Statistics Papers

Document Type

Journal Article

Date of this Version

6-2016

Publication Source

Journal of Quantitative Analysis in Sports

Volume

12

Issue

2

Start Page

51

Last Page

72

DOI

10.1515/jqas-2015-0027

Abstract

Traditional NBA player evaluation metrics are based on scoring differential or some pace-adjusted linear combination of box score statistics like points, rebounds, assists, etc. These measures treat performances with the outcome of the game still in question (e.g. tie score with five minutes left) in exactly the same way as they treat performances with the outcome virtually decided (e.g. when one team leads by 30 points with one minute left). Because they ignore the context in which players perform, these measures can result in misleading estimates of how players help their teams win. We instead use a win probability framework for evaluating the impact NBA players have on their teams’ chances of winning. We propose a Bayesian linear regression model to estimate an individual player’s impact, after controlling for the other players on the court. We introduce several posterior summaries to derive rank-orderings of players within their team and across the league. This allows us to identify highly paid players with low impact relative to their teammates, as well as players whose high impact is not captured by existing metrics.

Keywords

Basketball, Bayesian shrinkage, lasso, win probability

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Date Posted: 27 November 2017

This document has been peer reviewed.