Nonlinear modelling of European football scores using support vector machines

Nikolaos Vlastakis, George Dotsis () and Raphael Markellos

Applied Economics, 2008, vol. 40, issue 1, 111-118

Abstract: This article explores the linear and nonlinear forecastability of European football match scores using IX2 and Asian Handicap odds data from the English Premier league. To this end, we compare the performance of a Poisson count regression to that of a nonparametric Support Vector Machine (SVM) model. Our descriptive analysis of the odds and match outcomes indicates that these variables are strongly interrelated in a nonlinear fashion. An interesting finding is that the size of the Asian Handicap appears to be a significant predictor of both home and away team scores. The modelling results show that while the SVM is only marginally superior on the basis of statistical criteria, it manages to produce out-of-sample forecasts with much higher economic significance.

Date: 2008
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DOI: 10.1080/00036840701731546

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