The one model on the site that is learned rather than hand-set: a multinomial logistic regression trained on six past seasons of results, fed only things known before kick-off. It reads each side's rolling form and outputs home/draw/away probabilities.
How it works
For every team and every game, build a running average over the team's last 25 games (home and away mixed) of: goals for and against, expected goals for and against, and points per game. Five numbers per side; last season's games seed the window in August, and a short history is blended towards the league mean so two games cannot pass for form. (Shots and corners were dropped once xG was available - on the holdout, goals + xG + points beat the shot-based set, and adding xG on top of shots was worse than either.)
A fixture's feature row is the home side's nine numbers, the away side's nine, and which division it is. Nothing from the game itself - never its own shots or half-time score.
Train a multinomial logistic regression (three classes: H, D, A) on every played game from the previous six seasons across all five divisions, about 8,000 games, skipping each team's first three games of a season. Features are mean-imputed (xG exists from 2024-25 for the EFL and Premier League, from 2025-26 for the National League) and standardised; light L2 regularisation.
Predict: the model returns three probabilities for each fixture, shown as home:draw:away percentages. It is retrained from scratch every morning as part of the pipeline.
weights those against each other plus the division's baseline home advantage
Output
58:24:18 → home win
How BetChair calls it
The largest of the three percentages is the vote. Held out on the whole of 2025-26 (trained on the five seasons before it), the favourite was right 47.7% of the time with a log-loss of 1.053 - better than always backing the home side (~43%), short of the bookmakers' favourite (~50%). Honest, not magic.
Where it shines
Learned from data rather than thresholds someone picked; calibrated probabilities.
Uses expected goals as well as goals - a side creating chances but not scoring shows up here before it shows in results.
Carries form across seasons and divisions, so promoted and relegated clubs are not blank slates.
Where it falls down
Rolling averages are a blunt instrument; it knows nothing about opponents' strength beyond what shows in the numbers.
National League xG only exists from 2025-26, so its predictions lean more on goals.
Retrained daily on the same history, so it will not change much within a season - the features move, the weights barely do.