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

AAAI 2021 pp. 15447-15451

doi:10.1609/AAAI.V35I17.17815 /aaai/2021/beal2021aaai-combining/

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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Cite

Text

Beal et al. "Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I17.17815

Markdown

[Beal et al. "Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/beal2021aaai-combining/) doi:10.1609/AAAI.V35I17.17815

BibTeX

@inproceedings{beal2021aaai-combining,
  title     = {{Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model}},
  author    = {Beal, Ryan and Middleton, Stuart E. and Norman, Timothy J. and Ramchurn, Sarvapali D.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15447-15451},
  doi       = {10.1609/AAAI.V35I17.17815},
  url       = {https://mlanthology.org/aaai/2021/beal2021aaai-combining/}
}