Applications of machine learning : basketball strategy
Name
1127911338-MIT.pdf
Size
6.38 MB
Format
Adobe PDF
Checksum (MD5)
22f45fe3aec95d1b6288169f60b16f9a
Author(s)
Narayan, Santhosh.
Advisor(s)
Anette 'Peko' Hosoi.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
While basketball has begun to rapidly evolve in recent years with the popularization of the three-point shot, the way we understand the game has lagged behind. Players are still forced into the characterization of the traditional five positions: point guard, shooting guard, small forward, power forward, and center, and metrics such as True Shooting Percentage and Expected Shot Quality are just beginning to become well-known. In this paper, we show how to apply Principal Component Analysis to better understand traits of current player positions and create relevant player features based on in-game spatial event data. We also apply unsupervised machine learning techniques in clustering to discover new player categorizations and apply neural networks to create improved models of effective field goal percentage and effective shot quality.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 72-74).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
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