Abstract
The aim of this study was to update the metabolic power (MP) algorithm (, W·kg−1) related to the kinematics data (PGPS, W·kg−1) in a soccer-specific performance model. For this aim, seventeen professional (Serie A) male soccer players ( 55.7 ± 3.4 mL·min−1·kg−1) performed a 6 min run at 10.29 km·h−1 to determine linear-running energy cost (Cr). On a separate day, thirteen also performed an 8 min soccer-specific intermittent exercise protocol. For both procedures, a portable Cosmed K4b2 gas-analyzer and GPS (10 Hz) was used to assess the energy cost above resting (C). From this aim, the MP was estimated through a newly derived C equation (PGPSn) and compared with both the commonly used (PGPSo) equation and direct measurement (). Both PGPSn and PGPSo correlated with (r = 0.66, p < 0.05). Estimates of fixed bias were negligible (PGPSn = −0.80 W·kg−1 and PGPSo = −1.59 W·kg−1), and the bounds of the 95% CIs show that they were not statistically significant from 0. Proportional bias estimates were negligible (absolute differences from one being 0.03 W·kg−1 for PGPSn and 0.01 W·kg−1 for PGPSo) and not statistically significant as both 95% CIs span 1. All variables were distributed around the line of unity and resulted in an under- or overestimation of PGPSn, while PGPSo routinely underestimated MP across ranges. Repeated-measures ANOVA showed differences over MP conditions (F1,38 = 16.929 and p < 0.001). Following Bonferroni post hoc test significant differences regarding the MP between PGPSo and /PGPSn (p < 0.001) were established, while no differences were found between and PGPSn (p = 0.853). The new approach showed it can help the coaches and the soccer trainers to better monitor external training load during the training seasons.
Highlights
The application of new technologies has resulted in the evolution of performance analysis providing appropriate means to track, capture and analyse movement characteristics of soccer players [1].Many performance variables are routinely captured during match-play/training, helping determine player activity and assess individual player performance profiles to design personalised and novel training approaches [2,3,4]
The metabolic demand imposed by soccer match-play and training estimates is calculated from energy cost paradigms derived from laboratory models of constant-speed linear running that do not reflect the totality of soccer-related actions [13]
Fitness levels have been shown to influence constant-speed linear-running (Cr) as running economy can be improved through training [19,20], with research on professional soccer players showing a higher Cr by 14% from pre-season compared to in-season [21]
Summary
Many performance variables are routinely captured during match-play/training, helping determine player activity and assess individual player performance profiles to design personalised and novel training approaches [2,3,4]. Match-analysis observations have extensively reported that soccer players typically cover 9–14 km during a game with high-intensity running between 5 and 15%, and running speeds are used as the main parameter for the classification of soccer activity [5,6,7]. The metabolic demand imposed by soccer match-play and training estimates is calculated from energy cost paradigms derived from laboratory models of constant-speed linear running that do not reflect the totality of soccer-related actions [13]. Fitness levels have been shown to influence Cr as running economy can be improved through training [19,20], with research on professional soccer players showing a higher Cr by 14% from pre-season compared to in-season [21]
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