Data-Driven Softball Success: Leveraging Python and Database Management to Develop Predictive and Analytical Tools for Coaches; Analyzing Financial Differences in College Sports Through Machine Learning
Leonard, Joseph, School of Engineering and Applied Science, University of Virginia
Francisco, Pedro Augusto, EN-Engineering and Society, University of Virginia
Vrugtman, Rosanne, EN-Comp Science Dept, University of Virginia
Due to the lack of resources and research, college softball coaches face challenges in areas like player recruitment and game strategy. At Triple Crown Sports, I used Python and R to create various tools meant to drive on-field performance and allow coaches to see the game in a different light. When coaches saw the different dashboards made from the numerous statistics and metrics, the data and findings became much more digestible, making it easier to make changes on the field. Schools like the University of Tennessee and the University of Oklahoma, which play at the highest level, both finished in the top 5 rankings for the 2024 season with the help of our consulting tools. My future work will shift to writing scripts that will make the scraping of public data much easier.
BS (Bachelor of Science)
Data, College Sports, Machine Learning, Algorithms, Feminism
School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Rosanne Vrugtman
STS Advisor: Pedro Francisco
English
2025/05/07
