Optimizing Figure Skating Performance with ML

Figure skating demands extreme precision, yet skaters often rely on trial and error to find their optimal training conditions due to limited competitive opportunities. This Spring 2025 Sigma Xi project explores how data-driven insights can identify a skater's ideal conditions for executing spins, jumps, and full programs. Using CatBoostRegressor and SHAP (SHapley Additive exPlanations), the study analyzes individual training factors to produce actionable, personalized recommendations for each skater.

Collaborator: Elizabeth Wangley