IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0030776.html
   My bibliography  Save this article

The Problem of Shot Selection in Basketball

Author

Listed:
  • Brian Skinner

Abstract

In basketball, every time the offense produces a shot opportunity the player with the ball must decide whether the shot is worth taking. In this article, I explore the question of when a team should shoot and when they should pass up the shot by considering a simple theoretical model of the shot selection process, in which the quality of shot opportunities generated by the offense is assumed to fall randomly within a uniform distribution. Within this model I derive an answer to the question “how likely must the shot be to go in before the player should take it?” and I show that this lower cutoff for shot quality depends crucially on the number of shot opportunities remaining (say, before the shot clock expires), with larger demanding that only higher-quality shots should be taken. The function is also derived in the presence of a finite turnover rate and used to predict the shooting rate of an optimal-shooting team as a function of time. The theoretical prediction for the optimal shooting rate is compared to data from the National Basketball Association (NBA). The comparison highlights some limitations of the theoretical model, while also suggesting that NBA teams may be overly reluctant to shoot the ball early in the shot clock.

Suggested Citation

  • Brian Skinner, 2012. "The Problem of Shot Selection in Basketball," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-8, January.
  • Handle: RePEc:plo:pone00:0030776
    DOI: 10.1371/journal.pone.0030776
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030776
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0030776&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0030776?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
    ---><---

    References listed on IDEAS

    as
    1. Kubatko Justin & Oliver Dean & Pelton Kevin & Rosenbaum Dan T, 2007. "A Starting Point for Analyzing Basketball Statistics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-24, July.
    2. Jordi Duch & Joshua S Waitzman & Luís A Nunes Amaral, 2010. "Quantifying the Performance of Individual Players in a Team Activity," PLOS ONE, Public Library of Science, vol. 5(6), pages 1-7, June.
    3. Filippo Radicchi, 2011. "Who Is the Best Player Ever? A Complex Network Analysis of the History of Professional Tennis," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
    4. Skinner Brian, 2011. "Scoring Strategies for the Underdog: A General, Quantitative Method for Determining Optimal Sports Strategies," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-18, October.
    5. Tal Neiman & Yonatan Loewenstein, 2011. "Reinforcement learning in professional basketball players," Nature Communications, Nature, vol. 2(1), pages 1-8, September.
    6. Gur Yaari & Shmuel Eisenmann, 2011. "The Hot (Invisible?) Hand: Can Time Sequence Patterns of Success/Failure in Sports Be Modeled as Repeated Random Independent Trials?," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-10, October.
    7. Tal Neiman & Yonatan Loewenstein, 2011. "Reinforcement learning in professional basketball players," Discussion Paper Series dp593, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    8. Skinner Brian, 2010. "The Price of Anarchy in Basketball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(1), pages 1-18, January.
    9. Arkes Jeremy, 2010. "Revisiting the Hot Hand Theory with Free Throw Data in a Multivariate Framework," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(1), pages 1-12, January.
    10. C. Sire & S. Redner, 2009. "Understanding baseball team standings and streaks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 473-481, February.
    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. Jiao Jieying & Hu Guanyu & Yan Jun, 2021. "A Bayesian marked spatial point processes model for basketball shot chart," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 77-90, June.
    2. Leonardo Lamas & José Vitor Senatore & Gilbert Fellingham, 2020. "Two steps for scoring a point: Creating and converting opportunities in invasion team sports," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-16, October.
    3. Tal Neiman & Yonatan Loewenstein, 2014. "Spatial Generalization in Operant Learning: Lessons from Professional Basketball," Discussion Paper Series dp665, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    4. Jorge Serna & Verónica Muñoz-Arroyave & Jaume March-Llanes & M. Teresa Anguera & Queralt Prat & Aaron Rillo-Albert & David Falcón & Pere Lavega-Burgués, 2021. "Effect of Ball Screen and One-on-One on the Level of Opposition and Effectiveness of Shots in the ACB," IJERPH, MDPI, vol. 18(5), pages 1-16, March.
    5. Tal Neiman & Yonatan Loewenstein, 2014. "Spatial Generalization in Operant Learning: Lessons from Professional Basketball," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-8, May.

    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. Joshua B. Miller & Adam Sanjurjo, 2015. "Is it a Fallacy to Believe in the Hot Hand in the NBA Three-Point Contest?," Working Papers 548, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Joshua B. Miller & Adam Sanjurjo, 2014. "A Cold Shower for the Hot Hand Fallacy," Working Papers 518, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Tal Neiman & Yonatan Loewenstein, 2014. "Spatial Generalization in Operant Learning: Lessons from Professional Basketball," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-8, May.
    4. Miller, Joshua B. & Sanjurjo, Adam, 2021. "Is it a fallacy to believe in the hot hand in the NBA three-point contest?," European Economic Review, Elsevier, vol. 138(C).
    5. Miller, Joshua Benjamin & Sanjurjo, Adam, 2018. "A Visible (Hot) Hand? Expert Players Bet on the Hot Hand and Win," OSF Preprints sd32u, Center for Open Science.
    6. Miller, Joshua Benjamin & Sanjurjo, Adam, 2018. "Is it a Fallacy to Believe in the Hot Hand in the NBA Three-Point Contest?," OSF Preprints dmksp, Center for Open Science.
    7. Tal Neiman & Yonatan Loewenstein, 2014. "Spatial Generalization in Operant Learning: Lessons from Professional Basketball," Discussion Paper Series dp665, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    8. Mukherjee, Satyam, 2012. "Identifying the greatest team and captain—A complex network approach to cricket matches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6066-6076.
    9. Gur Yaari & Shmuel Eisenmann, 2011. "The Hot (Invisible?) Hand: Can Time Sequence Patterns of Success/Failure in Sports Be Modeled as Repeated Random Independent Trials?," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-10, October.
    10. Filippo Radicchi, 2011. "Who Is the Best Player Ever? A Complex Network Analysis of the History of Professional Tennis," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
    11. Gur Yaari & Gil David, 2012. "“Hot Hand” on Strike: Bowling Data Indicates Correlation to Recent Past Results, Not Causality," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-9, January.
    12. Arkes Jeremy & Martinez Jose, 2011. "Finally, Evidence for a Momentum Effect in the NBA," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-16, July.
    13. Gabel Alan & Redner Sidney, 2012. "Random Walk Picture of Basketball Scoring," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-20, March.
    14. Chacoma, Andrés & Billoni, Orlando V., 2023. "Probabilistic model for Padel games dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    15. Hanan Shteingart & Tal Neiman & Yonatan Loewenstein, 2012. "The Role of First Impression in Operant Learning," Discussion Paper Series dp626, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    16. Ofri Raviv & Merav Ahissar & Yonatan Loewenstein, 2012. "How Recent History Affects Perception: The Normative Approach and Its Heuristic Approximation," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-10, October.
    17. Peter Csapo & Markus Raab, 2014. "“Hand down, Man down.” Analysis of Defensive Adjustments in Response to the Hot Hand in Basketball Using Novel Defense Metrics," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-25, December.
    18. Aloys Prinz, 2019. "Learning (Not) to Evade Taxes," Games, MDPI, vol. 10(4), pages 1-18, September.
    19. Joshua B. Miller & Adam Sanjurjo, 2018. "Surprised by the Hot Hand Fallacy? A Truth in the Law of Small Numbers," Econometrica, Econometric Society, vol. 86(6), pages 2019-2047, November.
    20. Joshua B. Miller & Adam Sanjurjo, 2019. "Surprised by the Hot Hand Fallacy? A Truth in the Law of Small Numbers," Papers 1902.01265, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0030776. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.