Nonlinear Modeling of European Football Scores
Using Support Vector Machines

Markellos, Raphael Nicholas

Bibtex export

@article{ Markellos2008,
 title = {Nonlinear Modeling of European Football Scores
Using Support Vector Machines},
 author = {Markellos, Raphael Nicholas},
 journal = {Applied Economics},
 number = {1},
 pages = {111-118},
 volume = {40},
 year = {2008},
 issn = {1466-4283},
 doi = {https://doi.org/10.1080/00036840701731546},
 urn = {https://nbn-resolving.org/urn:nbn:de:0168-ssoar-242840},
 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.},
}