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Mining Player Ranking Dynamics in Team Sports

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10358))

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Abstract

The dynamics of players rankings play an important role in team sports. We use Kendall’s \(\tau \) and Spearman’s \(\rho \) distances between rankings to study player scoring ranking dynamics in the NBA over the full 2014 regular season. For each team, we study the distances between sequential games, noting the differences between the two distances. Additionally, we define the consistency of teams based on their ranking dynamics. Team consistency and winning percentage are compared. Finally, we use our findings to produce actionable results for sports managers.

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Acknowledgement

We would like to thank Charles Rohlf at stats.com for making their NBA dataset available to us. We would also like to thank Marc Pomplun for helpful suggestions.

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Authors and Affiliations

  1. Computer Science Department, University of Massachusetts Boston, Boston, USA

    Paul Fomenky & Dan A. Simovici

  2. Mathematics Department, University of Massachusetts Boston, Boston, USA

    Alfred Noel

Authors
  1. Paul Fomenky
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  2. Alfred Noel
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  3. Dan A. Simovici
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Corresponding author

Correspondence to Dan A. Simovici .

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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Sachsen, Germany

    Petra Perner

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Fomenky, P., Noel, A., Simovici, D.A. (2017). Mining Player Ranking Dynamics in Team Sports. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2017. Lecture Notes in Computer Science(), vol 10358. Springer, Cham. https://doi.org/10.1007/978-3-319-62416-7_24

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  • DOI: https://doi.org/10.1007/978-3-319-62416-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62415-0

  • Online ISBN: 978-3-319-62416-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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