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Prediction of Tiers in the Ranking of Ice Hockey Players

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Machine Learning and Data Mining for Sports Analytics (MLSA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1324))

Abstract

Many teams in the NHL utilize data analysis and employ data analysts. An important question for these analysts is to identify attributes and skills that may help predict the success of individual players. This study uses detailed player statistics from four seasons, player rankings from EA’s NHL video games, and six machine learning algorithms to find predictive models that can be used to identify and predict players’ ranking tier (top 10%, 25% and 50%). We also compare and contrast which attributes and skills best predict a player’s success, while accounting for differences in player positions (goalkeepers, defenders and forwards). When comparing the resulting models, the Bayesian classifiers performed best and had the best sensitivity. The tree-based models had the highest specificity, but had trouble classifying the top 10% tier players. In general, the models were best at classifying forwards, highlighting that many of the official metrics are focused on the offensive measures and that it is harder to use official performance metrics alone to differentiate between top tier players.

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Notes

  1. 1.

    See, e.g., https://en.wikipedia.org/wiki/Analytics_(ice_hockey).

  2. 2.

    https://www.hockey-reference.com/.

  3. 3.

    https://www.ea.com/games/nhl/nhl-20/ratings.

  4. 4.

    In the original data the forwards were categorized as left wing, right wing, center and wing.

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Authors and Affiliations

  1. Linköping University, Linköping, Sweden

    Timmy Lehmus Persson, Haris Kozlica, Niklas Carlsson & Patrick Lambrix

Authors
  1. Timmy Lehmus Persson
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  2. Haris Kozlica
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  3. Niklas Carlsson
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  4. Patrick Lambrix
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Corresponding author

Correspondence to Patrick Lambrix .

Editor information

Editors and Affiliations

  1. Leuphana University of Lüneburg, Lüneburg, Germany

    Ulf Brefeld

  2. Katholieke Universiteit Leuven, Leuven, Belgium

    Jesse Davis

  3. SciSports, Amersfoort, The Netherlands

    Jan Van Haaren

  4. Université de Caen Normandie, Caen, France

    Albrecht Zimmermann

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Lehmus Persson, T., Kozlica, H., Carlsson, N., Lambrix, P. (2020). Prediction of Tiers in the Ranking of Ice Hockey Players. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds) Machine Learning and Data Mining for Sports Analytics. MLSA 2020. Communications in Computer and Information Science, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-64912-8_8

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  • DOI: https://doi.org/10.1007/978-3-030-64912-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64911-1

  • Online ISBN: 978-3-030-64912-8

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