Longitudinal Analysis of Training Load Across Two Competitive Seasons in Collegiate Women’s Soccer Original Research

Abstract

Introduction: Monitoring training load is essential for optimizing performance and supporting long-term adaptation in team sport athletes. While heart rate and GPS technologies are widely used in applied settings, limited longitudinal data exist describing how internal and external load interact across multiple seasons in collegiate women’s soccer.

Methods: A retrospective longitudinal cohort design was used to examine NCAA Division II women’s soccer athletes monitored across the 2023 and 2024 competitive seasons. Internal training load was assessed using heart rate–derived metrics from a FirstBeat monitoring system, including Training Impulse (TRIMP), average and maximal heart rate, and percentage of time spent above 85% HRmax. External load was quantified using GPS measures, including total distance, high-speed running distance, sprint count, sprint distance, and work rate. Data from training sessions and matches were aggregated by season. Linear mixed effects models evaluated seasonal differences with the athlete treated as a random effect.

Results: Internal training load remained stable across seasons, with no significant differences in mean session TRIMP (2023: 308.0 ± 74.6 AU; 2024: 309.7 ± 71.2 AU; p = 0.68) or average exercise intensity (71.2 ± 6.4 vs. 71.5 ± 6.1% HRmax; p = 0.61). In contrast, match play imposed substantially greater external demands than training in both seasons (p < 0.001), with large to very large effect sizes across all GPS variables. Despite increased external demands, the TRIMP to distance ratio decreased from 64.1 to 57.2 TRIMP/mile⁻¹, indicating improved physiological efficiency.

Conclusions: Collegiate women’s soccer athletes demonstrated stable internal load despite large competition demands, supporting effective long-term load regulation and adaptation. Integrating heart rate and GPS metrics provides a comprehensive framework for monitoring training stress and informing evidence-based periodization strategies.