Brian Macdonald, Gregory J. Matthews, and Jun Yan
Photo courtesy of Jiafeng Sun
The fifth UConn Sports Analytics Symposium was held April 12–13 at the University of Connecticut, drawing 216 registrants and 149 in-person attendees. Organized by the Connecticut Statistical Data Science Lab, the symposium continued its mission of engaging students at various educational levels—from pre-college to graduate—in sports analytics and data science. The revamped event offered a comprehensive program designed to showcase the field’s interdisciplinary impact while fostering collaboration between students, academic programs, and the sports industry.
Keynote and Invited Presentations/Panel
The symposium opened with a keynote presentation from Esteban Navarro Garaiz, technical product manager at Zelus Analytics, titled “Baseball Analytics: Past, Present, and Beyond,” which offered insights about the evolution of baseball data analysis and its impact on the sport.
Kristin Morgan, assistant professor of biomedical engineering at the University of Connecticut, followed with a presentation titled “Interdisciplinary Data-Driven Approach to Improve Player Recovery and Performance,” which highlighted the convergence of biomechanics, data science, and health care in sports analytics.
The final speaker was Olympic, world, and US champion figure skater Nathan Chen, a recent statistics and data science graduate from Yale University. In his closing keynote presentation, titled “Designing the Optimal Figure Skating Program: Leveraging Data for a Competitive Edge,” Chen discussed how data science principles can be applied to create optimal figure skating routines, combining athleticism with statistical precision.
The symposium’s panel discussion, “Sport Analytics for Life: Many Different Paths,” featured experts from across the sports industry, including Sean Ahmed, Pittsburgh Pirates; Luke Benz, Harvard University; Sean Fischer, Cincinnati Reds; Paul Sabin, University of Pennsylvania Wharton School; and Emily Wright, Volleyball Canada Beach National Teams. They shared career insights and diverse perspectives on sports analytics, illustrating the field’s career paths for aspiring analysts.
The symposium also featured the following four invited sessions that focused on a wide array of topics in sports analytics:
- “Athlete Welfare Research Organized by Korey Stringer Institute of UConn”
- “Olympic Sports”
- “Big Data Bowl Finalists”
- “Sports Analytics Beyond the Field”
These sessions focused on specific issues and innovations, offering participants the opportunity to learn from industry and academic experts.
Data Challenge and Poster Session
A highlight of the symposium was the US Olympic and Paralympic Committee Data Challenge, facilitated by Elliot Schwartz, committee performance innovation lead. More than 30 teams submitted solutions aimed at optimizing Team USA’s gymnastics team success for the 2024 Summer Olympics, held in Paris, France, in August. Six finalist teams presented their work during the symposium’s poster session, with Duke University’s Team Blue Devil Statistics Magicians (Benjamin Thorpe, Sean Li, and Christopher Tsai) winning the challenge in the high school/undergraduate division and Yale University’s Team David-Siddharth-Abby (David Metrick, Siddharth Chandrappa, and Abby Spears) winning the challenge in the graduate division.
The student poster session attracted a wide range of submissions. Presenters were supported by a travel grant from the National Science Foundation to attend the symposium. The Student Poster Award winners were Min Sung Choi of Yonsei University for his analysis of NBA players’ passing and playmaking skills and Adam Slivinsky of the University of California at Santa Cruz for his work on evaluating Major League Baseball umpire performance using statistical neural networks.
Both the US Olympic and Paralympic Committee Data Challenge and student poster awards were evaluated by large judging teams of industry professionals, academic professionals, and practitioners.
Hands-On Training Workshops
Another feature of the symposium was an emphasis on student engagement through hands-on workshops. This year, six workshops were led by UConn students, five who were undergraduates in the UConn Data Science Club, further exemplifying the symposium’s commitment to fostering a learning environment driven by student leadership. The workshop sessions covered a range of topics about sports analytics, titled the following:
- “Introduction to R,” Fusheng Yang
- “Introduction to Python,” Charitarth Chugh
- “Analysis of Formula 1 Data with Python,” Abhiram Gunt
- “Basketball Analytics,” Mathew Chandy
- “Web Scraping for Sports Data,” Tyler Hinrichs
- “TensorFlow in Sports Analytics,” Hari Patchigolla
- “Causal Inference in Sports Analytics,” Kevin Cummiskey
View workshop recordings and selected presentations.
Looking Ahead to 2025: A New Era
In 2025, the Sports Analytics Symposium will be renamed the Connecticut Sports Analytics Symposium to reflect its broader focus and rotation of host institutions. It will be held April 11–12, 2025, at Yale University.
The 2025 Data Challenge will focus on analyzing data on bat speed and swing length to explore pitcher-batter interactions in Major League Baseball. The challenge, which is open to students only, provides an opportunity for participants to apply their analytical skills in a real-world sports context. Faculty and students are encouraged to incorporate the data challenge into their fall 2024 statistics or data science coursework.
Registration for the challenge will close December 1, and the submission deadline is January 15, 2025. Finalists will be notified by February 15, 2025. Questions may be sent to Brian Macdonald.
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