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On Estimating the Ability of NBA Players

Author

Listed:
  • Fearnhead Paul

    (Lancaster University)

  • Taylor Benjamin Matthew

    (Lancaster University)

Abstract

This paper introduces a new model and methodology for estimating the ability of NBA players. The main idea is to directly measure how good a player is by comparing how their team performs when they are on the court as opposed to when they are off it. This is achieved in such a way as to control for the changing abilities of the other players on court at different times during a match. The new method uses multiple seasons' data in a structured way to estimate player ability in an isolated season, measuring separately defensive and offensive merit as well as combining these to give an overall rating. The use of game statistics in predicting player ability will be considered. Results using data from the 2008/9 season suggest that LeBron James, who won the NBA MVP award, was the best overall player. The best defensive player was Lamar Odom and the best rookie was Russell Westbrook, neither of whom won an NBA award that season. The results further indicate that whilst the frequently-reported game statistics provide some information on offensive ability, they do not perform well in the prediction of defensive ability.

Suggested Citation

  • Fearnhead Paul & Taylor Benjamin Matthew, 2011. "On Estimating the Ability of NBA Players," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-18, July.
    • Handle: RePEc:bpj:jqsprt:v:7:y:2011:i:3:n:11
    DOI: 10.2202/1559-0410.1298
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. 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.
    3. Stephen A. Bergman & Trevon D. Logan, 2020. "Revenue per Quality of College Football Recruit," Journal of Sports Economics, , vol. 21(6), pages 571-592, August.
    4. Rodolfo Metulini & Giorgio Gnecco, 2023. "Measuring players’ importance in basketball using the generalized Shapley value," Annals of Operations Research, Springer, vol. 325(1), pages 441-465, June.
    5. Lim, Alejandro & Chiang, Chin-Tsang & Teng, Jen-Chieh, 2021. "Estimating robot strengths with application to selection of alliance members in FIRST robotics competitions," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    6. 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.
    7. Paola Zuccolotto & Marco Sandri & Marica Manisera, 2023. "Spatial performance analysis in basketball with CART, random forest and extremely randomized trees," Annals of Operations Research, Springer, vol. 325(1), pages 495-519, June.
    8. Kharrat, Tarak & McHale, Ian G. & Peña, Javier López, 2020. "Plus–minus player ratings for soccer," European Journal of Operational Research, Elsevier, vol. 283(2), pages 726-736.
    9. 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.
    10. Shankar Ghimire & Justin A Ehrlich & Shane D Sanders, 2020. "Measuring individual worker output in a complementary team setting: Does regularized adjusted plus minus isolate individual NBA player contributions?," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-11, August.
    11. Paola Zuccolotto & Marco Sandri & Marica Manisera, 2021. "Spatial Performance Indicators and Graphs in Basketball," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 725-738, August.

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