Modelling the NBA to make better predictions
Name
870969496-MIT.pdf
Description
Full printable version
Size
7.69 MB
Format
Adobe PDF
Checksum (MD5)
1d379adedffcc56e143e859acbe07ab9
Author(s)
Puranmalka, Keshav
Advisor(s)
Leslie P. Kaelbling.
Alternative Title
Modelling the National Basketball Association to make better predictions
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
Unexpected events often occur in the world of sports. In my thesis, I present work that models the NBA. My goal was to build a model of the NBA Machine Learning and other statistical tools in order to better make predictions and quantify unexpected events. In my thesis, I first review other quantitative models of the NBA. Second, I present novel features extracted from NBA play-by-play data that I use in building my predictive models. Third, I propose predictive models that use team-level statistics. In the team models, I show that team strength relations might not be transitive in these models. Fourth, I propose predictive models that use player-level statistics. In these player-level models, I demonstrate that taking the context of a play into account is important in making useful prediction. Finally, I analyze the effectiveness of the different models I created, and propose suggestions for future lines of inquiry.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 65-66).
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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