Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms

Peter Hassard; Dermot Kerr

doi:10.1109/ISSC61953.2024.10603147

Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms

Peter Hassard
, Dermot Kerr

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

83631 Downloads (Pure)

Abstract

This paper demonstrates how supervised machine learning algorithms can be leveraged to predict match outcomes from 2015/16 football season for matches in the top professional leagues of 5 countries (Spain - La Liga, England - Premier League, Italy - Serie A, Germany - 1. Bundesliga and France - Ligue 1). In the summer of 2023 StatsBomb released this free data; once important features on game play activity are generated and processed. A clear correlation between the match outcome and the location of a team's events on the pitch will be shown, moreover the ability to predict a match outcome based on the team's previous matches, this was done using their mean values for relevant features. For optimal prediction levels, the machine learning models in this paper had their hyperparameters selectively adjusted.

Original language	English
Title of host publication	Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024
Editors	Huiru Zheng, Ian Cleland, Adrian Moore, Haiying Wang, David Glass, Joe Rafferty, Raymond Bond, Jonathan Wallace
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9798350352986
DOIs	https://doi.org/10.1109/ISSC61953.2024.10603147
Publication status	Published (in print/issue) - 14 Jun 2024
Event	35th Irish Systems and Signals Conference, ISSC 2024 - Belfast, United Kingdom Duration: 13 Jun 2024 → 14 Jun 2024

Publication series

Name	Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024

Conference

Conference	35th Irish Systems and Signals Conference, ISSC 2024
Country/Territory	United Kingdom
City	Belfast
Period	13/06/24 → 14/06/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Machine Learning
StatsBomb
XGBoost

Access to Document

10.1109/ISSC61953.2024.10603147

Predicting_football_match_outcomesAuthor Accepted Manuscript, 1.44 MB
2024156785Final published version, 601 KB

Cite this

Hassard, P., & Kerr, D. (2024). Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms. In H. Zheng, I. Cleland, A. Moore, H. Wang, D. Glass, J. Rafferty, R. Bond, & J. Wallace (Eds.), Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024 (Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSC61953.2024.10603147

Hassard, Peter ; Kerr, Dermot. / Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms. Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024. editor / Huiru Zheng ; Ian Cleland ; Adrian Moore ; Haiying Wang ; David Glass ; Joe Rafferty ; Raymond Bond ; Jonathan Wallace. Institute of Electrical and Electronics Engineers Inc., 2024. (Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024).

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title = "Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms",

abstract = "This paper demonstrates how supervised machine learning algorithms can be leveraged to predict match outcomes from 2015/16 football season for matches in the top professional leagues of 5 countries (Spain - La Liga, England - Premier League, Italy - Serie A, Germany - 1. Bundesliga and France - Ligue 1). In the summer of 2023 StatsBomb released this free data; once important features on game play activity are generated and processed. A clear correlation between the match outcome and the location of a team's events on the pitch will be shown, moreover the ability to predict a match outcome based on the team's previous matches, this was done using their mean values for relevant features. For optimal prediction levels, the machine learning models in this paper had their hyperparameters selectively adjusted.",

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year = "2024",

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Hassard, P & Kerr, D 2024, Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms. in H Zheng, I Cleland, A Moore, H Wang, D Glass, J Rafferty, R Bond & J Wallace (eds), Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024. Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024, Institute of Electrical and Electronics Engineers Inc., 35th Irish Systems and Signals Conference, ISSC 2024, Belfast, United Kingdom, 13/06/24. https://doi.org/10.1109/ISSC61953.2024.10603147

Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms. / Hassard, Peter; Kerr, Dermot.
Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024. ed. / Huiru Zheng; Ian Cleland; Adrian Moore; Haiying Wang; David Glass; Joe Rafferty; Raymond Bond; Jonathan Wallace. Institute of Electrical and Electronics Engineers Inc., 2024. (Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kerr, Dermot

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N2 - This paper demonstrates how supervised machine learning algorithms can be leveraged to predict match outcomes from 2015/16 football season for matches in the top professional leagues of 5 countries (Spain - La Liga, England - Premier League, Italy - Serie A, Germany - 1. Bundesliga and France - Ligue 1). In the summer of 2023 StatsBomb released this free data; once important features on game play activity are generated and processed. A clear correlation between the match outcome and the location of a team's events on the pitch will be shown, moreover the ability to predict a match outcome based on the team's previous matches, this was done using their mean values for relevant features. For optimal prediction levels, the machine learning models in this paper had their hyperparameters selectively adjusted.

AB - This paper demonstrates how supervised machine learning algorithms can be leveraged to predict match outcomes from 2015/16 football season for matches in the top professional leagues of 5 countries (Spain - La Liga, England - Premier League, Italy - Serie A, Germany - 1. Bundesliga and France - Ligue 1). In the summer of 2023 StatsBomb released this free data; once important features on game play activity are generated and processed. A clear correlation between the match outcome and the location of a team's events on the pitch will be shown, moreover the ability to predict a match outcome based on the team's previous matches, this was done using their mean values for relevant features. For optimal prediction levels, the machine learning models in this paper had their hyperparameters selectively adjusted.

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KW - StatsBomb

KW - XGBoost

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BT - Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024

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A2 - Wang, Haiying

A2 - Glass, David

A2 - Rafferty, Joe

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Hassard P, Kerr D. Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms. In Zheng H, Cleland I, Moore A, Wang H, Glass D, Rafferty J, Bond R, Wallace J, editors, Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024. Institute of Electrical and Electronics Engineers Inc. 2024. (Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024). doi: 10.1109/ISSC61953.2024.10603147

Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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