Football Predictions Based on a Fuzzy Model with Genetic and Neural Tuning
Cybernetics and Systems Analysis
Predicting football results using Bayesian nets and other machine learning techniques
Knowledge-Based Systems
Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks
IEEE Transactions on Knowledge and Data Engineering
A compound framework for sports results prediction: A football case study
Knowledge-Based Systems
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A Bayesian network is a graphical probabilistic model that represents the conditional dependencies among uncertain variables, which can be both objective and subjective. We present a Bayesian network model for forecasting Association Football matches in which the subjective variables represent the factors that are important for prediction but which historical data fails to capture. The model (pi-football) was used to generate forecasts about the outcomes of the English Premier League (EPL) matches during season 2010/11 (but is easily extended to any football league). Forecasts were published online prior to the start of each match. We show that:(a)using an appropriate measure of forecast accuracy, the subjective information improved the model such that posterior forecasts were on par with bookmakers' performance; (b)using a standard profitability measure with discrepancy levels at =5%, the model generates profit under maximum, mean, and common bookmakers' odds, even allowing for the bookmakers' built-in profit margin. Hence, compared with other published football forecast models, pi-football not only appears to be exceptionally accurate, but it can also be used to 'beat the bookies'.