Title: Designing the Optimal Figure Skating Program: Leveraging Data for a Competitive Edge
Abstract: As figure skating continues to evolve into an increasingly complex and competitive sport, athletes and coaches face the challenging task of designing optimal, high-scoring program layouts that will offer a competitive edge. This complexity is driven by the need to balance technical difficulty with artistic expression, all while adhering to strict judging criteria. To demystify this process and aid in strategic planning, we embarked on an analysis of figure skating performances and elements, including jumps, spins, and step sequences. Our analysis involves a dataset comprising over 12,000 elements from various elite international competitions, collected from official scoresheets. Employing regression and Bayesian methods to evaluate jump success probabilities, as well as mixed-effects regression models to predict score outcomes, we evaluated variables encompassing jump types, sequences, scores, and performance contexts. We were able to identify important predictors of jump success, as well as predict scores to develop a model capable of determining the most advantageous jump layouts and element sequences, providing actionable insights for skaters to plan their programs.
Nathan Chen Nathan Chen is a senior at Yale University, majoring in Statistics and Data Science. He is the 2022 Olympic champion, 2018 Olympic bronze medalist, three-time World champion, and six-time U.S. national champion in men’s figure skating. He has been recognized on the Time100 and in the Forbes 30 Under 30 list for his skating achievements. At Yale, he works at the Cardiovascular Research Center, analyzing genomic data to better understand the impact of variants of known and uncertain significance on cardiovascular outcomes. In his free time, he enjoys spending time with family and friends, exploring new food and drink spots, and relaxing anywhere with a nice view.