| dc.contributor.advisor | Christina Chase. | en_US |
| dc.contributor.author | Ota, Karson L | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2018-01-12T20:57:22Z | |
| dc.date.available | 2018-01-12T20:57:22Z | |
| dc.date.copyright | 2017 | en_US |
| dc.date.issued | 2017 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/113120 | |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. | en_US |
| dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
| dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (page 33). | en_US |
| dc.description.abstract | In any competition, it is an advantage to know the actions of the opponent in advance. Knowing the move of the opponent allows for optimization of strategy in response to their move. Likewise, in football, defenses must react to the actions of the offense. Being able to predict what the offense is going to do before the play represents a tremendous advantage to the defense. This project applies machine learning algorithms to situational NFL data in order to more accurately predict play type as opposed to the widely used and overly general method of general statistics. Additionally, this project creates a way to discern tendencies in specific situations to help coaches create game plans and make in game decisions. | en_US |
| dc.description.statementofresponsibility | by Karson L. Ota. | en_US |
| dc.format.extent | 33 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Football play type prediction and tendency analysis | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 1016455954 | en_US |
