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Optimizing NBA Lineups.

Spector, Jason.

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Optimizing NBA Lineups.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimizing NBA Lineups./
Author:	Spector, Jason.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	34 p.
Notes:	Source: Masters Abstracts International, Volume: 82-01.
Contained By:	Masters Abstracts International82-01.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27999920
ISBN:	9798641509310

Optimizing NBA Lineups.
Spector, Jason.

Optimizing NBA Lineups. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 34 p.

Source: Masters Abstracts International, Volume: 82-01.

Thesis (M.S.)--The University of Arizona, 2020.

This item must not be sold to any third party vendors.

The goal of every NBA coach is to put the best lineup on the floor against the given opposition. A coach will pick individual players to form a lineup from a variety of factors with the end goal of scoring more points than the opposing lineup. This paper aims to analyze and assess NBA lineup creation from individual statistics using various forms of machine learning. We started by web- scraping individual player statistics and five-man lineup data from basketballreference.com. Then the general box score and advanced statistics of the individual players were joined to the players in the lineup. The lineup data was used to train a linear regression model, a random forest, a support vector machine, an extreme gradient boosted model, and a neural network. All models were evaluated on their mean absolute error with the final goal of getting as close to the points the lineup actually scored. None of the models created a conclusive algorithm to accurately portray the lineup capabilities from individual statistics. This was due to the fact that individual per game statistics do not hold enough information about how combinations of players might perform together or if the performance by the player would be above or below their expected individual statistics. Furthermore, the models often overfit the data, having the ability to understand the patterns of the training data well but not able to generalize. Because of this our focus shifted to creating simpler models with fewer features. Fewer features caused a slight increase in performance but not by much. Finally, we repeated the process with a classification of bad, average, and great offensive or defensive ability as the output in hopes of at least being able to classify a good offensive or defensive lineup. Both classification networks were only slightly better than random guessing however.

ISBN: 9798641509310Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Basketball

Optimizing NBA Lineups.
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520 $a The goal of every NBA coach is to put the best lineup on the floor against the given opposition. A coach will pick individual players to form a lineup from a variety of factors with the end goal of scoring more points than the opposing lineup. This paper aims to analyze and assess NBA lineup creation from individual statistics using various forms of machine learning. We started by web- scraping individual player statistics and five-man lineup data from basketballreference.com. Then the general box score and advanced statistics of the individual players were joined to the players in the lineup. The lineup data was used to train a linear regression model, a random forest, a support vector machine, an extreme gradient boosted model, and a neural network. All models were evaluated on their mean absolute error with the final goal of getting as close to the points the lineup actually scored. None of the models created a conclusive algorithm to accurately portray the lineup capabilities from individual statistics. This was due to the fact that individual per game statistics do not hold enough information about how combinations of players might perform together or if the performance by the player would be above or below their expected individual statistics. Furthermore, the models often overfit the data, having the ability to understand the patterns of the training data well but not able to generalize. Because of this our focus shifted to creating simpler models with fewer features. Fewer features caused a slight increase in performance but not by much. Finally, we repeated the process with a classification of bad, average, and great offensive or defensive ability as the output in hopes of at least being able to classify a good offensive or defensive lineup. Both classification networks were only slightly better than random guessing however.
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