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The Problem of Shot Selection in Basketball

Overview of attention for article published in PLOS ONE, January 2012
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Title
The Problem of Shot Selection in Basketball
Published in
PLOS ONE, January 2012
DOI 10.1371/journal.pone.0030776
Pubmed ID
Authors

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 f depends crucially on the number n of shot opportunities remaining (say, before the shot clock expires), with larger n demanding that only higher-quality shots should be taken. The function f(n) 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.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 3 4%
Spain 1 1%
Portugal 1 1%
Unknown 79 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 21%
Student > Master 17 20%
Student > Bachelor 14 17%
Researcher 9 11%
Professor 4 5%
Other 11 13%
Unknown 11 13%
Readers by discipline Count As %
Sports and Recreations 25 30%
Physics and Astronomy 6 7%
Social Sciences 5 6%
Mathematics 5 6%
Agricultural and Biological Sciences 4 5%
Other 24 29%
Unknown 15 18%