Predicting NBA games with matrix factorization
Name
967660262-MIT.pdf
Description
Full printable version
Size
1.34 MB
Format
Adobe PDF
Checksum (MD5)
98c84ed8b713d788794316df440784dc
Author(s)
Tran, Tuan, M. Eng (Tuan Minh) Massachusetts Institute of Technology
Advisor(s)
Regina Barzilay.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
In my thesis, I present the methods I use to predict NBA games using matrix factorization. Matrix factorization is popular through the Netflix recommendation problem, but in general, one can apply it to data that are best modeled as the result of pairwise interaction. My thesis contains three parts. First, I explain how I model NBA prediction as a matrix factorization problem and use the basic low-rank matrix factorization approach to discover structure in the data. I also explain some differences between using matrix factorization for NBA prediction versus that in the Netflix recommendation problem. Second, I use probabilistic matrix factorization (PMF) to incorporate the fact that when two teams play each other, the scores will be different each time. Lastly, I incorporate supplementary information such as the date of the game by combining multiple PMF problems using Gaussian process priors. I replace the scalar latent features with functions of this supplementary information to aid with prediction.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 37).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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