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

Ryan Beal; Stuart E. Middleton; Timothy J. Norman; Sarvapali D. Ramchurn

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arXiv · 2012.04380

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

Ryan Beal·Stuart E. Middleton·Timothy J. Norman·Sarvapali D. Ramchurn

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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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Ryan Beal, Stuart E. Middleton, Timothy J. Norman, Sarvapali D. Ramchurn. 2020-12-08. Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model. https://arxiv.org/abs/2012.04380

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