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Within-player associations between GPS-derived external training load and heart rate-based internal training load in
Krisztián Havanecz1, Tímea Téglás2,3, Leonidas Petridis4
1Training Theory and Methodology Research Center, Hungarian University of Sports Science, Budapest, Hungary.
Objectives:
This study examined within-player associations between GPS-derived external training load variables and heart rate-based internal training load during in-season training in elite youth soccer players. A secondary aim was to identify which intensity-based external load variables are independently associated with heart rate (HR) exertion per minute.
Methods:
Eleven under-17 male soccer players were monitored across a nine-week in-season period. 266 player-session observations were included. External training load was assessed using GPS units with integrated inertial sensors, including total distance covered, high-speed running distance, explosive efforts, player load, and their time-normalized derivatives. Internal training load was quantified using heart rate exertion derived from individualized HR zones. Within-player associations were examined using repeated-measures correlations. A linear mixed-effects model was used to examine predictors of heart rate exertion per minute.
Results:
HR exertion showed nearly perfect within-player associations with total distance covered (r = 0.921, p < 0.001) and PlayerLoadTM (r = 0.924, p < 0.001), while large associations were observed for high-speed running distance (r = 0.662, p < 0.001) and explosive efforts (r = 0.621, p < 0.001). Intensity-based variables, including total distance covered per minute and explosive efforts per minute, were significant positive predictors of HR exertion·min - ¹.
Conclusions:
In elite under-17 youth soccer training, cumulative HR exertion primarily reflects accumulated external training volume, whereas HR exertion·min - ¹ is more closely related to locomotor and explosive action intensity. These findings highlight the need to interpret cumulative and intensity-based internal training load metrics separately in within-player training monitoring.
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