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Nonlinear Modeling of European Football Scores Using Support Vector Machines

[journal article]

Markellos, Raphael Nicholas

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Abstract This paper 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 indicate 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 modeling 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.
Classification National Economy; Economic Sectors
Document language English
Publication Year 2008
Page/Pages p. 111-118
Journal Applied Economics, 40 (2008) 1
DOI http://dx.doi.org/10.1080/00036840701731546
ISSN 1466-4283
Status Postprint; reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)
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