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Improving the Expected Goal Value in Football Using Multilayer Perceptron Networks

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Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1863))

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Abstract

The development of big data and machine learning is having a special boost in the field of innovation in sports. Football is the most popular sport in Europe with millions of players and billions of euros invested. Currently, machine learning applications in football are focused on video analysis to events detection (tracking of players, statistics matches, scouting, ...), injuries evaluation and prediction, among others. The xG metric (Expected Goals) determines the probability that a shot will result in a goal and is often displayed on screen during the most important football matches in the world. In this paper we present a new model to obtain more accurate predictions of the probability of a shot becoming a goal. The model is based on a multi-layer perceptron neural network (MLP), which allows the interaction of different variables and improves the model’s performance. Our proposal includes an evaluation of the quality and a comparison with the xG provided by statsbomb, one of the most important football data providers of the world. The results show that our model outperforms the quality of the expectations provided by statsbomb. Specifically, it is clearly better in their capability to detect actual positive cases (goals).

This work has been supported by the Spanish MINECO/FEDER project AwESOMe (PID2021-122215NB-C31) and the Region of Madrid project FORTE-CM (S2018/TCS-4314), co-funded by EIE Funds of the European Union.

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Notes

  1. 1.

    Not all xG models take into account the same factors. We refer in this work to the xG model given by Statsbomb [25], which uses more contextual events and better quality data than any other provider to accurately measure the quality of chances.

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Authors and Affiliations

  1. Design and Testing of Reliable Systems research group, Universidad Complutense de Madrid, Madrid, Spain

    Manuel Méndez, Carlos Montero & Manuel Núñez

Authors
  1. Manuel Méndez
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  2. Carlos Montero
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  3. Manuel Núñez
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Corresponding author

Correspondence to Manuel Núñez .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Siridech Boonsang

  3. Iwate Prefectural University, Iwate, Japan

    Hamido Fujita

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  6. King Mongkut's Institute of Technology, Ladkrabang, Thailand

    Kitsuchart Pasupa

  7. Malaysia Japan International Institute of Technology, Kuala Lumpur, Malaysia

    Ali Selamat

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Méndez, M., Montero, C., Núñez, M. (2023). Improving the Expected Goal Value in Football Using Multilayer Perceptron Networks. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_29

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  • DOI: https://doi.org/10.1007/978-3-031-42430-4_29

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  • Online ISBN: 978-3-031-42430-4

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