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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp015x21th84m
Title: Money(basket)ball: Using Machine Learning To Build an NBA Winning Strategy Based on Offensive Efficiency
Authors: Buono, Michael
Advisors: Dobkin, David
Department: Computer Science
Class Year: 2016
Abstract: In this paper, I attempt to assess the validity of a certain theory of how NBA basketball should be played. To do this, I first look to establish a correlation between shot efficiency and winning, scraping data from stats.nba.com and testing whether applying the theory can predict the outcomes of NBA games and seasons. I then attempt to use the theory to explain past phenomena and predict future situations. In the pages that follow, I describe this efficiencydriven theory, explain how the tests work, and discuss how the theory stood up against the tests
Extent: 59 pages
URI: http://arks.princeton.edu/ark:/88435/dsp015x21th84m
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Computer Science, 1988-2016

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