JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 573
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Sports
Abstract #312419 View Presentation
Title: Estimating NBA Player Contribution with Regularized Logistic Regression
Author(s): Sameer Deshpande*+
Companies: Wharton School
Keywords: Logistic Regression ; Regularization ; Shrinkage ; Basketball analytics

We propose a regularized logistic regression model to estimate NBA player contributions adjusted for all other players, teams, and other in-game variables. Traditional statistics, such as adjusted plus-minus, while useful for retrospectively comparing players, are often ill-suited to inform in-game lineup decisions. We instead consider the following regression problem: what does each player contribute to the odds of preserving a lead? By incorporating real-time box score information, we attempt to predict whether using a particular lineup at a specific point during a game helps or hurts a team's chances of winning. We perform two separate analyses: variable selection to identify the "best" and "worst" contributors and a fully Bayesian analysis to predict lineup efficacy.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2014 program

2014 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Professional Development program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.