Can machine learning robustly predict grade of execution in figure skating jumps from kinematic features across competitions : a case study of ladies’ double axel at the world championships

In the current figure skating scoring system, a jump’s score is determined as the sum of its base value, which represents difficulty, and the grade of execution (GOE), which reflects execution quality. However, the criteria used to evaluate the GOE allow for subjective judgment by the judges. Consequently, skaters may train without concrete guidelines for maximizing their scores. If execution quality could be robustly predicted based on kinematic characteristics, it could contribute to improving the performance of skaters.　This study examined whether the GOE assigned by judges to double Axel jumps performed by female skaters at the 2019 and 2023 World Championships could be robustly predicted based on kinematic features and investigated which features contributed to these predictions. The results demonstrated that three simple kinematic features—vertical height, horizontal distance, and landing distance—explained 42.9% of the variance in GOE, even when the dataset included performance from different competitions. The mean absolute error of the prediction was 0.528. Although the GOE evaluation criteria mentioned “very good height and length,” vertical height had little impact in practice. Instead, jumps with a greater horizontal distance and landing distance resulted in higher GOE. However, ratio-based derived features, previously shown to be relevant, were not significantly related to GOE, suggesting that their influence was competition-specific rather than consistent across different competitions. This study contributes to understanding the performance evaluation in judged sports from a kinematic perspective, in which scoring is inherently subjective and based on complex criteria.