Interactive tools for fantasy football analytics and predictions using machine learning
Name
933249435-MIT.pdf
Description
Full printable version
Size
10.36 MB
Format
Adobe PDF
Checksum (MD5)
1fce0862025f438e5b0ad1b401b2cc5b
Author(s)
Parikh,Neena (Neena S.)
Advisor(s)
Anantha Chandrakasan.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
The focus of this project is multifaceted: we aim to construct robust predictive models to project the performance of individual football players, and we plan to integrate these projections into a web-based application for in-depth fantasy football analytics. Most existing statistical tools for the NFL are limited to the use of macro-level data; this research looks to explore statistics at a finer granularity. We explore various machine learning techniques to develop predictive models for different player positions including quarterbacks, running backs, wide receivers, tight ends, and kickers. We also develop an interactive interface that will assist fantasy football participants in making informed decisions when managing their fantasy teams. We hope that this research will not only result in a well-received and widely used application, but also help pave the way for a transformation in the field of football analytics.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 83-84).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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