Movement Dynamics in Elite Female Soccer Athletes: The Quantile Cube Approach

This paper presents an innovative adaptation of existing methodology to investigate external load in elite female soccer athletes using GPS-derived movement data from 23 matches. We developed a quantitative framework to examine velocity, acceleration, and movement angle across game halves, enabling transparent and meaningful performance insights. By constructing a quantile cube to quantify movement patterns, we segmented athletes' movements into distinct velocity, acceleration, and angle quantiles. Statistical analysis revealed significant differences in movement distributions between match halves for individual athletes. Principal Component Analysis (PCA) identified anomalous games with unique movement dynamics, particularly at the start and end of the season. Dirichlet-multinomial regression further explored how factors like athlete position, playing time, and game characteristics influenced movement profiles. This approach provides a structured method for analyzing movement dynamics, revealing external load variations over time and offering insights into performance optimization. The integration of these statistical techniques demonstrates the potential of data-driven strategies to enhance athlete monitoring in soccer.