{"title":"Performance in professional soccer players: normative data and benchmarks from official matches for metabolic power and high-intensity activities.","authors":"Francesco Laterza, Vincenzo Manzi","doi":"10.23736/S0022-4707.24.16186-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The opportunity to compare data among players using normative data and benchmarks may represent a helpful tool for coaches to assess the players' physical performance easily and quickly. This study aimed to create normative data and benchmarks that can be exploited to compare professional soccer players' physical capacity competing in the Premier Division Championship (series A).</p><p><strong>Methods: </strong>Match data from six professional soccer teams competing in the Premier Division championship (Serie A and Italy Cup matches) were collected during the entire season. Players (N.=112) were divided based on the role positioning as follows: forwards and wingers (FW), midfielders (MF), side-backs (SB), and center-backs (CB). All the teams analyzed competed with a 4-3-3 formation, and only players who played for the entire match (85-95 minutes) were considered. The video analysis system STATS SportVU was used to collect the data during official matches. Average metabolic power (AP) was considered to estimate the energy cost. The number of power events (PE), distance (m) covered at more than 25 W/kg (D25), and finally, the distance covered at v>25 km/h (VHS) were considered as high-intensity assessments. Standardized T-scores (scaled from 0 to 100) were calculated for each role and variable.</p><p><strong>Results: </strong>T-score data was used to create performance bands combined with qualitative description (ranging from extremely poor to excellent), and a traffic light system approach was implemented to simplify the data's interpretation.</p><p><strong>Conclusions: </strong>The results could be used to compare different athletes' performance quickly and effectively, to detect symptoms of overtraining, and to give helpful insights to coaches on what the training should be focused on.</p>","PeriodicalId":17013,"journal":{"name":"Journal of Sports Medicine and Physical Fitness","volume":" ","pages":"211-217"},"PeriodicalIF":1.2000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sports Medicine and Physical Fitness","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.23736/S0022-4707.24.16186-5","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/18 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"SPORT SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The opportunity to compare data among players using normative data and benchmarks may represent a helpful tool for coaches to assess the players' physical performance easily and quickly. This study aimed to create normative data and benchmarks that can be exploited to compare professional soccer players' physical capacity competing in the Premier Division Championship (series A).
Methods: Match data from six professional soccer teams competing in the Premier Division championship (Serie A and Italy Cup matches) were collected during the entire season. Players (N.=112) were divided based on the role positioning as follows: forwards and wingers (FW), midfielders (MF), side-backs (SB), and center-backs (CB). All the teams analyzed competed with a 4-3-3 formation, and only players who played for the entire match (85-95 minutes) were considered. The video analysis system STATS SportVU was used to collect the data during official matches. Average metabolic power (AP) was considered to estimate the energy cost. The number of power events (PE), distance (m) covered at more than 25 W/kg (D25), and finally, the distance covered at v>25 km/h (VHS) were considered as high-intensity assessments. Standardized T-scores (scaled from 0 to 100) were calculated for each role and variable.
Results: T-score data was used to create performance bands combined with qualitative description (ranging from extremely poor to excellent), and a traffic light system approach was implemented to simplify the data's interpretation.
Conclusions: The results could be used to compare different athletes' performance quickly and effectively, to detect symptoms of overtraining, and to give helpful insights to coaches on what the training should be focused on.
期刊介绍:
The Journal of Sports Medicine and Physical Fitness publishes scientific papers relating to the area of the applied physiology, preventive medicine, sports medicine and traumatology, sports psychology. Manuscripts may be submitted in the form of editorials, original articles, review articles, case reports, special articles, letters to the Editor and guidelines.