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Comparative Analysis of Statistical and Machine Learning Methods for Classification of Match Outcomes in Association Football

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Proceedings of the 7th International Conference on the Applications of Science and Mathematics 2021

Part of the book series: Springer Proceedings in Physics ((SPPHY,volume 273))

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Abstract

Association football is one of the most popular sports in the world, having a large fan base that draws media and entertainment platforms to it. The three major difficulties to developing highly accurate match result prediction models, especially in prediction match outcomes, are data availability and quality [1], model assumptions [2], and testing various models and parameters [3]. The primary goal of the study is to identify the best model in predicting football match outcomes. The football dataset was obtained from the top 5 leagues in Euro. Exploratory data analysis had been conducted to better understand the dataset. Models used in predicting the football match outcomes include Logistic Regression, Artificial Neural Networks, and XGBoost. The predictive performance of three classification models was compared in terms of accuracy, precision and recall. The results showed that the Artificial Neural Networks achieved highest accuracy of 0.6788, followed by Logistic Regression (0.668) and XGBoost (0.654). These results are hoped to be used as benchmark results for future experiments in the area of football match classification.

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Acknowledgements

This research is supported by the Ministry of Higher Education (MOHE) through Fundamental Research Grant Scheme (FRGS/1/2020/ICT02/UTHM/02/4).

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Authors and Affiliations

  1. Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, 84600, Johor, Panchor, Malaysia

    Syazira Zulkifli, Aida Binti Mustapha & Shuhaida Ismail

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Parit Raja, Malaysia

    Nazim Razali

Authors
  1. Syazira Zulkifli
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  2. Aida Binti Mustapha
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  3. Shuhaida Ismail
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  4. Nazim Razali
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Corresponding author

Correspondence to Aida Binti Mustapha .

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Editors and Affiliations

  1. Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Aida Binti Mustapha

  2. Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Suhadir Shamsuddin

  3. Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Syed Zuhaib Haider Rizvi

  4. Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Saliza Binti Asman

  5. Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Siti Suhana Jamaian

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Zulkifli, S., Mustapha, A.B., Ismail, S., Razali, N. (2022). Comparative Analysis of Statistical and Machine Learning Methods for Classification of Match Outcomes in Association Football. In: Mustapha, A.B., Shamsuddin, S., Zuhaib Haider Rizvi, S., Asman, S.B., Jamaian, S.S. (eds) Proceedings of the 7th International Conference on the Applications of Science and Mathematics 2021. Springer Proceedings in Physics, vol 273. Springer, Singapore. https://doi.org/10.1007/978-981-16-8903-1_31

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