Support Vector Machine–Based Prediction System for a Football Match Result

Abstract

Different techniques have been used to develop result prediction systems. In particular, football match result prediction systems have been developed with techniques such as artificial neural networks, naïve Bayesian system, k-nearest neighbor algorithms (k-nn), and others. The choice of any technique depends on the application domain as well as the feature sets. The priority of a system developer or designer in most cases is to obtain a high prediction accuracy. The objective of this study is to investigate the performance of a Support Vector Machine (SVM) with respect to the prediction of football matches. Gaussian combination kernel type is used to generate 79 support vectors at 100000 iterations. 16 example football match results (data sets) were trained to predict 15 matches. The findings showed 53.3% prediction accuracy, which is relatively low. Until proven otherwise by other studies, an SVM-based system (as devised here) is not good enough in this application domain.