Incremental Learning for Football Match Outcomes Prediction.

Generating predictions for football match results is an expanding research area due to the commercial assets involved in the betting process. Traditionally, the results of the matches are predicted using statistical models verified by domain experts. Nowadays, this approach is challenged by the increasing amount of diverse football related information that need to be processed. In this paper, we propose an incremental learning method to predict the football match outcome category (home win, draw, away win) based on prior to the game publicly available information. The proposed framework is illustrated with data for the Portuguese first division football teams for 2017/2018 season. Factor Analysis was applied to extract most discriminating features which allowed gradual convergence of the prediction error to 32.4% after accumulation of about one third of the season games. Our approach outperforms traditional models in the gambling industry today and implies potential financial opportunities. The proposed prediction model is useful for researchers, football betting crowd, bookmakers, sport managers.