Assessing end-of-season performance as a function of average minutes played for NBA players
-
Matthew Jewell
und
C. Shane Reese
Abstract
Since the National Basketball Association (NBA) fined the San Antonio Spurs in 2012 for their decision to rest their best players during a nationally televised game, there has been much discussion surrounding player “load” management. In fact, more teams in the NBA have begun to adopt the Spurs approach of strategically resting players in an effort to avoid injury and ensure players are at peak performance towards the end of the season. Although there is general agreement that load management is important for some players to avoid injury, it isn’t clear that managing minutes during the beginning of a season results in peak performance towards the end. In this paper we attempt to address this. We do this by formulating a Bayesian hierarchical model that borrows strength among players to estimate a league-wide effect that average minutes played has on end-of-season performance based on 14 modern performance metrics. Generally speaking, it appears that end-of-season offensive and defensive performance tends be above average as average minutes played is increased.
Acknowledgements
We’d like to thank the referee and associate editor for comments that greatly improved the quality of this paper.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. GLP: heavily involved in the model formulation, model fitting, model evaluation, and writing and revising document MJ: Principal data collector and heavily involved in initial model formulation and fitting. CSR: Heavily involved in model formulation, model evaluation, writing and revising document.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: The author states no conflict of interest.
-
Research funding: None declared.
-
Data availability: The data that support the findings of this study are available from the corresponding author, [GLP], upon reasonable request.
References
Belk, J. W., Marshall, H. A., McCarthy, E. C., and Kraeutler, M. J. (2017). The effect of regular-season rest on playoff performance among players in the National Basketball Association. Orthop. J. Sports Med. 5, https://doi.org/10.1177/2325967117729798.Suche in Google Scholar PubMed PubMed Central
Bourdas, D. I., Travlos, A. K., Souglis, A., Gofas, D. C., Stavropoulos, D., and Bakirtzoglou, P. (2024). Basketball fatigue impact on kinematic parameters and 3-point shooting accuracy: insights across players’ Positions and Cardiorespiratory Fitness Associations of High-Level Players. Sports 12, https://doi.org/10.3390/sports12030063.Suche in Google Scholar PubMed PubMed Central
de Valpine, P., Paciorek, C., Turek, D., Michaud, N., Anderson-Bergman, C., Obermeyer, F., Wehrhahn Cortes, C., Rodrìguez, A., Temple Lang, D., and Paganin, S. (2022). NIMBLE: MCMC, particle filtering, and programmable hierarchical modeling, R package version 0.12.2.Suche in Google Scholar
de Valpine, P., Turek, D., Paciorek, C., Anderson-Bergman, C., Temple Lang, D., and Bodik, R. (2017). Programming with models: writing statistical algorithms for general model structures with NIMBLE. J. Comput. Graph. Statistics 26: 403–413, https://doi.org/10.1080/10618600.2016.1172487.Suche in Google Scholar
Finlay, S. G., Grelle, B. A., and Andersen, J. C. (2019). Guided load management for female soccer athletes: a case series using Global positioning System Technology (gps) during Return to Sport. Int. J. Sports Phys. Ther. 14: S15.Suche in Google Scholar
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis, 3rd ed. Chapman and Hall/CRC, Boca Raton, Florida, USA.10.1201/b16018Suche in Google Scholar
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis 1: 515–534, https://doi.org/10.1214/06-ba117a.Suche in Google Scholar
Gong, H., Watanabe, N. M., Soebbing, B. P., Brown, M. T., and Nagel, M. S. (2021). Exploring tanking strategies in the NBA: an empirical analysis of resting healthy players. Sport Manag. Rev. 25, https://doi.org/10.1080/14413523.2021.1970972.Suche in Google Scholar
Grazian, C. and Robert, C. P. (2018). Jeffreys priors for mixture estimation: properties and alternatives. Comput. Statistics Data Analysis 121: 149–163, https://doi.org/10.1016/j.csda.2017.12.005.Suche in Google Scholar
Lewis, M. (2018). It’s a hard-knock life: game load, fatigue, and injury risk in the National Basketball Association. J. Athl. Train. 53: 503–509, https://doi.org/10.4085/1062-6050-243-17.Suche in Google Scholar PubMed PubMed Central
Llc, S. R. (2022). Sports Reference LLC basketball-reference.Com – Basketball Statistics and History. Available at: https://www.basketball-reference.com/.Suche in Google Scholar
Lynn, J. (2022). Clippers president addresses load management plan for 2022–23 season. Sports Illustrated, Available at: https://www.si.com/nba/clippers/news/clippers-president-addresses-load-management-plan-for-2022-23-season (Accessed 9 Sep 2022).Suche in Google Scholar
Marks, B. (2023). How the NBA’s new rules on resting stars will work. Available at: https://www.espn.com/nba/story/_/id/38386013/how-nba-%20new-rules-resting-stars-work Suche in Google Scholar
Nakamura-Sakai, S., Forastiere, L., and Macdonald, B. (2024). Estimating the age-conditioned average treatment effects curves: an application for assessing load-management strategies in the NBA. arXiv:2402.12400, https://arxiv.org/abs/2402.12400.Suche in Google Scholar
Pradham, S. and Miller, T. J. (2022). Does rest breed rust? An examination of dnp-rest decisions and performance in the National Basketball Association regular and post-season. Frontiers Sports Active Living 4, https://doi.org/10.3389/fspor.2022.977692.Suche in Google Scholar PubMed PubMed Central
Sansone, P., Gasperi, L., Tessitore, A., and Gomez, M. A. (2021). Training load, recovery and game performance in semiprofessional male basketball: influence of individual characteristics and contextual factors. Biology of Sport 38: 207–217, https://doi.org/10.5114/biolsport.2020.98451.Suche in Google Scholar PubMed PubMed Central
Weiss, K. J., Allen, S. V., McGuigan, M. R., and Whatman, C. S. (2017). The relationship between training load and injury in men’s professional basketball. Int. J. Sports Physiol. Perform. 12: 1238–1242, https://doi.org/10.1123/ijspp.2016-0726.Suche in Google Scholar PubMed
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/jqas-2023-0038).
© 2025 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Editorial
- NBA basketball issue introduction: current academic state and challenges
- Research Articles
- Multi-agent statistically discriminative sub-trajectory mining and an application to NBA basketball
- Assessing end-of-season performance as a function of average minutes played for NBA players
- A Bayesian two-stage framework for lineup-independent assessment of individual rebounding ability in the NBA
- Improving the aggregation and evaluation of NBA mock drafts
- A basketball paradox: exploring NBA team defensive efficiency in a positionless game
Artikel in diesem Heft
- Frontmatter
- Editorial
- NBA basketball issue introduction: current academic state and challenges
- Research Articles
- Multi-agent statistically discriminative sub-trajectory mining and an application to NBA basketball
- Assessing end-of-season performance as a function of average minutes played for NBA players
- A Bayesian two-stage framework for lineup-independent assessment of individual rebounding ability in the NBA
- Improving the aggregation and evaluation of NBA mock drafts
- A basketball paradox: exploring NBA team defensive efficiency in a positionless game
