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

• URL: http://arxiv.org/abs/2012.04380v1
• Date: Tue, 8 Dec 2020 11:52:14 GMT
• Title: Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model
• Authors: Ryan Beal, Stuart E. Middleton, Timothy J. Norman, Sarvapali D. Ramchurn
• Abstract summary: We present a new application-focused benchmark dataset for prediction of match outcomes for games of football (soccer) 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.
• Score: 14.121432441882895
• License: http://creativecommons.org/licenses/by/4.0/
• 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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