The aim of this study was to update the metabolic power (MP) algorithm (${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$, 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 ($\dot{\mathrm{V}}{\mathrm{O}}_{2\max }$ 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 (${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$). Both P_GPSn and P_GPSo correlated with ${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$ (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 ${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$/P_GPSn (p < 0.001) were established, while no differences were found between ${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$ 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.

The influence of playing formations and team ranking on the physical performance of professional soccer players is an open question that needs to be explored. The present study aimed to investigate the impact of these factors on the physical exertion of Serie A soccer players. We analyzed match data from 375 players, categorizing teams based on their final ranking and comparing performance across different playing formations. The Kruskal–Wallis test and the Dunn test with Bonferroni adjustment revealed that high-ranking (HR) teams exhibited a higher percentage of high-intensity (HI) accelerations compared to mid-ranking teams, suggesting the critical role of HI efforts in achieving favorable match outcomes. Moreover, the 4-3-3 playing formation was associated with greater acceleration demands than other formations, particularly in HR teams. Our study also established benchmarks for various performance metrics, enabling coaches to assess player performance and identify potential signs of overtraining. These findings contribute to a deeper understanding of the physical demands in soccer and offer practical implications for coaches and players in optimizing training and performance strategies.

Background: Studies assessing post-activation potentiation (PAP) responses comparing male and female athletes are conflicting. Objectives: This study investigated whether differences exist in the duration for optimal post-preload stimulus measures on performance in male and female team sport athletes. Methods: Twenty-four participants (12 males and 12 females) participated in the study. Two familiarization sessions were conducted with each participant. Then, three experimental conditions were implemented, incorporating a standardized warm-up, followed by back squats (conditioning exercises) and varying passive recovery times of 4 min (PAP4), 8 min (PAP8), or 12 min (PAP12). Following the recovery, players performed three physical performance measures related to team sports: A countermovement jump, a modified agility t-test, and a 20-m linear sprint. The significance level was set at P < 0.05. Results: All performance measures were significantly greater in PAP12 than in PAP4 and PAP8 conditions in both males (1.50 to 2.95%) and females (1.09 to 5.79%) (P < 0.05). The PAP12 condition also had significantly lower values for HR (3.18 to 5.15 beats.min-1; P < 0.0005) and ratings of perceived exertion (RPE) (0.63 to 1.02; P < 0.05) than PAP8 and PAP4. Males performed better on all the performance tests (19.33 to 26.34%) compared to their female counterparts (P < 0.0005). Conclusions: A pre-load stimulus consisting of one set of 5 repetitions of back squat at 85% one-repetition maximum can elicit a PAP response. A 12-min passive rest after the pre-load stimulus was most beneficial in improving physical performance measures in both male and female team-sport athletes.

The influence of playing formations and team ranking on the physical performance of professional soccer players is an open question that needs to be explored. The present study aimed to investigate the impact of these factors on the physical exertion of Serie A soccer players. We analyzed match data from 375 players, categorizing teams based on their final ranking and comparing performance across different playing formations. Krustal-Wallies test, and Dunn test with Bonferroni adjustment, revealed that high-ranking (HR) teams exhibited a higher percentage of high-intensity (HI) accelerations compared to mid-ranking teams, suggesting the critical role of HI efforts in achieving favorable match outcomes. Moreover, the 4-3-3 playing formation was associated with greater acceleration demands than other formations, particularly in HR teams. Our study also established benchmarks for various performance metrics, enabling coaches to assess player performance and identify potential signs of overtraining. These findings contribute to a deeper understanding of the physical demands in soccer and offer practical implications for coaches and players in optimizing training and performance strategies.

Physiological characteristics such as VO_2max, running economy (RE), maximal aerobic speed (MAS), maximal sprinting speed (MSS), anaerobic speed reserve (ASR), and player profiling (based on MSS and MAS) have been proven to be important for training prescriptions in football. However, previous studies on player profiling have neglected the absolute values of MSS and MAS. The objectives of this study were to compare the aforementioned physiological variables among player roles, create benchmarks, and provide normative data to help coaches categorize players, ultimately proposing a new player profiling method. We analyzed 195 male professional football players (50 forwards, 59 midfielders, 44 full-backs, and 42 center-backs). Multivariate analysis of variance with Tukey’s post hoc tests revealed positional differences. Center-backs exhibited lower VO_2max than midfielders and full-backs. Both center-backs and forwards showed poorer RE and MAS compared to midfielders and full-backs. Full-backs achieved higher MSS than midfielders and center-backs, and forwards outperformed center-backs. Finally, midfielders demonstrated lower ASR than all other positions. Benchmarks based on T-scores for all variables were provided. Finally, in the new profiling method proposed—also based on T-scores—players were classified as “speed”, “endurance”, or “hybrid” if their MAS and/or MSS T-score exceeded 60, “in development” if both were below 45, and “average” if both scores were between 45 and 60 without any value above 60. The normative data provided can assist coaches in identifying specific areas for improvement in players’ physical conditioning—particularly valuable for youth athletes or those returning from injury. Additionally, the new profiling method offers insights into individual player characteristics, enabling more tailored and effective training interventions.

This study aimed to evaluate the within-system agreement and interchangeability of real-time and post-processed external load metrics in elite football. Data were collected from 50 official Serie A matches using Dynamix (K-Sport World S.R.L., Pesaro, Italy), the platform for acquiring and standardizing tracking inputs. SmartLive, a real-time monitoring module embedded within Dynamix, was compared with post-processed data from the league-approved optical tracking provider (Hawk-Eye Innovations Limited, Basingstoke, UK) in Serie A. The external load metrics analyzed included total distance covered; distances at speeds exceeding 15, 20, and 25 km·h⁻¹; distances within the 15–20 km·h⁻¹ and 20–25 km·h⁻¹ ranges; distance covered during accelerations > 2 m·s⁻² and decelerations < −2 m·s⁻²; and peak speed. Intraclass correlation coefficients (ICCs) demonstrated excellent agreement across all metrics, with values ranging from 0.929 to 0.999. Bland–Altman analysis revealed small mean differences between systems, indicating strong agreement. Overall, the findings confirm that both real-time and post-processed data are in close agreement across a wide range of performance metrics. Minor discrepancies were observed in intermediate speed zones and acceleration/deceleration events. This study provides the first validation of SmartLive’s within-system agreement with post-processed data, supporting its use alongside post-processed data in elite football environments.