Predicting Football Match Result Using Fusion-based Classification Models

Abstract

In recent decades, many researchers attempted to predict football match outcome. To forecast future match results, most papers relied on using in-game match statistics, such as number of shots on target, yellow cards, red cards, etc. In this paper, fusion-based classification model was constructed for future matches, using none of in-game statistics. The model used video games’ ratings of players and teams to help in prediction. Two types of fusion-based models, which are hierarchical model and ensemble model, were proposed in this paper. In the experiment, the proposed models were compared with different simple classification models in terms of accuracy using a dataset of English Premier League (EPL) season 2010/2011 to 2014/2015. Additionally, each model was also tested on the whole 2015/2016 EPL season as the selected season contains several unexpected results. Both proposed models yielded the accurate rates at 56.5332% and 56.8002%, which are higher than those of the other models.