How the Dixon-Coles football prediction model works
Betsfiled trains a separate model for each competition using completed league matches. Provider-specific team names are normalised to stable canonical keys, so free historical API-Football results can be matched to current football-data.org fixtures. Every club receives an attacking strength and a defensive strength. A home-advantage term and the competition scoring level turn those strengths into expected home and away goals.
Recent matches matter more
Results are exponentially weighted with a default time-decay value of 0.0019. A match roughly one year old therefore carries about half the influence of a new match. Older results remain useful, but they cannot dominate a team that has changed.
Why Dixon-Coles is different from standard Poisson
Football contains more low-scoring dependence than two fully independent Poisson variables describe. The fitted rho correction adjusts 0–0, 0–1, 1–0 and 1–1 before the score matrix is normalised. From that one coherent matrix we calculate correct score, home-draw-away, BTTS, over/under and double-chance estimates.
Regularisation and promoted teams
Ridge regularisation pulls teams with limited evidence toward the league average. A club without enough usable history is not assigned an invented strong rating; the system falls back to the simpler venue model or withholds the forecast.
Market information
When genuine pre-match 1X2 odds are captured, their margin can be removed and the resulting probabilities compared with the model. A blend is allowed only from timestamped prices. Betsfiled does not reconstruct or fabricate historical odds.
Testing without looking into the future
A proper walk-forward test trains only on matches available before each prediction window. Log loss, Brier score and calibration are evaluated on later matches. Hyperparameters are selected on a validation period and the final report must use a separate holdout period. Synthetic tests verify the code; they are not advertised as real forecasting performance.
What the model cannot promise
No football prediction algorithm guarantees a correct score or profit. Line-ups, injuries, tactical changes, weather and market movement can add information not present in the historical results. Every forecast remains an estimate, and losing selections stay in the public results archive.