(65) TRAINING LOAD: EVALUATING A NOVEL FRAMEWORK FOR QUANTIFYING MECHANICAL AND METABOLIC LOAD IN FEMALE SOCCER PLAYERS

Purpose: This study introduces a framework aimed at differentiating mechanical and metabolic stress using acceleration and deceleration metrics. The objective was to observe whether these distinct metrics show correlation between manufacture-assigned player load and the novel load. Methods: A cross-sectional correlational design was employed using female, NCAA Division II soccer athletes (n=22), monitored across a full competitive season. Data was collected during training and competition using Titan GPS tracking harnesses. Metrics of interest were acceleration/deceleration zones and the manufacturer’s calculated player load. Acceleration and deceleration zones were used to calculate a training load score, assigning distinct weights to each zone to estimate mechanical and metabolic loads. Correlation analyses (Pearson's r) assessed relationships between traditional GPS player load metrics and our novel metrics. Additionally, ANOVA was conducted to detect positional differences among defenders (n=7), forwards (n=4), midfielders (n=9), and mixed positions (n=2). Results: Correlation analyses revealed strong, significant relationships between Accel Physiological Load (r = 0.801, (p < 0.001, 95% [0.580, 0.912]), Decel Physiological Load (r = 0.884, p < 0.001, 95% [0.662, 0.932]), Accel Mechanical Load (r = 0.817, p < 0.001, 95% [0.611, 0.920]), Decel Mechanical Load (r = 0.832, p < 0.001, 95% [0.638, 0.926]), and Total Combined Load (r = 0.840, p < 0.001, 95% [0.655, 0.930]) and the traditional method. However, ANOVA results showed no statistically significant positional variations for the refined load metrics (Accel Physiological Load p = 0.344; Decel Physiological Load p = 0.401; Accel Mechanical Load p = 0.312; Decel Mechanical Load p = 0.364; Total Combined Load p = 0.188; Manufacturer-Assigned Player Load p = 0.517). Conclusions: The novel approach to training load demonstrated strong statistical correlations with the traditional method across both individual players and positions, suggesting the traditional and novel metrics can be used interchangeably in data analysis. Despite its theoretical benefits and the comparable usefulness, the novel approach requires additional effort to compute, which may limit its practicality. Practical Applications: A clearer interpretation and usefulness of the training load metric is needed. Distinguishing between the types of stress experienced by players could offer meaningful insight for coaches and players in determining appropriate training and recovery strategies. Acknowledgements: None