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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp015x21th84m
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dc.contributor.advisorDobkin, David-
dc.contributor.authorBuono, Michael-
dc.date.accessioned2016-06-22T15:04:45Z-
dc.date.available2016-06-22T15:04:45Z-
dc.date.created2016-04-29-
dc.date.issued2016-06-22-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp015x21th84m-
dc.description.abstractIn 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 testsen_US
dc.format.extent59 pages*
dc.language.isoen_USen_US
dc.titleMoney(basket)ball: Using Machine Learning To Build an NBA Winning Strategy Based on Offensive Efficiencyen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2016en_US
pu.departmentComputer Scienceen_US
pu.pdf.coverpageSeniorThesisCoverPage-
Appears in Collections:Computer Science, 1988-2020

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