Autors: Domingues, J., Lopes, B., Mihaylova, P. T., Georgieva, P.
Title: Incremental Learning for Football Match Outcomes Prediction
Keywords: Domain experts, Football betting, Incremental learning, Outc

Abstract: 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 oppor.

References

    Issue

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, Madrid, Spain, vol. 11868 LNCS, pp. 217-228, 2019, Switzerland, Springer Nature, DOI 10.1007/978-3-030-31321-0_19

    Copyright Springer Nature Switzerland AG.

    Вид: пленарен доклад в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus