Dissertation/Thesis Abstract

Predicting National Basketball Association Game Outcomes Using Ensemble Learning Techniques
by Valenzuela, Russell, M.S., California State University, Long Beach, 2018, 54; 10980443
Abstract (Summary)

There have been a number of studies that try to predict sporting event outcomes. Most previous research has involved results in football and college basketball. Recent years has seen similar approaches carried out in professional basketball. This thesis attempts to build upon existing statistical techniques and apply them to the National Basketball Association using a synthesis of algorithms as motivation. A number of ensemble learning methods will be utilized and compared in hopes of improving the accuracy of single models. Individual models used in this thesis will be derived from Logistic Regression, Naïve Bayes, Random Forests, Support Vector Machines, and Artificial Neural Networks while aggregation techniques include Bagging, Boosting, and Stacking. Data from previous seasons and games from both?players and teams will be used to train models in R.

Indexing (document details)
Advisor: Safer, Alan
Commitee: Korosteleva, Olga, Suaray, Kagba
School: California State University, Long Beach
Department: Mathematics and Statistics
School Location: United States -- California
Source: MAI 58/05M(E), Masters Abstracts International
Subjects: Statistics, Artificial intelligence
Publication Number: 10980443
ISBN: 978-1-392-07406-0
Copyright © 2020 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy