Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model

Authors

  • Ryan Beal University of Southampton
  • Stuart E. Middleton University of Southampton
  • Timothy J. Norman University of Southampton
  • Sarvapali D. Ramchurn University of Southampton

Keywords:

Data Science, Applications, Sports Analytics, Natural Language Processing

Abstract

In this paper, we present a new application-focused benchmark dataset and results from a set of baseline Natural Language Processing and Machine Learning models for prediction of match outcomes for games of football (soccer). By doing so we give a baseline for the prediction accuracy that can be achieved exploiting both statistical match data and contextual articles from human sports journalists. Our dataset is focuses on a representative time-period over 6 seasons of the English Premier League, and includes newspaper match previews from The Guardian. The models presented in this paper achieve an accuracy of 63.18% showing a 6.9% boost on the traditional statistical methods.

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Published

2021-05-18

How to Cite

Beal, R., Middleton, S. E., Norman, T. J., & Ramchurn, S. D. (2021). Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15447-15451. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17815

Issue

Section

IAAI Technical Track on AI Best Practices, Challenge Problems, Training AI Users