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A Starting Point for Analyzing Basketball Statistics

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Author Info

  • Kubatko Justin

    (The Ohio State University and basketball-reference.com)

  • Oliver Dean

    (Basketball on Paper and Denver Nuggets)

  • Pelton Kevin

    (Seattle Sonics & Storm)

  • Rosenbaum Dan T

    (University of North Carolina at Greensboro and Cleveland Cavaliers)

Abstract

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.

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Bibliographic Info

Article provided by De Gruyter in its journal Journal of Quantitative Analysis in Sports.

Volume (Year): 3 (2007)
Issue (Month): 3 (July)
Pages: 1-24

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Handle: RePEc:bpj:jqsprt:v:3:y:2007:i:3:n:1

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Web page: http://www.degruyter.com

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Cited by:
  1. Štrumbelj, Erik & Vračar, Petar, 2012. "Simulating a basketball match with a homogeneous Markov model and forecasting the outcome," International Journal of Forecasting, Elsevier, vol. 28(2), pages 532-542.
  2. Joseph Price & Justin Wolfers, 2007. "Racial Discrimination Among NBA Referees," NBER Working Papers 13206, National Bureau of Economic Research, Inc.
  3. Turner, Chad & Hakes, Jahn Karl, 2007. "The Collective Bargaining Effects of NBA Player Productivity Dynamics," MPRA Paper 5058, University Library of Munich, Germany.

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