Connecticut Sports Analytics Symposium (CSAS) 2026

• Data/Time: April 10-11 (Friday and Saturday), 2026
• Location: University of Connecticut, Storrs, CT
• A pdf flyer
• A form to add your data science club to the master list on statds.org

Congratulations to Data Challenge Awardees

• High school/undergraduate division
  • Aaron Lin (Ladue Horton Watkins School): Optimal Timing of the Power Play in Mixed Doubles Curling
• Graduate division
  • Trey Elder, Samen Hossain, and Jonathan Pipping (University of Pennsylvania): A Win Probability Strategy for the Power Play in Mixed Doubles Curling

Congratulations to Poster Awardees (In random order)

• Gordan Tao and Aneesh Sallaram (University of North Carolina at Chapel Hill, SAIL): GHOST: A Novel Deep Learning Framework for Quantifying NFL Receiver Gravity
• Everett B. Hargrove (Iowa State University): Professional Disc Golf Player Ratings, Predictions, and Event Probability Estimation
• Alex Susi (Georgia Institute of Technology): A Two-Stage Approach to Quantifying Scoring Talent in the NBA
• Andrew Kang (Carnegie Mellon University): Imagining the Intended Shot: Separating Decision-making from Execution with Physics-based Monte Carlo Simulation for Curling

About CSAS

Many established sports analytics conferences remain difficult for students to access due to high technical barriers, costs, or capacity constraints. The Connecticut Sports Analytics Symposium (CSAS) was created as a direct response to this gap, building on the UConn Sports Analytics Symposium (UCSAS) launched in 2019 and expanding its scope and reach. CSAS was intentionally designed to engage participants at all educational levels, including high school, undergraduate, and graduate, who are interested in sports analytics or, more broadly, data science. The event aims to:

• showcase sports analytics at an accessible level;
• provide hands-on training in data analytics using sports data;
• foster collaboration between academia and the sports industry.