IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i4d10.1007_s00180-024-01556-4.html
   My bibliography  Save this article

The role of the frailty in the evaluation of injury risk factors for National Basketball Association players

Author

Listed:
  • Ambra Macis

    (University of Brescia)

Abstract

Injuries often occur in sports and, due to medical and economic reasons, it is important to understand the factors that mainly affect the risk of experiencing them. This work aims to explore this field in the context of the National Basketball Association (NBA) league. Thus, the main purpose is to identify the main individual players’ characteristics that are associated to a higher risk of suffering an injury in a shorter time, taking into account ten seasons, from the beginning of 2010–2011 season until the end of 2019–2020 season. All the needed information has been retrieved from different big datasets regarding NBA players. The work stands in the survival data analysis framework and, for the purpose, a Cox regression model with frailty has been used. Results suggest that the player’s position and the Body Mass Index have a significant effect on the injury’s risk. From a methodological point of view, this manuscript provides an insight into the role of the frailty in the model, studying its relationship with the residuals of a mispecified Cox model.

Suggested Citation

  • Ambra Macis, 2025. "The role of the frailty in the evaluation of injury risk factors for National Basketball Association players," Computational Statistics, Springer, vol. 40(4), pages 1985-2003, April.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01556-4
    DOI: 10.1007/s00180-024-01556-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01556-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01556-4?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. José Maria Pratas & Anna Volossovitch & Ana Isabel Carita, 2016. "The effect of performance indicators on the time the first goal is scored in football matches," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 16(1), pages 347-354, April.
    2. Wangrow, David B. & Schepker, Donald J. & Barker, Vincent L., 2018. "Power, performance, and expectations in the dismissal of NBA coaches: A survival analysis study," Sport Management Review, Elsevier, vol. 21(4), pages 333-346.
    3. David B. Wangrow & Donald J. Schepker & Vincent L. Barker, 2018. "Power, performance, and expectations in the dismissal of NBA coaches: A survival analysis study," Sport Management Review, Taylor & Francis Journals, vol. 21(4), pages 333-346, October.
    4. Andreas Wienke & Konstantin G. Arbeev & Isabella Locatelli & Anatoli I. Yashin, 2003. "A simulation study of different correlated frailty models and estimation strategies," MPIDR Working Papers WP-2003-018, Max Planck Institute for Demographic Research, Rostock, Germany.
    5. Xiaogang Su & Juanjuan Fan, 2004. "Multivariate Survival Trees: A Maximum Likelihood Approach Based on Frailty Models," Biometrics, The International Biometric Society, vol. 60(1), pages 93-99, March.
    6. Thomas Andrew C, 2007. "Inter-arrival Times of Goals in Ice Hockey," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-17, July.
    7. Gergely Csurilla & Imre Fertő, 2022. "How long does a medal win last? Survival analysis of the duration of Olympic success," Applied Economics, Taylor & Francis Journals, vol. 54(43), pages 5006-5020, September.
    8. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    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. Salaga, Steven & Juravich, Matthew, 2020. "National Football League head coach race, performance, retention, and dismissal," Sport Management Review, Elsevier, vol. 23(5), pages 978-991.
    2. Andreas Wienke, 2003. "Frailty models," MPIDR Working Papers WP-2003-032, Max Planck Institute for Demographic Research, Rostock, Germany.
    3. Jonathan A. Jensen, 2023. "Searching for the "Holy Grail" of sponsorship-linked marketing: A generalizable sponsorship ROI model," Papers 2305.09473, arXiv.org.
    4. Bagdonavicius, Vilijandas & Nikulin, Mikhail, 2000. "On goodness-of-fit for the linear transformation and frailty models," Statistics & Probability Letters, Elsevier, vol. 47(2), pages 177-188, April.
    5. Feehan, Dennis & Wrigley-Field, Elizabeth, 2020. "How do populations aggregate?," SocArXiv 2fkw3, Center for Open Science.
    6. Filipe Costa Souza & Wilton Bernardino & Silvio C. Patricio, 2024. "How life-table right-censoring affected the Brazilian social security factor: an application of the gamma-Gompertz-Makeham model," Journal of Population Research, Springer, vol. 41(3), pages 1-38, September.
    7. K. Motarjem & M. Mohammadzadeh & A. Abyar, 2020. "Geostatistical survival model with Gaussian random effect," Statistical Papers, Springer, vol. 61(1), pages 85-107, February.
    8. Xu, Linzhi & Zhang, Jiajia, 2010. "An EM-like algorithm for the semiparametric accelerated failure time gamma frailty model," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1467-1474, June.
    9. Annamaria Olivieri & Ermanno Pitacco, 2016. "Frailty and Risk Classification for Life Annuity Portfolios," Risks, MDPI, vol. 4(4), pages 1-23, October.
    10. James W. Vaupel, 2002. "Post-Darwinian longevity," MPIDR Working Papers WP-2002-043, Max Planck Institute for Demographic Research, Rostock, Germany.
    11. Maxim S. Finkelstein, 2005. "Shocks in homogeneous and heterogeneous populations," MPIDR Working Papers WP-2005-024, Max Planck Institute for Demographic Research, Rostock, Germany.
    12. Luping Zhao & Timothy E. Hanson, 2011. "Spatially Dependent Polya Tree Modeling for Survival Data," Biometrics, The International Biometric Society, vol. 67(2), pages 391-403, June.
    13. Yeo, Keng Leong & Valdez, Emiliano A., 2006. "Claim dependence with common effects in credibility models," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 609-629, June.
    14. Hui Zheng, 2014. "Aging in the Context of Cohort Evolution and Mortality Selection," Demography, Springer;Population Association of America (PAA), vol. 51(4), pages 1295-1317, August.
    15. Graziella Caselli & Franco Peracchi & Elisabetta Barbi & Rosa Maria Lipsi, 2003. "Differential Mortality and the Design of the Italian System of Public Pensions," LABOUR, CEIS, vol. 17(s1), pages 45-78, August.
    16. Enrique Acosta & Alain Gagnon & Nadine Ouellette & Robert R. Bourbeau & Marilia R. Nepomuceno & Alyson A. van Raalte, 2020. "The boomer penalty: excess mortality among baby boomers in Canada and the United States," MPIDR Working Papers WP-2020-003, Max Planck Institute for Demographic Research, Rostock, Germany.
    17. Zhang, Zhehao, 2018. "Renewal sums under mixtures of exponentials," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 281-301.
    18. Hess Wolfgang & Tutz Gerhard & Gertheiss Jan, 2016. "A Flexible Link Function for Discrete-Time Duration Models," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(4), pages 455-481, August.
    19. Bas Klaauw & Limin Wang, 2011. "Child mortality in rural India," Journal of Population Economics, Springer;European Society for Population Economics, vol. 24(2), pages 601-628, April.
    20. Xian Liu, 2000. "Development of a Structural Hazard Rate Model in Sociological Research," Sociological Methods & Research, , vol. 29(1), pages 77-117, August.

    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:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01556-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.