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Using Box-Scores to Determine a Position's Contribution to Winning Basketball Games

Author

Listed:
  • Page Garritt L

    (Iowa State University)

  • Fellingham Gilbert W

    (Brigham Young University)

  • Reese C. Shane

    (Brigham Young University)

Abstract

While it is generally recognized that the relative importance of different skills is not constant across different positions on a basketball team, quantification of the differences has not been well studied. 1163 box scores from games in the National Basketball Association during the 1996-97 season were used to study the relationship of skill performance by position and game outcome as measured by point differentials. A hierarchical Bayesian model was fit with individual players viewed as a draw from a population of players playing a particular position: point guard, shooting guard, small forward, power forward, center, and bench. Posterior distributions for parameters describing position characteristics were examined to discover the relative importance of various skills as quantified in box scores across the positions. Results were consistent with expectations, although defensive rebounds from both point and shooting guards were found to be quite important.

Suggested Citation

  • Page Garritt L & Fellingham Gilbert W & Reese C. Shane, 2007. "Using Box-Scores to Determine a Position's Contribution to Winning Basketball Games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(4), pages 1-18, October.
    • Handle: RePEc:bpj:jqsprt:v:3:y:2007:i:4:n:1
    DOI: 10.2202/1559-0410.1033
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    References listed on IDEAS

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    1. David J. Berri, 1999. "Who is 'most valuable'? Measuring the player's production of wins in the National Basketball Association," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 411-427.
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    Cited by:

    1. Page Garritt L. & Barney Bradley J. & McGuire Aaron T., 2013. "Effect of position, usage rate, and per game minutes played on NBA player production curves," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(4), pages 337-345, December.
    2. Manner Hans, 2016. "Modeling and forecasting the outcomes of NBA basketball games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 31-41, March.
    3. Marco Sandri & Paola Zuccolotto & Marica Manisera, 2020. "Markov switching modelling of shooting performance variability and teammate interactions in basketball," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1337-1356, November.
    4. Loeffelholz Bernard & Bednar Earl & Bauer Kenneth W, 2009. "Predicting NBA Games Using Neural Networks," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-17, January.
    5. Jackson P. Lautier, 2023. "A New Framework to Estimate Return on Investment for Player Salaries in the National Basketball Association," Papers 2309.05783, arXiv.org.
    6. João Vítor Rocha da Silva & Paulo Canas Rodrigues, 2022. "All-NBA Teams’ Selection Based on Unsupervised Learning," Stats, MDPI, vol. 5(1), pages 1-18, February.
    7. Pierpalo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A Bayesian network to analyse basketball players’ performances: a multivariate copula-based approach," Annals of Operations Research, Springer, vol. 325(1), pages 419-440, June.

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