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What Data Should Be Collected for a Good Handball Expected Goal model?

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Machine Learning and Data Mining for Sports Analytics (MLSA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2035))

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Abstract

Expected goal models (xG) are of great importance as they are the most accurate predictor of future performance of teams and players in the world of soccer. This metric can be modeled by machine learning, and the models developed consider an increasing number of attributes, which increases the cost of learning it. The use of xG is not widespread in handball, so the question of learning it for this sport arose, in particular which attributes are relevant for learning. Here, we used a wrapper approach to determine these relevant attributes and guide teams through the data collection stage.

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Notes

  1. 1.

    https://www.statsperform.com/opta-analytics/.

  2. 2.

    https://www.scisports.com/total-shots-ratio/.

  3. 3.

    https://en.wikipedia.org/wiki/Fenwick_(statistic).

  4. 4.

    https://en.wikipedia.org/wiki/Corsi_(statistic).

  5. 5.

    https://www.statsperform.com/opta/.

  6. 6.

    https://statsbomb.com/.

  7. 7.

    https://statsbomb.com/soccer-metrics/expected-goals-xg-explained/.

  8. 8.

    https://statsbomb.com/what-we-do/soccer-data/.

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Acknowledgements

This work was partially funded by the ANR and the Normandy Region as part of the HAISCoDe project.

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Authors and Affiliations

  1. Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC, 14000, Caen, France

    Alexis Mortelier & François Rioult

  2. National Institute of Education, Nanyang Technological University, Singapore, Singapore

    John Komar

Authors
  1. Alexis Mortelier
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  2. François Rioult
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  3. John Komar
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Correspondence to Alexis Mortelier .

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Editors and Affiliations

  1. Leuphana Universität Lüneburg, Lüneburg, Germany

    Ulf Brefeld

  2. KU Leuven, Leuven, Belgium

    Jesse Davis

  3. KU Leuven, Leuven, Belgium

    Jan Van Haaren

  4. University of Caen Normandy, Caen CEDEX 5, France

    Albrecht Zimmermann

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Mortelier, A., Rioult, F., Komar, J. (2024). What Data Should Be Collected for a Good Handball Expected Goal model?. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds) Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science, vol 2035. Springer, Cham. https://doi.org/10.1007/978-3-031-53833-9_10

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  • DOI: https://doi.org/10.1007/978-3-031-53833-9_10

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