Graduation Year
2020
Date of Submission
6-2020
Document Type
Open Access Senior Thesis
Degree Name
Bachelor of Arts
Department
Mathematics
Second Department
Computer Science
Reader 1
Mark Huber
Terms of Use & License Information
Rights Information
© 2020 Harrison D Miller
Abstract
In this paper I will be breaking down a scholarly article, written by Sameer K. Deshpande and Shane T. Jensen, that proposed a new method to evaluate NBA players. The NBA is the highest level professional basketball league in America and stands for the National Basketball Association. They proposed to build a model that would result in how NBA players impact their teams chances of winning a game, using machine learning and probability concepts. I preface that by diving into these concepts and their mathematical backgrounds. These concepts include building a linear model using ordinary least squares method, the bias variance trade off, regularization and three methods of regularization, Gibbs samplers, and kernel density estimation. Furthermore, I explain how each of these concepts affect the process of building their model. Lastly, I explain the effectiveness of their methodology, as well as its flaws and how I would improve it.
Recommended Citation
Miller, Harrison, "How Machine Learning and Probability Concepts Can Improve NBA Player Evaluation" (2020). CMC Senior Theses. 3222.
https://scholarship.claremont.edu/cmc_theses/3222
Included in
Applied Statistics Commons, Data Science Commons, Other Applied Mathematics Commons, Probability Commons, Statistical Methodology Commons, Statistical Models Commons, Statistical Theory Commons
