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Nonlinear modelling of European football scores using support vector machines

Author

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  • Nikolaos Vlastakis
  • George Dotsis
  • Raphael Markellos

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.

Suggested Citation

  • Nikolaos Vlastakis & George Dotsis & Raphael Markellos, 2008. "Nonlinear modelling of European football scores using support vector machines," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 111-118.
    • Handle: RePEc:taf:applec:v:40:y:2008:i:1:p:111-118
    DOI: 10.1080/00036840701731546
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    References listed on IDEAS

    as
    1. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521710091, October.
    2. Nikolaos Vlastakis & George Dotsis & Raphael N. Markellos, 2009. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 426-444.
    3. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521883818.
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    4. S Lessmann & M-C Sung & J E V Johnson, 2011. "Towards a methodology for measuring the true degree of efficiency in a speculative market," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(12), pages 2120-2132, December.
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