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Nonlinear Modeling of European Football Scores Using Support Vector Machines

[journal article]

Markellos, Raphael Nicholas

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. ... view more

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.... view less

Classification
National Economy
Economic Sectors

Document language
English

Publication Year
2008

Page/Pages
p. 111-118

Journal
Applied Economics, 40 (2008) 1

DOI
https://doi.org/10.1080/00036840701731546

ISSN
1466-4283

Status
Postprint; peer reviewed

Licence
PEER Licence Agreement (applicable only to documents from PEER project)


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.