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How does Footlab compute its football predictions?

For every match, Footlab estimates an expected number of goals for each team, then turns those into outcome probabilities with a bivariate Poisson model. The expected number combines six inputs: the recent strength of both teams measured over their last 180 days, a pool-anchored world Elo rating, the probable starting line-up, confirmed absences, home advantage, and match context such as a second leg or days of rest. The model reaches 53.9% correct 1X2 outcomes on a temporal hold-out, where random guessing between three outcomes would give 33.3%.

Model version: 2026-08. Last functional change: web verification of the probable line-up, 9 August 2026. Reference backtest: 16 July 2026, 25,424 matches.

What exactly does the model output for a match?

The model outputs a probability for each of the three outcomes (home win, draw, away win), the most likely scoreline, and the probability of a few derived markets computed on the same score matrix.

How is a team's strength measured?

A team's strength is estimated over its last 180 days of matches, with a weighting that halves the weight of a match every 180 days, corrected for the quality of the opponents faced and for the averages of its competition.

How can two teams that never meet be compared?

Each domestic league is a near-closed pool whose internal matches cancel out: without a correction, the best club in Romania and the best club in Ukraine end up with the same Elo. Footlab therefore adds a pool anchor to the club Elo, updated only by cross-pool matches.

Is the starting line-up taken into account?

Yes: since 9 August 2026, Footlab verifies the probable line-up on the web before each match and corrects its prediction based on the announced eleven, its experience and the individual ratings of its players.

Why are matches between different leagues handled separately?

Because domestic form barely transfers: measured on the backtest, it retains only 10% of its weight on a cross-league match. It is the most expensive lesson of the project, and it is encoded as such.

How often is the model's data refreshed?

Scores are followed minute by minute during matches, standings are recomputed hourly, and injuries as well as Elo ratings are resynchronised daily.

What are the model's parameters?

The eight parameters below are the ones that weigh most on the prediction. Each was set by sweeping values on the backtest, then confirmed on a later sample the model had never seen.

ParameterValueWhat it does
Form half-life180 daysA six-month-old match weighs half as much as a recent one.
Shrinkage5 matchesA rarely observed team is pulled back towards its competition average.
Expected-goals weight0.8Strength is estimated 80% on expected goals, 20% on actual goals.
Pool anchoring coefficient24Speed at which an entire league rises or falls in the world rating.
Home advantage (Elo)60 pointsBonus applied to the home team in the rating update.
Cross-league damping0.1Domestic form keeps only 10% of its weight outside its own league.
Totals damping0.8Corrects the overconfidence measured on extreme over-2.5-goals probabilities.
First-half goal share0.44Basis for half-time probabilities.

What does the model not do?

This list matters as much as the previous one. It holds two kinds of items: what Footlab rules out on principle, and what was implemented, measured, found useless, then left at zero in the code.

Statistical analysis based on Footlab data (Poisson model calibrated on 17,000+ matches). Not betting advice.

Footlab (footlab.ai) is an independent software product. It is unrelated to FOOTLAB World, the indoor football centre franchise, and unrelated to Footy Labs.

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