Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Critically Analyzing NBA Operations with a Focus on Revenue Sharing Restructuring

Description
The NBA has experienced success because of its ability to adapt and reform business operations to reflect dynamic economic conditions. This critical analysis uses the Collective Bargaining Agreement to explore the NBA operational structure, examine the current state of affairs, and propose solutions to fundamental issues. Included is an in-depth…

The NBA has experienced success because of its ability to adapt and reform business operations to reflect dynamic economic conditions. This critical analysis uses the Collective Bargaining Agreement to explore the NBA operational structure, examine the current state of affairs, and propose solutions to fundamental issues. Included is an in-depth investigation into correcting team financial reporting and fixing market inequality across the league. Most notably, a proposal to restructure the current revenue sharing system is presented. By progressing the system to correlate winning with team financial performance, there is potential to improve competition and alleviate existing conflict. This will produce a better overall product for the NBA that drives more consumer interest, yields more revenue, and supports stronger international growth opportunity.
ContributorsHensel, Nathaniel Elijah Jeremiah (Author) / Koretz, Lora (Thesis director) / Mokwa, Michael (Committee member) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Using Machine Learning to Predict the NBA

Description
Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and outcomes are easily measurable. Predicting the outcomes of sports events…

Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and outcomes are easily measurable. Predicting the outcomes of sports events may also be easily profitable, predictions can be taken to a sportsbook and wagered on. A successful prediction model could easily turn a profit. The goal of this project was to build a model using machine learning to predict the outcomes of NBA games.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.
ContributorsGarretson, Samuel Burkett (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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The Just Saying Podcast

Description

Jack Grant and Sam Truman, two seniors at Arizona State University, discuss the latest in major sports, current events, and various other topics. Within their informal discussions, Jack and Sam "just say" whatever comes to mind and never shy away from a hot take. Most episodes include only Jack and…

Jack Grant and Sam Truman, two seniors at Arizona State University, discuss the latest in major sports, current events, and various other topics. Within their informal discussions, Jack and Sam "just say" whatever comes to mind and never shy away from a hot take. Most episodes include only Jack and Sam, but some entertain numerous guests and differing formats. The podcast is supported by a multimedia website, including written articles and interactive features. All components were further marketed through social media outreach and engagement. The Just Saying Podcast thesis paper analyzes podcast history and what has made them such a popular media outlet. Further, the paper discusses what makes The Just Saying Podcast a unique product. Our deliverable, The Just Saying Podcast, can be found at: https://podcasts.apple.com/us/podcast/the-just-saying-podcast/id1585891858 All components can be accessed through: https://www.justsayingpod.com/ https://twitter.com/JustSayingP
ContributorsTruman, Sam (Author) / Grant, Jack (Co-author) / Baker, Aaron (Thesis director) / Bonfiglio, Thomas (Committee member) / Barrett, The Honors College (Contributor) / Department of Management and Entrepreneurship (Contributor)
Created2022-05