Developer builds Dixon-Coles football prediction model for eight under-modelled leagues

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A software developer built a football prediction model using the Dixon-Coles statistical method, applying it to eight leagues that rarely receive modelling attention, including MLS, Liga MX, and Colombia's Primera A. The model generates 1X2, Over/Under 2.5, Both Teams To Score, and correct-score probabilities from a single consistent framework. Dixon-Coles improves on basic Poisson models by correcting for the real-world tendency of matches to end 0-0 or 1-1 more frequently than independent probability predicts, using an extra parameter called rho. The developer fitted league-specific parameters using three seasons of historical match data sourced from ESPN's public scoreboard API, with results weighted by recency to reduce the influence of older fixtures. Key practical findings included that rho and home advantage vary meaningfully by league, and that querying the API by date range rather than single day avoids false data gaps for lower-activity leagues.

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