A Bayesian approach for major European football league match prediction

This paper presents a Bayesian Approach for Major European Football League match prediction. In this study, four variants of Bayesian approaches are investigated to observe the impact of different structural learning algorithms within the family of Bayesian Network which are Naive Bayes (NB), Tree Augmented Naive Bayes (TAN) and two General Bayesian Networks (GBN); K2 algorithm with BDeu scoring function (GBN-K2) and Hill Climbing algorithm with MDL scoring function (GBNHC). The predictive performance of all Bayesian approaches is evaluated and compared based on football match results from five major European Football League consisting of three complete seasons of 1,140 matches. The results showed that GBN-HC gained 92.01% of accuracy while GBN-K2 and TAN produced comparable results with 91.86% and 91.94% accuracy, respectively. The lowest result was produced by NB, with only 72.78% accuracy. The results suggest that TAN requires further exploration in football prediction with its ability to cater the minimal dependency among attributes in a small-sized dataset.