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Predicting the Outcomes of Football Matches Using Machine Learning Approach

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Informatics and Intelligent Applications (ICIIA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1547))

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Abstract

Predicting outcomes of football matches is among the rapid growing area of research due to the interest of large number of people, and the stochastic nature of the results. Many researches have been conducted to predict the outcomes of football matches. Statisticians predict the football match outcomes to showcase their skills whereas Operational researchers use the prediction to experiment with various effects of football tournament design. This research is aimed at determining feature and classifier combination that would provide the best English premier League football outcomes prediction accuracy. Despite the fact that feature and classifier combination might effects the football outcomes prediction accuracy, however, based on our knowledge, no other researches had considered to determine which possible feature and classifier would produce a good English Premier League football prediction accuracy. We proposed to use multiple machine learning classifiers and features combination to determine which feature and classifier combination would produce high football prediction accuracy. Experiments were conducted using various feature and classifier combinations including previous football match records of English premier league for two seasons. The results of all feature and classifier combinations were compared to determine the one that would achieve the highest accuracy. The result of the experiments show that using home team factor, away team, twenty two (22) first players from the two teams, and goal difference together with K-NN machine learning classifier achieved the highest accuracy of 83.95%. In addition, the result of the best feature and classifier combination was compared with the result of the existing English Premier League football prediction accuracy and found that our result achieved improvement over existing results.

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Author information

Authors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Usman Haruna & Ram Gopal Raj

  2. Department of Computer Science, Faculty of Science, Yusuf Maitama Sule University, Kano, Nigeria

    Usman Haruna & Murtala Mohammed

  3. Department of Information Technology, Faculty of Computer Science and Information Technology, Bayero University, Kano, 3011, Nigeria

    Jaafar Zubairu Maitama

Authors
  1. Usman Haruna
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  2. Jaafar Zubairu Maitama
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  3. Murtala Mohammed
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  4. Ram Gopal Raj
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Corresponding author

Correspondence to Usman Haruna .

Editor information

Editors and Affiliations

  1. Østfold University College, Halden, Norway

    Sanjay Misra

  2. Covenant University, Ota, Nigeria

    Jonathan Oluranti

  3. Silesian University of Technology, Gliwice, Poland

    Robertas Damaševičius

  4. Kaunas University of Technology, Kaunas, Lithuania

    Rytis Maskeliunas

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Haruna, U., Maitama, J.Z., Mohammed, M., Raj, R.G. (2022). Predicting the Outcomes of Football Matches Using Machine Learning Approach. In: Misra, S., Oluranti, J., Damaševičius, R., Maskeliunas, R. (eds) Informatics and Intelligent Applications. ICIIA 2021. Communications in Computer and Information Science, vol 1547. Springer, Cham. https://doi.org/10.1007/978-3-030-95630-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-95630-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95629-5

  • Online ISBN: 978-3-030-95630-1

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