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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942 Crew

Description
A new student organization called the 942 Crew was started in 2012 as an extension of the Student Initiative to increase student attendance and the in game atmosphere at Sun Devil men's basketball games. The new organization is part of Sun Devil Athletics and is full of students of all…

A new student organization called the 942 Crew was started in 2012 as an extension of the Student Initiative to increase student attendance and the in game atmosphere at Sun Devil men's basketball games. The new organization is part of Sun Devil Athletics and is full of students of all ages and backgrounds. In being part of Sun Devil Athletics, the 942 Crew has the financial support as well as resources to achieve the goals of increasing student attendance and improving the in game atmosphere for students, fans, and players. I have been a member of the 942 Crew for four of its five years on campus, and have seen and been a part of the changes brought to Sun Devil Athletics because of the organization. Through new marketing techniques that include a peer-to-peer aspect Sun Devil Athletics did not have before, the revolutionary Curtain of Distraction, and the support of the athletic department, the 942 Crew has been able to nearly double the average student attendance from the 2012-2013 season to the 2015-2016 season. With average student attendance growing from 455.06 students a game in 2012-2013 to 881.41 in 2015-2016, a new era of Sun Devil Athletics has begun in which a student organization and administration work together to create a more consistent level of higher student attendance. Aside from men's basketball, the 942 Crew has also changed the student ticketing of football games through the emergence of Camp Fargo and broken student attendance records at both men's and women's sports across the university.
ContributorsFicker, Ryan Michael (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12

Utilizing Information Measurement Theory as a Predictor for Successful Basketball Coaches

Description
The author is interested in a variety of topics in the sector of sports, especially coaching. Growing up in a household with a father who was a high school basketball coach for 21 years, the author has always seen sports in a different light, especially basketball. As a result, the…

The author is interested in a variety of topics in the sector of sports, especially coaching. Growing up in a household with a father who was a high school basketball coach for 21 years, the author has always seen sports in a different light, especially basketball. As a result, the author has been intrigued for quite some time by possible indicators and/or predictors for successful basketball coaches. Principles taken from Dr. Dean Kashiwagi's Infomation Measurement Theory and Kashiwagi Solution Model were utilized in the evaluation of current and former coaches in the National Basketball Association. 4 NBA coaches were researched in a manner that evaluated their overall success based on: longevity, wins/losses, and championships. While many of the key principles highlighted in IMT/KSM are applicable in this study, much of the emphasis was placed on evaluating Type A and Type C characteristics present in both successful and unsuccessful coaches.
ContributorsWinter, Logan Brian (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Basketball Shooting Prediction Using Machine Learning Models and Motion Capture System

Description
This project explores the potential for the accurate prediction of basketball shooting posture with machine learning (ML) prediction algorithms, using the data collected by an Internet of Things (IoT) based motion capture system. Specifically, this question is addressed in the research - Can I develop an ML model to generalize…

This project explores the potential for the accurate prediction of basketball shooting posture with machine learning (ML) prediction algorithms, using the data collected by an Internet of Things (IoT) based motion capture system. Specifically, this question is addressed in the research - Can I develop an ML model to generalize a decent basketball shot pattern? - by introducing a supervised learning paradigm, where the ML method takes acceleration attributes to predict the basketball shot efficiency. The solution presented in this study considers motion capture devices configuration on the right upper limb with a sole motion sensor made by BNO080 and ESP32 attached on the right wrist, right forearm, and right shoulder, respectively, By observing the rate of speed changing in the shooting movement and comparing their performance, ML models that apply K-Nearest Neighbor, and Decision Tree algorithm, conclude the best range of acceleration that different spots on the arm should implement.
ContributorsLiang, Chengxu (Author) / Ingalls, Todd (Thesis advisor) / Turaga, Pavan (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2023