There are 32 teams in the National Football League all competing to be the best by creating the strongest roster possible. The problem of evaluating talent has created extreme competition between teams in the form of a rookie draft and a fiercely competitive veteran free agent market. The difficulty with player evaluation is due to the noise associated with measuring a particular player’s value. The intent of this paper is to create an algorithm for identifying the inefficiencies in pricing in these player markets. In particular, this paper focuses on the veteran free agent market for offensive linemen in the NFL. NFL offensive linemen are difficult to evaluate empirically because of the significant amount of noise present due to an inability to measure a lineman’s performance directly. The algorithm first uses a machine learning technique, k-means cluster analysis, to generate a comparative set of offensive lineman. Then using that set of comparable offensive linemen, the algorithm flags any lineman that vary significantly in earnings from their peers. It is in this fashion that the algorithm provides relative valuations for particular offensive lineman.
| Identifer | oai:union.ndltd.org:CLAREMONT/oai:scholarship.claremont.edu:cmc_theses-2634 |
| Date | 01 January 2017 |
| Creators | Pyne, Sean |
| Publisher | Scholarship @ Claremont |
| Source Sets | Claremont Colleges |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CMC Senior Theses |
| Rights | © 2017 Sean Pyne, default |
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