Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp015x21th84m
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Dobkin, David | - |
dc.contributor.author | Buono, Michael | - |
dc.date.accessioned | 2016-06-22T15:04:45Z | - |
dc.date.available | 2016-06-22T15:04:45Z | - |
dc.date.created | 2016-04-29 | - |
dc.date.issued | 2016-06-22 | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp015x21th84m | - |
dc.description.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 | en_US |
dc.format.extent | 59 pages | * |
dc.language.iso | en_US | en_US |
dc.title | Money(basket)ball: Using Machine Learning To Build an NBA Winning Strategy Based on Offensive Efficiency | en_US |
dc.type | Princeton University Senior Theses | - |
pu.date.classyear | 2016 | en_US |
pu.department | Computer Science | en_US |
pu.pdf.coverpage | SeniorThesisCoverPage | - |
Appears in Collections: | Computer Science, 1988-2020 |
Files in This Item:
File | Size | Format | |
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Buono_Michael_thesis.pdf | 2.23 MB | Adobe PDF | Request a copy |
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