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|Title:||Money(basket)ball: Using Machine Learning To Build an NBA Winning Strategy Based on Offensive Efficiency|
|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|
|Type of Material:||Princeton University Senior Theses|
|Appears in Collections:||Computer Science, 1988-2017|
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|Buono_Michael_thesis.pdf||2.23 MB||Adobe PDF||Request a copy|
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