The aim of this study was to update the metabolic power (MP) algorithm (PV˙O2, W·kg^-1) related to the kinematics data (P_GPS, W·kg^-1) in a soccer-specific performance model. For this aim, seventeen professional (Serie A) male soccer players (V˙O2max 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 (C_r). 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 (P_GPSn) and compared with both the commonly used (P_GPSo) equation and direct measurement (PV˙O2). Both P_GPSn and P_GPSo correlated with PV˙O2 (r = 0.66, p < 0.05). Estimates of fixed bias were negligible (P_GPSn = -0.80 W·kg^-1 and P_GPSo = -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 P_GPSn and 0.01 W·kg^-1 for P_GPSo) 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 P_GPSn, while P_GPSo routinely underestimated MP across ranges. Repeated-measures ANOVA showed differences over MP conditions (F_1,38 = 16.929 and p < 0.001). Following Bonferroni post hoc test significant differences regarding the MP between P_GPSo and PV˙O2/P_GPSn (p < 0.001) were established, while no differences were found between PV˙O2 and P_GPSn (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.

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