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