IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v9y2013i4p337-345n1.html
   My bibliography  Save this article

Effect of position, usage rate, and per game minutes played on NBA player production curves

Author

Listed:
  • Page Garritt L.

    (Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile)

  • Barney Bradley J.

    (Kennesaw State University, Department of Mathematics and Statistics, Kennesaw, GA, USA)

  • McGuire Aaron T.

    (Capital One, VA, USA)

Abstract

In this paper, we model a basketball player’s on-court production as a function of the percentiles corresponding to the number of games played. A player’s production curve is flexibly estimated using Gaussian process regression. The hierarchical structure of the model allows us to borrow strength across players who play the same position and have similar usage rates and play a similar number of minutes per game. From the results of the modeling, we discuss questions regarding the relative deterioration of production for each of the different player positions. Learning how minutes played and usage rate affect a player’s career production curve should prove to be useful to NBA decision makers.

Suggested Citation

  • 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.
  • Handle: RePEc:bpj:jqsprt:v:9:y:2013:i:4:p:337-345:n:1
    DOI: 10.1515/jqas-2012-0023
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2012-0023
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/jqas-2012-0023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Ozmen M. Utku, 2012. "Foreign Player Quota, Experience and Efficiency of Basketball Players," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-18, March.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Alexander Hinton & Yiguo Sun, 2020. "The sunk-cost fallacy in the National Basketball Association: evidence using player salary and playing time," Empirical Economics, Springer, vol. 59(2), pages 1019-1036, August.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Macdonald Brian, 2012. "Adjusted Plus-Minus for NHL Players using Ridge Regression with Goals, Shots, Fenwick, and Corsi," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-24, October.
    4. 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.
    5. 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.
    6. 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.
    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. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:9:y:2013:i:4:p:337-345:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.