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Optimal Stopping in the NBA: An Empirical Model of the Miami Heat

35 Pages Posted: 6 Dec 2013 Last revised: 20 Aug 2014

Matt Goldman

Microsoft Corporation - Microsoft Research - Redmond; UC San Diego

Justin M. Rao

Microsoft Research; Microsoft Corporation - Microsoft Research - Redmond

Date Written: August 19, 2014

Abstract

We study how highly experienced agents, professional basketball players, solve an optimal stopping problem. By rule, teams must shoot within 24 seconds of the start of a possession. The decision of when to shoot requires weighing the current shooting opportunity against the continuation value of the possession. At each second of the "shot clock," optimal play requires that a lineup's reservation shot value equals the continuation value of the possession. We empirically test this prediction with a structural stopping model. Most lineups adopt a reservation threshold that matches the continuation value function very closely. These lineups tend to be those with more shared playing experience. Mistakes we do observe come in the form too low a threshold and excess steepness. Overall, the lineups we study capture 84% of the gains of a dynamic threshold vs. an optimal fixed threshold, with only a single lineup estimated to capture less than 66%.

Keywords: optimal stopping, expertise, lab vs. field, behavioral biases, marginal thinking

JEL Classification: D83, C51, D03, D22

Suggested Citation

Goldman, Matt and Rao, Justin M., Optimal Stopping in the NBA: An Empirical Model of the Miami Heat (August 19, 2014). Available at SSRN: https://ssrn.com/abstract=2363709 or http://dx.doi.org/10.2139/ssrn.2363709

Matt Goldman

Microsoft Corporation - Microsoft Research - Redmond ( email )

Building 99
Redmond, WA
United States

UC San Diego ( email )

CA
United States

Justin M. Rao (Contact Author)

Microsoft Research ( email )

641 Avenue of Americas
7th Floor
New York, NY 11249
United States

Microsoft Corporation - Microsoft Research - Redmond ( email )

Building 99
Redmond, WA
United States

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