“…Moreover, the use of substitute players was not analyzed as an independent variable; therefore, future studies could investigate if these players perform greater external load than starters. Finally, a recent scoping review reported that the use of individualized players' speed thresholds (e.g., sprinting speed or maximal aerobic speed) could be helpful in training load monitoring (17). Future studies could investigate if the use of individualized thresholds, for instance, using the peak speed (e.g., recorded by GNSS) could give additional insights into the monitoring of physical performance in professional football.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, the use of substitute players was not analyzed as an independent variable; therefore, future studies could investigate if these players perform greater external load than starters. Finally, a recent scoping review reported that the use of individualized players' speed thresholds (e.g., sprinting speed or maximal aerobic speed) could be helpful in training load monitoring (17). Future studies could investigate if the use of individualized thresholds, for instance, using the peak speed (e.g., recorded by GNSS) could give additional insights into the monitoring of physical performance in professional football.
Practical Applications
This study verified that physical parameters were different between players' positions during official matches in English League 1 football, in particular, CB were the players who covered the lower amount of distance, HSR, sprinting distance, accelerations, decelerations, and HMLD.…”
Beato, M, Youngs, A, and Costin, AJ. The analysis of physical performance during official competitions in professional English football: Do positions, game locations, and results influence players’ game demands? J Strength Cond Res XX(X): 000–000, 2023—This study aimed, first, to verify if physical parameters were different between players' positions during official matches in English professional football, and second, if the game location (H and A) or results (W, L, D) affected players' physical performance. Twenty-five male professional football players of the same team were included in this data analysis (age = 27 ± 9 years) during the 2022–23 season. Players were divided into positions: center backs, wide backs, center midfielders, attacking midfielders, and strikers. The external load parameters were distance covered, high-speed running (HSR), sprinting distance, accelerations, decelerations, and high metabolic load distance (HMLD, meters > 25.5 w·kg−1) that were monitored using global navigation satellite systems Apex (STATSports). Linear mixed models' analysis for positions reported a significant difference in total distance (p = 0.011), HSR (p < 0.001), sprinting distance (p < 0.001), accelerations (p = 0.003), decelerations (p = 0.002), and HMLD (p < 0.001). Centre backs showed the lowest physical performance in the metrics analyzed, whereas players in the other positions frequently displayed a similar physical performance. Regarding locations and results, differences were only found between locations for decelerations (p = 0.041) and between results for HMLD (p = 0.010). In conclusion, physical performance was influenced by players' positions, whereas game location or results seem to not affect physical performance during official competitions. Consequently, practitioners can physically prepare their players independently from the location of the match or of the possible game results, whereas specific positional training is requested to optimally prepare their players.
“…Moreover, the use of substitute players was not analyzed as an independent variable; therefore, future studies could investigate if these players perform greater external load than starters. Finally, a recent scoping review reported that the use of individualized players' speed thresholds (e.g., sprinting speed or maximal aerobic speed) could be helpful in training load monitoring (17). Future studies could investigate if the use of individualized thresholds, for instance, using the peak speed (e.g., recorded by GNSS) could give additional insights into the monitoring of physical performance in professional football.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, the use of substitute players was not analyzed as an independent variable; therefore, future studies could investigate if these players perform greater external load than starters. Finally, a recent scoping review reported that the use of individualized players' speed thresholds (e.g., sprinting speed or maximal aerobic speed) could be helpful in training load monitoring (17). Future studies could investigate if the use of individualized thresholds, for instance, using the peak speed (e.g., recorded by GNSS) could give additional insights into the monitoring of physical performance in professional football.
Practical Applications
This study verified that physical parameters were different between players' positions during official matches in English League 1 football, in particular, CB were the players who covered the lower amount of distance, HSR, sprinting distance, accelerations, decelerations, and HMLD.…”
Beato, M, Youngs, A, and Costin, AJ. The analysis of physical performance during official competitions in professional English football: Do positions, game locations, and results influence players’ game demands? J Strength Cond Res XX(X): 000–000, 2023—This study aimed, first, to verify if physical parameters were different between players' positions during official matches in English professional football, and second, if the game location (H and A) or results (W, L, D) affected players' physical performance. Twenty-five male professional football players of the same team were included in this data analysis (age = 27 ± 9 years) during the 2022–23 season. Players were divided into positions: center backs, wide backs, center midfielders, attacking midfielders, and strikers. The external load parameters were distance covered, high-speed running (HSR), sprinting distance, accelerations, decelerations, and high metabolic load distance (HMLD, meters > 25.5 w·kg−1) that were monitored using global navigation satellite systems Apex (STATSports). Linear mixed models' analysis for positions reported a significant difference in total distance (p = 0.011), HSR (p < 0.001), sprinting distance (p < 0.001), accelerations (p = 0.003), decelerations (p = 0.002), and HMLD (p < 0.001). Centre backs showed the lowest physical performance in the metrics analyzed, whereas players in the other positions frequently displayed a similar physical performance. Regarding locations and results, differences were only found between locations for decelerations (p = 0.041) and between results for HMLD (p = 0.010). In conclusion, physical performance was influenced by players' positions, whereas game location or results seem to not affect physical performance during official competitions. Consequently, practitioners can physically prepare their players independently from the location of the match or of the possible game results, whereas specific positional training is requested to optimally prepare their players.
“…However, these predetermined thresholds may not accurately reflect the real demands of experienced basketball players due to the maximum speed registered in competition being between 18 and 22 km/h [ 3 , 22 ]. In this sense, it is necessary to improve the methodological aspects of using individualized thresholds in team sports [ 23 , 24 ].…”
Currently, basketball teams use inertial devices for monitoring external and internal workload demands during training and competitions. However, the intensity thresholds preset by device manufacturers are generic and not adapted for specific sports (e.g., basketball) and players’ positions (e.g., guards, forwards, and centers). Using universal intensity thresholds may lead to failure in accurately capturing the true external load faced by players in different positions. Therefore, the present study aimed to identify external load demands based on playing positions and establish different intensity thresholds based on match demands in order to have specific reference values for teams belonging to the highest competitive level of Spanish basketball. Professional male players (n = 68) from the Spanish ACB league were monitored during preseason official games. Three specific positions were used to group the players: guards, forwards, and centers. Speed, accelerations, decelerations, impacts/min, and player load/min were collected via inertial devices. Two-step clustering and k-means clustering categorized load metrics into intensity zones for guards, forwards, and centers. Guards covered more distance at high speeds (12.72–17.50 km/h) than forwards and centers (p < 0.001). Centers experienced the most impacts/min (p < 0.001). Guards exhibited greater accelerations/decelerations, albeit mostly low magnitude (p < 0.001). K-means clustering allowed the setting of five zones revealing additional thresholds. All positions showed differences in threshold values (p < 0.001). The findings provide insights into potential disparities in the external load during competition and help establish position-specific intensity thresholds for optimal monitoring in basketball. These data are highly applicable to the design of training tasks at the highest competitive level.
“…To the best of our knowledge, there are limited previous comparisons of external load resulting from arbitrary and individualized speed thresholds within the typical microcycle in professional soccer players [ 25 , 26 ]. Furthermore, no previous research has analysed possible discrepancies between arbitrary and individual speed thresholds, as determined using V IFT .…”
The aims of this study were to: (a) determine the differences in external load quantification between arbitrary and individual speed thresholds over the weekly microcycle in professional soccer players, and (b) analyse the association between internal load and different external load quantification strategies (ELQSs). Ten professional outfield players were monitored during training sessions and official matches using 10 Hz GPS devices over a 6-week in-season period. The absolute and relative (“R” before the distance category) distances covered were calculated for the following external load variables: medium-intensity running distance (MIR), high-intensity running (HIR), sprint distance (SD), and very high-intensity running (VHIR). Individualized thresholds were determined based on maximal sprinting speed (MSS) and the last speed achieved during the 30–15 Intermittent Fitness Test (V
IFT
) of each player. In terms of match-day workload, significant differences (
p
< 0.05) were observed between arbitrary and individualized strategies (i.e., MSS and V
IFT
) for the distance covered in MIR, HIR, SD, VHIR, RHIR, RSD, and RVHIR. The MSS strategy compared to arbitrary thresholds revealed significant differences (
p
< 0.05) for distance covered in HIR, RHIR, and VHIR during all training sessions. The present results showed that arbitrary thresholds lead to underestimation of external load absolute and relative metrics compared to the MSS strategy throughout the microcycle. The V
IFT
strategy mainly revealed differences in external load quantification regarding MD compared to arbitrary thresholds. Individualized speed threshold strategies did not achieve better associations with internal load measures in comparison with arbitrary thresholds in professional soccer players.
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