GENDER-SPECIFIC PREDICTORS OF VAULT PERFORMANCE IN GYMNASTICS: A MACHINE LEARNING APPROACH

Abstract

This study investigated gender-specific predictors of vault performance in gymnastics by applying machine learning techniques to analyse body composition and run-up dynamics. Data were collected from 27 national-level gymnasts (17 female, 10 male) during the Slovenian Cup competition. The focus on gender-specific predictors stems from fundamental physiological and biomechanical differences between male and female athletes, which influence force production, movement kinematics, and execution mechanics. A deeper understanding of these distinctions enhances the precision of performance modelling and supports the development of targeted, evidence-based training interventions.

Spatiotemporal parameters of the run-up were recorded using the OptoGait system, while body composition was assessed with the Tanita DC-360. Principal Component Analysis (PCA) and Boosting regression models were used to identify key predictors of vault execution scores. These methods were selected for their ability to reduce dimensionality and capture complex, nonlinear relationships in performance data. The results revealed clear gender-specific patterns. For female gymnasts, the model explained 74.4% of the variance in execution scores, with Overall Lean Body Mass emerging as the most influential predictor (47.12% relative influence), followed by Overall Contact Phases (25.28%). For male gymnasts, the model demonstrated exceptionally high predictive power, explaining 97.8% of the variance, with Body Fat as the primary predictor (48.44% relative influence), followed by Flight and Contact Dynamics (35.22%). These findings suggest that training strategies should be tailored to gender-specific needs. For women, emphasis on lean muscle development, stride optimisation, and the coordination of rhythm and timing may be beneficial. For men, managing body fat levels, optimising flight and contact dynamics, and adopting an integrated approach to stride mechanics appear essential. Given the potential for misinterpretation of body composition metrics, a holistic approach to athletic conditioning is recommended. However, the study’s limitations, including the small sample size and cross-sectional design-warrant cautious interpretation. This research provides a foundation for future investigations into gender-specific factors affecting vault performance. Larger and longitudinal studies are needed to validate these findings and support the development of more precise training interventions.