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Estimating an NBA player’s impact on his team’s chances of winning

Author

Listed:
  • Deshpande Sameer K.

    (The Wharton School, University of Pennsylvania – Statistics, 434 Jon M. Huntsman Hall 3730 Walnut St., Philadelphia, Pennsylvania 19104, USA)

  • Jensen Shane T.

    (The Wharton School, University of Pennsylvania – Statistics, Philadelphia, Pennsylvania, USA)

Abstract

Traditional NBA player evaluation metrics are based on scoring differential or some pace-adjusted linear combination of box score statistics like points, rebounds, assists, etc. These measures treat performances with the outcome of the game still in question (e.g. tie score with five minutes left) in exactly the same way as they treat performances with the outcome virtually decided (e.g. when one team leads by 30 points with one minute left). Because they ignore the context in which players perform, these measures can result in misleading estimates of how players help their teams win. We instead use a win probability framework for evaluating the impact NBA players have on their teams’ chances of winning. We propose a Bayesian linear regression model to estimate an individual player’s impact, after controlling for the other players on the court. We introduce several posterior summaries to derive rank-orderings of players within their team and across the league. This allows us to identify highly paid players with low impact relative to their teammates, as well as players whose high impact is not captured by existing metrics.

Suggested Citation

  • Deshpande Sameer K. & Jensen Shane T., 2016. "Estimating an NBA player’s impact on his team’s chances of winning," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(2), pages 51-72, June.
    • Handle: RePEc:bpj:jqsprt:v:12:y:2016:i:2:p:51-72:n:2
    DOI: 10.1515/jqas-2015-0027
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    Citations

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    Cited by:

    1. Sabin R. Paul, 2021. "Estimating player value in American football using plus–minus models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(4), pages 313-364, December.
    2. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    3. Song, Kai & Shi, Jian, 2020. "A gamma process based in-play prediction model for National Basketball Association games," European Journal of Operational Research, Elsevier, vol. 283(2), pages 706-713.
    4. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.

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