Task-Dependent Load, High Variability and Multidimensional Structure of Goalkeeper Training: An Inertial Sensor-Based Case Study in Women’s Football

The integration of wearable inertial sensors into sports performance analysis has enabled the automatic quantification of complex movement patterns, particularly in highly specialized roles such as the goalkeeper. However, the ability of sensor-derived metrics to characterize and differentiate goalkeeper-specific actions across different training contexts remains limited. This pilot study adopted a data-driven, multilevel approach to analyze external load and specific actions in a semi-professional female football goalkeeper across 39 training sessions comprising 148 tasks. The aims were: (i) to characterize external load profiles according to task type, (ii) to identify differences in mechanical demands across training contexts, and (iii) to determine the latent structure of goalkeeper-specific load. Descriptive analyses, coefficient of variation (CV), Kruskal–Wallis tests with post hoc comparisons, and principal component analysis (PCA) were conducted. The results revealed a clear task-dependent load gradient, with goalkeeper-specific tasks showing higher values than small-sided games and full-game situations, particularly for variables such as dives/min, dives load/min, and impacts +8/min. High variability was observed, with coefficient of variation values exceeding 100% in several indicators, reflecting the intermittent nature of goalkeeper involvement. Principal component analysis identified a two-component structure explaining 76.6% of the total variance, associated with specific involvement intensity and postural-control/movement-load demands. In this single-case pilot study, the analyzed goalkeeper’s external load was task-dependent, highly variable, and multidimensional. In comparable women’s football contexts, these findings support the use of task-specific inertial monitoring to verify and adjust female goalkeepers’ exposure to key position-specific actions, such as dives, impacts, accelerations/decelerations, and set-position demands, rather than relying exclusively on full-game tasks or generic locomotor indicators.