IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v16y2020i4p301-309n4.html
   My bibliography  Save this article

Foul accumulation in the NBA

Author

Listed:
  • Chu Dani

    (Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A1S6, Canada)

  • Swartz Tim B.

    (Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A1S6, Canada)

Abstract

This paper investigates the fouling time distribution of players in the National Basketball Association. A Bayesian analysis is presented based on the assumption that fouling time distributions follow a gamma distribution. Various insights are obtained including the observation that players accumulate fouls at a rate that increases with the current number of fouls. We demonstrate possible ways to incorporate the fouling time distributions to provide decision support to coaches in the management of playing time.

Suggested Citation

  • Chu Dani & Swartz Tim B., 2020. "Foul accumulation in the NBA," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(4), pages 301-309, December.
  • Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:4:p:301-309:n:4
    DOI: 10.1515/jqas-2019-0119
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2019-0119
    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-2019-0119?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. Beaudoin, David & Swartz, Tim B., 2010. "Strategies for Pulling the Goalie in Hockey," The American Statistician, American Statistical Association, vol. 64(3), pages 197-204.
    2. Mogensen, Ulla B. & Ishwaran, Hemant & Gerds, Thomas A., 2012. "Evaluating Random Forests for Survival Analysis Using Prediction Error Curves," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i11).
    Full references (including those not matched with items on IDEAS)

    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. Bayu Adhi Tama & Sunghoon Lim, 2020. "A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems," Mathematics, MDPI, vol. 8(10), pages 1-25, October.
    2. Timothy C. Y. Chan & Justin A. Cho & David C. Novati, 2012. "Quantifying the Contribution of NHL Player Types to Team Performance," Interfaces, INFORMS, vol. 42(2), pages 131-145, April.
    3. Aizawa, Toshiaki, 2021. "Inequality of opportunity in infant mortality in South Asia: A decomposition analysis of survival data," Economics & Human Biology, Elsevier, vol. 43(C).
    4. Buttrey Samuel E., 2016. "Beating the market betting on NHL hockey games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(2), pages 87-98, June.
    5. Kamaryn T. Tanner & Linda D. Sharples & Rhian M. Daniel & Ruth H. Keogh, 2021. "Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 3-30, January.
    6. Arfan Raheen Afzal & Jing Yang & Xuewen Lu, 2021. "Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters," Computational Statistics, Springer, vol. 36(2), pages 829-855, June.
    7. Hoora Moradian & Denis Larocque & François Bellavance, 2017. "$$L_1$$ L 1 splitting rules in survival forests," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 671-691, October.
    8. Lore Zumeta-Olaskoaga & Maximilian Weigert & Jon Larruskain & Eder Bikandi & Igor Setuain & Josean Lekue & Helmut Küchenhoff & Dae-Jin Lee, 2023. "Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 101-126, March.
    9. Chunyang Li & Vikas Patil & Kelli M. Rasmussen & Christina Yong & Hsu-Chih Chien & Debbie Morreall & Jeffrey Humpherys & Brian C. Sauer & Zachary Burningham & Ahmad S. Halwani, 2021. "Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning," IJERPH, MDPI, vol. 18(5), pages 1-19, March.
    10. Zhengnan Huang & Hongjiu Zhang & Jonathan Boss & Stephen A Goutman & Bhramar Mukherjee & Ivo D Dinov & Yuanfang Guan & for the Pooled Resource Open-Access ALS Clinical Trials Consortium, 2017. "Complete hazard ranking to analyze right-censored data: An ALS survival study," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-21, December.
    11. Wang, Shikun & Li, Zhao & Lan, Lan & Zhao, Jieyi & Zheng, W. Jim & Li, Liang, 2022. "GPU accelerated estimation of a shared random effect joint model for dynamic prediction," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    12. Kim, Dongwoo, 2024. "Corporate loan duration, macroeconomic environments, and COVID-19," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 1088-1103.
    13. Julia Gilhodes & Florence Dalenc & Jocelyn Gal & Christophe Zemmour & Eve Leconte & Jean Marie Boher & Thomas Filleron, 2020. "Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings," Post-Print hal-02934793, HAL.
    14. Mikulec Artur & Misztal Małgorzata, 2018. "Does the Type of Business Activity and the Enterprise Location Affect a Firm’S Survival? Results of an Analysis for Natural Persons Conducting Economic Activity in the Łódzkie Voivodship," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(3), pages 23-40, September.
    15. Abrevaya Jason & McCulloch Robert, 2014. "Reversal of fortune: a statistical analysis of penalty calls in the National Hockey League," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 207-224, June.
    16. Sill, Martin & Hielscher, Thomas & Becker, Natalia & Zucknick, Manuela, 2014. "c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i05).
    17. Heidi Seibold & Christoph Bernau & Anne-Laure Boulesteix & Riccardo De Bin, 2018. "On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models," Computational Statistics, Springer, vol. 33(3), pages 1195-1215, September.

    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:16:y:2020:i:4:p:301-309:n:4. 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.