Measuring spatial allocative efficiency in basketball
Sandholtz Nathan (),
Mortensen Jacob () and
Bornn Luke ()
Additional contact information
Sandholtz Nathan: Simon Fraser University, Burnaby, Canada
Mortensen Jacob: Simon Fraser University, Burnaby, Canada
Bornn Luke: Simon Fraser University, Burnaby, Canada
Journal of Quantitative Analysis in Sports, 2020, vol. 16, issue 4, 271-289
Abstract:
Every shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.
Keywords: basketball; Bayesian hierarchical model; ordering; ranking; spatial data (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1515/jqas-2019-0126 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bpj:jqsprt:v:16:y:2020:i:4:p:271-289:n:2
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jqas/html
DOI: 10.1515/jqas-2019-0126
Access Statistics for this article
Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman
More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().