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Bisecting for Selecting: Using a Laplacian Eigenmaps Clustering Approach to Create the New European Football Super League

Author

Listed:
  • Alexander John Bond

    (Carnegie School of Sport, Leeds Beckett University, Leeds LS6 3QQ, UK)

  • Clive B. Beggs

    (Carnegie School of Sport, Leeds Beckett University, Leeds LS6 3QQ, UK)

Abstract

Ranking sports teams generally relies on supervised techniques, requiring either prior knowledge or arbitrary metrics. In this paper, we offer a purely unsupervised technique. We apply this to operational decision-making, specifically, the controversial European Super League for association football, demonstrating how this approach can select dominant teams to form the new league. We first use random forest regression to select important variables predicting goal difference, which we use to calculate the Euclidian distances between teams. Creating a Laplacian eigenmap, we bisect the Fiedler vector to identify the natural clusters in five major European football leagues. Our results show how an unsupervised approach could identify four clusters based on five basic performance metrics: shots, shots on target, shots conceded, possession, and pass success. The top two clusters identify teams that dominate their respective leagues and are the best candidates to create the most competitive elite super league.

Suggested Citation

  • Alexander John Bond & Clive B. Beggs, 2023. "Bisecting for Selecting: Using a Laplacian Eigenmaps Clustering Approach to Create the New European Football Super League," Mathematics, MDPI, vol. 11(3), pages 1-17, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:720-:d:1053336
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    References listed on IDEAS

    as
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    4. Alexander John Bond & Francesco Addesa, 2019. "TV demand for the Italian Serie A: star power or competitive intensity?," Economics Bulletin, AccessEcon, vol. 39(3), pages 2110-2116.
    5. Clive B Beggs & Simon J Shepherd & Stacey Emmonds & Ben Jones, 2017. "A novel application of PageRank and user preference algorithms for assessing the relative performance of track athletes in competition," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-26, June.
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