Journal of Quantitative Analysis in Sports
An official journal of the American Statistical Association
Editor-in-Chief: Mark Glickman PhD
SCImago Journal Rank (SJR) 2014: 0.265
Source Normalized Impact per Paper (SNIP) 2014: 0.513
Impact per Publication (IPP) 2014: 0.452
Volume 12 (2016)
Volume 11 (2015)
Volume 10 (2014)
Volume 9 (2013)
Volume 5 (2009)
Volume 1 (2005)
Most Downloaded Articles
- Creating space to shoot: quantifying spatial relative field goal efficiency in basketball by Shortridge, Ashton/ Goldsberry, Kirk and Adams, Matthew
- A generative model for predicting outcomes in college basketball by Ruiz, Francisco J. R. and Perez-Cruz, Fernando
- Predicting the draft and career success of tight ends in the National Football League by Mulholland, Jason and Jensen, Shane T.
- Building an NCAA men’s basketball predictive model and quantifying its success by Lopez, Michael J. and Matthews, Gregory J.
- openWAR: An open source system for evaluating overall player performance in major league baseball by Baumer, Benjamin S./ Jensen, Shane T. and Matthews, Gregory J.
A Starting Point for Analyzing Basketball Statistics
1The Ohio State University and basketball-reference.com
2Basketball on Paper and Denver Nuggets
3Seattle Sonics & Storm
4University of North Carolina at Greensboro and Cleveland Cavaliers
Citation Information: Journal of Quantitative Analysis in Sports. Volume 3, Issue 3, ISSN (Online) 1559-0410, DOI: 10.2202/1559-0410.1070, July 2007
- Published Online:
The quantitative analysis of sports is a growing branch of science and, in many ways one that has developed through non-academic and non-traditionally peer-reviewed work. The aim of this paper is to bring to a peer-reviewed journal the generally accepted basics of the analysis of basketball, thereby providing a common starting point for future research in basketball. The possession concept, in particular the concept of equal possessions for opponents in a game, is central to basketball analysis. Estimates of possessions have existed for approximately two decades, but the various formulas have sometimes created confusion. We hope that by showing how most previous formulas are special cases of our more general formulation, we shed light on the relationship between possessions and various statistics. Also, we hope that our new estimates can provide a common basis for future possession estimation. In addition to listing data sources for statistical research on basketball, we also discuss other concepts and methods, including offensive and defensive ratings, plays, per-minute statistics, pace adjustments, true shooting percentage, effective field goal percentage, rebound rates, Four Factors, plus/minus statistics, counterpart statistics, linear weights metrics, individual possession usage, individual efficiency, Pythagorean method, and Bell Curve method. This list is not an exhaustive list of methodologies used in the field, but we believe that they provide a set of tools that fit within the possession framework and form the basis of common conversations on statistical research in basketball.
Keywords: basketball possessions; offensive ratings; defensive ratings; plays; per-minute statistics; pace adjustments; true shooting percentage; effective field goal percentage; rebound rates; Four Factors; plus/minus statistics; counterpart statistics; linear weights metrics; individual possession usage; individual efficiency; Pythagorean method; Bell Curve method
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