Machine learning-based football match prediction system

Tianyou Wang; Zheng Zhang

doi:10.54254/2755-2721/92/20241749

Research Article

Open access

Published on 9 October 2024

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Wang,T.;Zhang,Z. (2024). Machine learning-based football match prediction system. Applied and Computational Engineering,92,181-186.

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Machine learning-based football match prediction system

Tianyou Wang ¹, Zheng Zhang *^,2,

¹ Department of Statistics and Data Science, Faculty of Science and Technology, BNU-HKBU United International College, Zhuhai, China
² Department of Statistics and Data Science, Faculty of Science and Technology, BNU-HKBU United International College, Zhuhai, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/92/20241749

Abstract

This study develops a machine learning-based system to predict English Premier League (EPL) outcomes, employing models such as Principal Component Analysis (PCA), K-Nearest Neighbors (KNN), Random Forests, and Support Vector Machines (SVM). The analysis covered a large dataset of matches, with the data normalized to ensure consistency and accuracy across models. Among the methods used, Random Forests showed the most robust performance in predicting match outcomes, particularly in forecasting wins and losses. However, both Random Forests and SVM encountered difficulties in accurately predicting draws, which points to areas where further refinement is needed. The prediction probabilities largely fell within a specific range, indicating the models' ability to identify patterns, but significant overfitting was observed in the models. This overfitting suggests that while the models performed well on the training data, they struggled to generalize to new, unseen data, highlighting the importance of implementing more effective regularization techniques to prevent overfitting and improve the models' overall predictive accuracy in real-world scenarios.

Keywords

Machine Learning Techniques, Football Data Analytics, Predictive Modeling, Team Performance Metrics

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References

[1]. E. G. Dunning, J. A. Maguire, and R. E. Pearton, The Sports Process: A Comparative and Developmental Approach. Champaign, IL: Human Kinetics Publishers, 1993.

[2]. E. Ong and A. Flitman, “Using neural networks to predict binary outcomes,” in Proc. 1997 IEEE Int. Conf. on Intelligent Processing Systems (Cat. No. 97TH8335), vol. 1, IEEE, 1997, pp. 427–431.

[3]. J. Hucaljuk and A. Rakipović, “Predicting football scores using machine learning techniques,” in Proc. 2011 34th Int. Convention MIPRO, IEEE, 2011, pp. 1623–1627.

[4]. A. Joseph, N. E. Fenton, and M. Neil, “Predicting football results using Bayesian nets and other machine learning techniques,” Knowledge-Based Systems, vol. 19, no. 7, pp. 544–553, 2006.

[5]. J. Gomes, F. Portela, and M. F. Santos, “Decision support system for predicting football game result,” in Computers-19th Int. Conf. on Circuits, Systems, Communications and Computers-Intelligent Systems and Applications Special Sessions, vol. 32, 2015, pp. 348–353.

[6]. F. Rodrigues and Â. Pinto, “Prediction of football match results with machine learning,” Procedia Computer Science, vol. 204, pp. 463–470, 2022.

Cite this article

Wang,T.;Zhang,Z. (2024). Machine learning-based football match prediction system. Applied and Computational Engineering,92,181-186.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://2024.confcds.org/

ISBN：978-1-83558-595-5(Print) / 978-1-83558-596-2(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.92

ISSN：2755-2721(Print) / 2755-273X(Online)

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