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Using social networks to improve team transition prediction in professional sports

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  • Emily J Evans
  • Rebecca Jones
  • Joseph Leung
  • Benjamin Z Webb

Abstract

We examine whether social data can be used to predict how members of Major League Baseball (MLB) and members of the National Basketball Association (NBA) transition between teams during their career. We find that incorporating social data into various machine learning algorithms substantially improves the algorithms’ ability to correctly determine these transitions in the NBA but only marginally in MLB. We also measure the extent to which player performance and team fitness data can be used to predict transitions between teams. This data, however, only slightly improves our predictions for players for both basketball and baseball players. We also consider whether social, performance, and team fitness data can be used to infer past transitions. Here we find that social data significantly improves our inference accuracy in both the NBA and MLB but player performance and team fitness data again does little to improve this score.

Suggested Citation

  • Emily J Evans & Rebecca Jones & Joseph Leung & Benjamin Z Webb, 2022. "Using social networks to improve team transition prediction in professional sports," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-23, June.
    • Handle: RePEc:plo:pone00:0268619
    DOI: 10.1371/journal.pone.0268619
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    References listed on IDEAS

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    4. Peter Arcidiacono & Josh Kinsler & Joseph Price, 2017. "Productivity Spillovers in Team Production: Evidence from Professional Basketball," Journal of Labor Economics, University of Chicago Press, vol. 35(1), pages 191-225.
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