every prediction is accompanied by a model-agreement (N-of-M) score, per-league calibration, and a 0-10 confidence/uncertainty level.

Calibration matters because raw model outputs are rarely honest probabilities. We apply Platt scaling per market on real settled outcomes, so a stated 60% is designed to land near 60% in the long run. Fit sample sizes are published: global n=15,958; Match Result n=802; Over/Under 2.5, Over/Under 4.5, Both Teams To Score and Double Chance n=760 each; Over/Under 1.5 and Over/Under 3.5 n=1,200; team-goals n=4,560; total-goals n=3,800; cards n=736; corners n=740. Fitting per league is precisely how we absorb the dispersion described above instead of pretending it does not exist.

The honest thesis: calibrated, not market-beating

This is the part most platforms will not say plainly. 11Stat is well-calibrated — its stated probabilities track real-world frequencies — but it does not beat the closing betting line on ROI, and we publish that fact rather than hide it. The value we offer is transparent, calibrated probability, verifiable public records, and closing-line-value tracking, not a market-beating betting edge.

11Stat is a football data-analytics and education platform, not a betting or gambling service. Nothing here is betting advice, a tip, or a coupon. Every ROI and win-rate figure is a simulated/paper backtest quality metric only, and past performance is not indicative of future results. If you explore the platform, treat the probabilities as measured estimates with stated uncertainty — read them, do not wager on our word. Start with the 11Stat homepage.

Key terms defined

• Calibration. Adjusting model outputs so a stated probability matches the real long-run frequency (a set of 60% calls should win about 60% of the time).
• Walk-forward (point-in-time) backtest. Evaluating a model using only data available before each match, so no future information leaks into past predictions.
• Closing-line value (CLV). How a prediction's implied probability compares with the final pre-kick-off market price — a transparency benchmark, not a promise of profit.
• Brier score / ECE. Standard calibration and accuracy measures; the Brier score rewards sharp, correct probabilities, and Expected Calibration Error (ECE) measures the gap between stated and observed frequencies.
• Model agreement (N-of-M). How many of 11Stat's independent engines agree on a read, surfaced as a consensus score alongside a 0-10 confidence level.

Risk note: football outcomes are uncertain; all figures on this page are simulated/paper analytics and guarantee nothing.