Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp016d56zw70rFull metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Papanicolaou, Andrew | - |
| dc.contributor.author | Lyons, Maxwell | - |
| dc.date.accessioned | 2013-07-12T15:33:27Z | - |
| dc.date.available | 2013-07-12T15:33:27Z | - |
| dc.date.created | 2013-06 | - |
| dc.date.issued | 2013-07-12 | - |
| dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp016d56zw70r | - |
| dc.description.abstract | NFL General Managers are constantly faced with high-risk, multi-million dollar decisions of selecting football players for their teams that maximize their teams ability to win while minimizing cost. This paper seeks to provide an analytical approach to measure player performance by creating a framework based on expected points and then valuing players, specifically running backs, based on this framework. | en_US |
| dc.format.extent | 71 pages | en_US |
| dc.language.iso | en_US | en_US |
| dc.title | The Analytic GM: Using Data Mining to Predict NFL Performance | en_US |
| dc.type | Princeton University Senior Theses | - |
| pu.date.classyear | 2013 | en_US |
| pu.department | Operations Research and Financial Engineering | en_US |
| pu.pdf.coverpage | SeniorThesisCoverPage | - |
| dc.rights.accessRights | Walk-in Access. This thesis can only be viewed on computer terminals at the <a href=http://mudd.princeton.edu>Mudd Manuscript Library</a>. | - |
| pu.mudd.walkin | yes | - |
| Appears in Collections: | Operations Research and Financial Engineering, 2000-2020 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| LYONS Maxwell.thesis.pdf | 1.97 MB | Adobe PDF | Request a copy |
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