53 datasets found
  1. Average player age of teams at the World Cup 2022

    • statista.com
    Updated Dec 19, 2022
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    Statista (2022). Average player age of teams at the World Cup 2022 [Dataset]. https://www.statista.com/statistics/1298094/average-player-age-national-teams-qatar-world-cup/
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    Dataset updated
    Dec 19, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    World
    Description

    Iran's squad was the oldest out of any team at the 2022 FIFA World Cup, with an average age of 28.9. This was over four years older than the team with the youngest squad, Ghana, with an average age of 24.7.

  2. Europe: average age of the football players in UEFA league teams 2016

    • statista.com
    Updated Oct 9, 2024
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    Statista (2024). Europe: average age of the football players in UEFA league teams 2016 [Dataset]. https://www.statista.com/statistics/721728/top-division-football-player-average-age-europe/
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    Dataset updated
    Oct 9, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2016
    Area covered
    Europe
    Description

    This statistic displays the average age of football players in the UEFA league teams in Europe in 2016, by country. As of January 2016, the average age of the football players in German top tier league teams was 25.4 years. UEFA is the administrative body for the union of the national football associations in Europe. In 2015/2016, UEFA registered a total revenue of 4.6 billion euro. Further information about football in Europe can be found in the Dossier: UEFA.

  3. Average age of players competing in Euro 2021, by team

    • statista.com
    Updated Aug 28, 2023
    + more versions
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    Statista (2023). Average age of players competing in Euro 2021, by team [Dataset]. https://www.statista.com/statistics/1246212/average-age-of-players-competing-european-championship-by-team/
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    Dataset updated
    Aug 28, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 3, 2021 - Jun 11, 2021
    Area covered
    Europe
    Description

    In celebration of the Euro 2020/21 competition, Statista published the Celebrity Index report. The Celebrity Index - Euro 2021 ranked all competing teams and players by their celebrity status. Metrics that are used to weight this ranking include social media following, their transfer market value as well as the sentiment and number of global online news articles that mention any player competing. The Turkish team is the youngest of all teams competing in the Euros in 2021, with an average age of 24.96 years, or 24 years, 11 months, and 17 days.

  4. Average age of Serie A soccer players in Italy as of March 2022, by club

    • statista.com
    Updated May 14, 2024
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    Statista (2024). Average age of Serie A soccer players in Italy as of March 2022, by club [Dataset]. https://www.statista.com/statistics/1040678/average-age-of-serie-a-football-players-italy-by-team/
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    Dataset updated
    May 14, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2022
    Area covered
    Italy
    Description

    As of March 2022, Spice Football was the Serie A soccer club with the youngest average age. On average, its players were roughly 23.5 years old. On the contrary, Inter was the Serie A team with the oldest average age. Its team recorded an average age of over 30 years old.

  5. Latin America: average age of professional footballers 2020-2021, by league

    • statista.com
    Updated Feb 8, 2023
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    Statista (2023). Latin America: average age of professional footballers 2020-2021, by league [Dataset]. https://www.statista.com/statistics/1218774/average-age-soccer-players-latin-american-leagues/
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    Dataset updated
    Feb 8, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    LAC, Latin America
    Description

    In the second semester of 2020 and 2021, the Argentine professional soccer league was the one with the youngest team, among the four major Latin American leagues. Meanwhile, the Mexican league was the the one with the eldest squad in 2021, reporting an average age of 27.8 years.

  6. 2022-2023 Football Player Stats

    • kaggle.com
    Updated Feb 12, 2023
    + more versions
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    Vivo Vinco (2023). 2022-2023 Football Player Stats [Dataset]. https://www.kaggle.com/datasets/vivovinco/20222023-football-player-stats/versions/5
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 12, 2023
    Dataset provided by
    Kaggle
    Authors
    Vivo Vinco
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Context

    This dataset contains 2022-2023 football player stats per 90 minutes. Only players of Premier League, Ligue 1, Bundesliga, Serie A and La Liga are listed.

    Content

    +2500 rows and 124 columns. Columns' description are listed below.

    • Rk : Rank
    • Player : Player's name
    • Nation : Player's nation
    • Pos : Position
    • Squad : Squad’s name
    • Comp : League that squat occupies
    • Age : Player's age
    • Born : Year of birth
    • MP : Matches played
    • Starts : Matches started
    • Min : Minutes played
    • 90s : Minutes played divided by 90
    • Goals : Goals scored or allowed
    • Shots : Shots total (Does not include penalty kicks)
    • SoT : Shots on target (Does not include penalty kicks)
    • SoT% : Shots on target percentage (Does not include penalty kicks)
    • G/Sh : Goals per shot
    • G/SoT : Goals per shot on target (Does not include penalty kicks)
    • ShoDist : Average distance, in yards, from goal of all shots taken (Does not include penalty kicks)
    • ShoFK : Shots from free kicks
    • ShoPK : Penalty kicks made
    • PKatt : Penalty kicks attempted
    • PasTotCmp : Passes completed
    • PasTotAtt : Passes attempted
    • PasTotCmp% : Pass completion percentage
    • PasTotDist : Total distance, in yards, that completed passes have traveled in any direction
    • PasTotPrgDist : Total distance, in yards, that completed passes have traveled towards the opponent's goal
    • PasShoCmp : Passes completed (Passes between 5 and 15 yards)
    • PasShoAtt : Passes attempted (Passes between 5 and 15 yards)
    • PasShoCmp% : Pass completion percentage (Passes between 5 and 15 yards)
    • PasMedCmp : Passes completed (Passes between 15 and 30 yards)
    • PasMedAtt : Passes attempted (Passes between 15 and 30 yards)
    • PasMedCmp% : Pass completion percentage (Passes between 15 and 30 yards)
    • PasLonCmp : Passes completed (Passes longer than 30 yards)
    • PasLonAtt : Passes attempted (Passes longer than 30 yards)
    • PasLonCmp% : Pass completion percentage (Passes longer than 30 yards)
    • Assists : Assists
    • PasAss : Passes that directly lead to a shot (assisted shots)
    • Pas3rd : Completed passes that enter the 1/3 of the pitch closest to the goal
    • PPA : Completed passes into the 18-yard box
    • CrsPA : Completed crosses into the 18-yard box
    • PasProg : Completed passes that move the ball towards the opponent's goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area
    • PasAtt : Passes attempted
    • PasLive : Live-ball passes
    • PasDead : Dead-ball passes
    • PasFK : Passes attempted from free kicks
    • TB : Completed pass sent between back defenders into open space
    • Sw : Passes that travel more than 40 yards of the width of the pitch
    • PasCrs : Crosses
    • TI : Throw-Ins taken
    • CK : Corner kicks
    • CkIn : Inswinging corner kicks
    • CkOut : Outswinging corner kicks
    • CkStr : Straight corner kicks
    • PasCmp : Passes completed
    • PasOff : Offsides
    • PasBlocks : Blocked by the opponent who was standing it the path
    • SCA : Shot-creating actions
    • ScaPassLive : Completed live-ball passes that lead to a shot attempt
    • ScaPassDead : Completed dead-ball passes that lead to a shot attempt
    • ScaDrib : Successful dribbles that lead to a shot attempt
    • ScaSh : Shots that lead to another shot attempt
    • ScaFld : Fouls drawn that lead to a shot attempt
    • ScaDef : Defensive actions that lead to a shot attempt
    • GCA : Goal-creating actions
    • GcaPassLive : Completed live-ball passes that lead to a goal
    • GcaPassDead : Completed dead-ball passes that lead to a goal
    • GcaDrib : Successful dribbles that lead to a goal
    • GcaSh : Shots that lead to another goal-scoring shot
    • GcaFld : Fouls drawn that lead to a goal
    • GcaDef : Defensive actions that lead to a goal
    • Tkl : Number of players tackled
    • TklWon : Tackles in which the tackler's team won possession of the ball
    • TklDef3rd : Tackles in defensive 1/3
    • TklMid3rd : Tackles in middle 1/3
    • TklAtt3rd : Tackles in attacking 1/3
    • TklDri : Number of dribblers tackled
    • TklDriAtt : Number of times dribbled past plus number of tackles
    • TklDri% : Percentage of dribblers tackled
    • TklDriPast : Number of times dribbled past by an opposing player
    • Blocks : Number of times blocking the ball by standing in its path
    • BlkSh : Number of times blocking a shot by standing in its path
    • BlkPass : Number of times blocking a pass by standing in its path
    • Int : Interceptions
    • Tkl+Int : Number of players tackled plus number of interceptions
    • Clr : Clearances
    • Err : Mistakes leading to an opponent's shot
    • Touches : Number of times a player touched the ball. Note: Receiving a pass, then dribbling, then sending a pass counts as one touch
    • TouDefPen : Touches in defensive penalty area
    • TouDef3rd : Touches in defensive 1/3
    • TouMid3rd : Touches in middle 1/3
    • TouAtt3rd : Touches in attacking 1/3
    • TouAttPen : Touches in attacking penalty area
    • TouLive : Live-ball touches. Does not include corner kicks, free kicks, throw-ins, kick-offs, goal kicks or penalty kicks.
    • ToAtt : Number of attempts to take on defenders while dribbling
    • ToSuc : Number of defenders taken on successfully, by dribbling past them
    • ToSuc% : Percentage of take-ons Completed Successfully
    • ToTkl : Number of times tackled by a defender during a take-on attempt
    • ToTkl% : Percentage of time tackled by a defender during a take-on attempt
    • Carries : Number of times the player controlled the ball with their feet
    • CarTotDist : Total distance, in yards, a player moved the ball while controlling it with their feet, in any direction
    • CarPrgDist : Total distance, in yards, a player moved the ball while controlling it with their feet towards the opponent's goal
    • CarProg : Carries that move the ball towards the opponent's goal at least 5 yards, or any carry into the penalty area
    • Car3rd : Carries that enter the 1/3 of the pitch closest to the goal
    • CPA : Carries into the 18-yard box
    • CarMis : Number of times a player failed when attempting to gain control of a ball
    • CarDis : Number of times a player loses control of the ball after being tackled by an opposing player
    • Rec : Number of times a player successfully received a pass
    • RecProg : Completed passes that move the ball towards the opponent's goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area
    • CrdY : Yellow cards
    • CrdR : Red cards
    • 2CrdY : Second yellow card
    • Fls : Fouls committed
    • Fld : Fouls drawn
    • Off : Offsides
    • Crs : Crosses
    • TklW : Tackles in which the tackler's team won possession of the ball
    • PKwon : Penalty kicks won
    • PKcon : Penalty kicks conceded
    • OG : Own goals
    • Recov : Number of loose balls recovered
    • AerWon : Aerials won
    • AerLost : Aerials lost
    • AerWon% : Percentage of aerials won

    Acknowledgements

    Data from Football Reference. Image from Sky Sports.

    If you're reading this, please upvote.

  7. Europe: leading professional football clubs 2019, by average age of players

    • statista.com
    Updated Jun 14, 2023
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    Statista (2023). Europe: leading professional football clubs 2019, by average age of players [Dataset]. https://www.statista.com/statistics/1028857/europe-leading-professional-football-clubs-by-average-player-age/
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    Dataset updated
    Jun 14, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 1, 2019
    Area covered
    Europe
    Description

    This statistic displays the leading professional football clubs in Europe in 2019, by average age of football players. In 2019, Borussia Dortmund had the youngest football squad among the leading professional football clubs. The average age of the players was 25.3 years. Further information about football in Europe can be found in the Dossier: UEFA.

  8. Average age of women in top tier football leagues in Europe 2017-2019

    • statista.com
    Updated Dec 9, 2022
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    Statista (2022). Average age of women in top tier football leagues in Europe 2017-2019 [Dataset]. https://www.statista.com/statistics/1049673/average-age-womens-football-top-division/
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    Dataset updated
    Dec 9, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Europe
    Description

    The average age of players in the highest women's division football leagues of the four European countries presented is very similar in each league. As of 2019, the Women's Super League in England had the highest average age at 26 years and the German Frauen Bundesliga had the lowest at 24.7 years.

  9. f

    Scores (mean ± SD) of the “lower-level” cognitive tasks and EF tasks scores...

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 2, 2023
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    Barbara C. H. Huijgen; Sander Leemhuis; Niels M. Kok; Lot Verburgh; Jaap Oosterlaan; Marije T. Elferink-Gemser; Chris Visscher (2023). Scores (mean ± SD) of the “lower-level” cognitive tasks and EF tasks scores of elite (n = 47) and sub-elite (n = 41) youth soccer players. [Dataset]. http://doi.org/10.1371/journal.pone.0144580.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Barbara C. H. Huijgen; Sander Leemhuis; Niels M. Kok; Lot Verburgh; Jaap Oosterlaan; Marije T. Elferink-Gemser; Chris Visscher
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Scores (mean ± SD) of the “lower-level” cognitive tasks and EF tasks scores of elite (n = 47) and sub-elite (n = 41) youth soccer players.

  10. Data from: General perceptual-cognitive abilities: age and position in...

    • zenodo.org
    • data.niaid.nih.gov
    • +1more
    Updated May 30, 2022
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    Nils Schumacher; Mike Schmidt; Kai Wellmann; Klaus-Michael Braumann; Nils Schumacher; Mike Schmidt; Kai Wellmann; Klaus-Michael Braumann (2022). Data from: General perceptual-cognitive abilities: age and position in soccer [Dataset]. http://doi.org/10.5061/dryad.27635v2
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    Dataset updated
    May 30, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Nils Schumacher; Mike Schmidt; Kai Wellmann; Klaus-Michael Braumann; Nils Schumacher; Mike Schmidt; Kai Wellmann; Klaus-Michael Braumann
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Various studies suggest the importance of sport-specific cognitive and perceptual abilities in soccer. However, the role of general perceptual-cognitive abilities and the relation of age respective to position have not been clarified for soccer in detail. Therefore, it was the objective of the present study to determine the relation of age and position to general perceptual-cognitive abilities. 178 highly talented male soccer players (mean age 16.2, age range 10 to 33 years) were involved. The participants performed computer-based sustained attention and anticipation (using Vienna Test System) tests. 139 subjects (mean age 16.6) took part in visual and acoustic reaction tests (using Talent Diagnostic System). The soccer players, subdivided into age and position groups, were recruited from a youth academy of a professional soccer club and played at the highest and 2nd highest national soccer competition for their age. Group differences were tested using analysis of variance. Correlations were analyzed for age and abilities.

    Significant correlations and group differences were found for age and sustained attention tasks. Significant differences for position groups were observed with regard to acoustic reaction time (ART). Further, we found statistical tendencies for group differences regarding the visual reaction time (VRT), indicating that midfielders outperform defenders and strikers in simple reaction tasks. Improved skills in sustained attention tasks resulted for defenders, who worked faster and more precisely in figural tasks. Regarding general anticipation tasks differences were not found. No group differences were found in basic anticipation tasks. Our study indicates that additional research is needed to further clarify the development of general perceptual-cognitive abilities and position-specific differences in the above abilities of highly talented soccer players.

  11. Average player age of participating national teams at the 2018 World Cup in...

    • statista.com
    Updated May 22, 2024
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    Statista (2024). Average player age of participating national teams at the 2018 World Cup in Russia [Dataset]. https://www.statista.com/statistics/865746/fifa-world-cup-2018-russia-teams-by-average-player-age/
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    Dataset updated
    May 22, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2018
    Area covered
    World
    Description

    While Costa Rica might have crashed out of the 2018 World Cup in the first round having finished bottom of their group, they were able to claim the title of the oldest average team. Their squad had an average age of 29.6 years, almost four years older than the squad of Nigeria, who had the youngest squad at the tournament.
    France’s stars bring home the trophyFrance’s squad was full of superstars such as Kylian Mbappé, Paul Pogba, and Antoine Griezmann, making it the most expensive squad assembled at the 2018 World Cup. The stars did not fail to deliver, winning the World Cup final 4-2 against Croatia. This marked France’s second World Cup title, following the title they won at their home World Cup in 1998. Interestingly, the captain of the 1998 squad, Didier Deschamps, tasted success again in 2018 as he was the coach of the title-winning French team. With an annual salary of 3.5 million U.S. dollars, Deschamps was one of the best-paid managers at the 2018 World Cup, beaten only by Germany’s Joachim Löw, who boasted annual pay of 3.85 million U.S. dollars.

    Getting through the group stages a tall order? With the World Cup being one of the highlights of the sporting calendar, it is no surprise that the players and squads are scrutinized to the finest detail. One of the more curious statistics is the average height of the players at the 2018 World Cup. Serbia’s squad towered over the rest with an average player height of 185.6 centimeters. By contrast, the smallest squad belonged to Saudi Arabia at an average of a comparatively diminutive 176.2 centimeters. As both teams failed to make it past the group stages, it seems that being of an average height was the recipe for success - France’s title winning squad measured up at a distinctly average 180.5 centimeters.

  12. f

    Sample sizes and descriptive statistics (mean ± standard deviation) for...

    • plos.figshare.com
    xls
    Updated Jun 9, 2023
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    Robert M. Malina; Manuel J. Coelho-e-Silva; Diogo V. Martinho; Paulo Sousa-e-Siva; Antonio J. Figueiredo; Sean P. Cumming; Miroslav Králík; Sławomir M. Kozieł (2023). Sample sizes and descriptive statistics (mean ± standard deviation) for chronological age (CA) at prediction, observed maturity offset and predicted maturity offset, predicted ages at PHV and the difference of predicted age at PHV minus observed ages at PHV (criterion) with the original (Mirwald) and modified (Moore) equations at each observation in players classified as advanced, average and delayed based on observed ages at PHV†. [Dataset]. http://doi.org/10.1371/journal.pone.0254659.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 9, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Robert M. Malina; Manuel J. Coelho-e-Silva; Diogo V. Martinho; Paulo Sousa-e-Siva; Antonio J. Figueiredo; Sean P. Cumming; Miroslav Králík; Sławomir M. Kozieł
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Sample sizes and descriptive statistics (mean ± standard deviation) for chronological age (CA) at prediction, observed maturity offset and predicted maturity offset, predicted ages at PHV and the difference of predicted age at PHV minus observed ages at PHV (criterion) with the original (Mirwald) and modified (Moore) equations at each observation in players classified as advanced, average and delayed based on observed ages at PHV†.

  13. Average age of soccer squads at EURO 2020, by nation

    • statista.com
    Updated Dec 9, 2022
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    Statista (2022). Average age of soccer squads at EURO 2020, by nation [Dataset]. https://www.statista.com/statistics/1241550/average-age-euro-2020/
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    Dataset updated
    Dec 9, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Europe
    Description

    The European Championships (EURO) 2020 were postponed by one year as a result of the coronavirus (COVID-19) pandemic and took place in 11 different nations across Europe from June 11 to July 11, 2021. Age differences between the national team squads at the tournament were very marginal on average, but Sweden had the overall oldest squad with an average age of 29.2 years.

  14. f

    Table1_An early start at a professional soccer academy is no prerequisite...

    • frontiersin.figshare.com
    xlsx
    Updated Nov 23, 2023
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    Sebastiaan Willem-Jan Platvoet; Germen van Heuveln; Jos van Dijk; Tom Stevens; Mark de Niet (2023). Table1_An early start at a professional soccer academy is no prerequisite for world cup soccer participation.xlsx [Dataset]. http://doi.org/10.3389/fspor.2023.1283003.s001
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    xlsxAvailable download formats
    Dataset updated
    Nov 23, 2023
    Dataset provided by
    Frontiers
    Authors
    Sebastiaan Willem-Jan Platvoet; Germen van Heuveln; Jos van Dijk; Tom Stevens; Mark de Niet
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Introduction829 players from 32 nations on five continents participated in the 2022 men's World Cup tournament in Qatar. Not much is known about the youth careers of World Cup players from all over the world, especially about the age at which they began playing youth soccer in a professional academy. This study aimed to provide insights in the age national team players participating in World Cup Qatar 2022 started playing for a professional soccer academy and whether their starting age relates to continent and their current playing position (i.e., goalkeepers, defenders, midfielders, and forwards).MethodSystematic online desk research was conducted to determine the age at which World Cup players started playing for professional youth soccer organizations. The median and interquartile ranges were expressed for the starting age in professional youth soccer organizations and the current age at the World Cup. The variables were compared with playing position, the continent of the player's World Cup nation, and the continent on which the player was raised.ResultsThe results reveal that World Cup Qatar 2022 players started playing for professional soccer academies at a median age of 13.2 years (range: 4.2–22.6). In Europe, players started playing for professional youth soccer organizations earlier than players on other continents [χ2 (4) = 142.0, p 

  15. Average player age of participating national teams at the 2014 World Cup in...

    • statista.com
    Updated Jun 5, 2014
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    Statista (2014). Average player age of participating national teams at the 2014 World Cup in Brazil [Dataset]. https://www.statista.com/statistics/303661/fifa-world-cup-2014-brazil-teams-by-average-player-age/
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    Dataset updated
    Jun 5, 2014
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2014
    Area covered
    World
    Description

    The statistic shows a ranking of the participating national teams at the 2014 FIFA World Cup in Brazil by average age of players. The average age of the Germany squad for the World Cup in Brazil is 26.3 years.

    Average player age of the 2014 World Cup squads - additional information

    There were a total of 736 players across 32 teams in the World Cup, picked from domestic leagues in 52 countries. The Cameroon squad had an average age of 26.9, which matched the average age of all of the players at the tournament. This overall average marks a small change from the average of 27 years and 5 months at each of the last three FIFA World Cups. 58 players celebrated their birthday over the course of the tournament, including Argentina’s Lionel Messi, the most valuable player at the World Cup, who turned 27 on 24th June.

    Argentina, who had the fifth most valuable team at the World Cup also went into the tournament with the oldest team on average (28.5 years). This squad included Martín Demichelis, Hugo Campagnaro and Maxi Rodríguez, all of whom were 33 at the start of the tournament, thus making them the joint 32nd oldest players in the World Cup. Ghana had the most youthful squad with the team’s average age standing at 24.9. AC Milan’s Michael Essien, aged 31, was the only squad member over the age of 30.

    The oldest player at the whole tournament was Colombian goalkeeper Faryd Mondragon, aged 43. By coming on as a substitute in the 85th minute of Colombia’s final group game against Japan, he became the oldest player ever to play in a World Cup game at the age of 43 years and 3 days, surpassing the record set by Cameroon’s Roger Milla at the 1994 World Cup in the USA.

    The youngest player at the 2014 tournament was 18 year old Cameroonian forward Fabrice Olinga, although he remained an unused substitute throughout. Had he been selected, Olinga would have become the ninth-youngest player in World Cup history. The youngest-ever is Norman Whiteside, who played for Northern Ireland at Spain 1982 just 41 days after turning 17.

  16. 2018 FIFA World Cup: average age of Latin American soccer teams

    • statista.com
    Updated Jun 15, 2018
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    2018 FIFA World Cup: average age of Latin American soccer teams [Dataset]. https://www.statista.com/statistics/871266/average-age-soccer-teams-latin-america-fifa-world-cup-russia/
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    Dataset updated
    Jun 15, 2018
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Latin America, World, LAC
    Description

    The statistic presents the average age of all Latin American soccer teams participating in the 2018 FIFA World Cup in Russia. Costa Rica was the Latin American soccer team with the oldest average age (29.8 years), followed by Argentina with team players averaging 29.6 years old.

  17. Descriptives (mean, SD in brackets) for the elite players (n = 63) included...

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Tom L. G. Bergkamp; Ruud J. R. den Hartigh; Wouter G. P. Frencken; A. Susan M. Niessen; Rob R. Meijer (2023). Descriptives (mean, SD in brackets) for the elite players (n = 63) included in the study, classified by age category (i.e., team). [Dataset]. http://doi.org/10.1371/journal.pone.0239448.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Tom L. G. Bergkamp; Ruud J. R. den Hartigh; Wouter G. P. Frencken; A. Susan M. Niessen; Rob R. Meijer
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Descriptives (mean, SD in brackets) for the elite players (n = 63) included in the study, classified by age category (i.e., team).

  18. f

    Summary table of the mean values and between maturity grouping effect sizes...

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Calum MacMaster; Matt Portas; Guy Parkin; Sean Cumming; Chris Wilcox; Christopher Towlson (2023). Summary table of the mean values and between maturity grouping effect sizes for U13 and U14 academy soccer players physical fitness characteristics when categorised using an aggregated chronological age group or three different Mirwald, Baxter-Jones [23], Fransen, Bush [24] and Moore, McKay [29] bio-banding (pre-PHV, circa-PHV and post-PHV) methods. [Dataset]. http://doi.org/10.1371/journal.pone.0260136.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Calum MacMaster; Matt Portas; Guy Parkin; Sean Cumming; Chris Wilcox; Christopher Towlson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Summary table of the mean values and between maturity grouping effect sizes for U13 and U14 academy soccer players physical fitness characteristics when categorised using an aggregated chronological age group or three different Mirwald, Baxter-Jones [23], Fransen, Bush [24] and Moore, McKay [29] bio-banding (pre-PHV, circa-PHV and post-PHV) methods.

  19. Average age of Bundesliga players 2010-2024

    • statista.com
    Updated Sep 5, 2024
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    Statista (2024). Average age of Bundesliga players 2010-2024 [Dataset]. https://www.statista.com/statistics/595477/bundesliga-germany-average-age-of-players/
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    Dataset updated
    Sep 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Germany
    Description

    The average player age in Germany's highest football league has generally increased since 2017, reaching 25.9 years in the 2024/25 season. This was over two years older than the average age in 2017/18.

  20. Training load during the 2-day, 1-day and control group.

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Jason Moran; Norodin Vali; Ben Drury; Raouf Hammami; Jamie Tallent; Helmi Chaabene; Rodrigo Ramirez-Campillo (2023). Training load during the 2-day, 1-day and control group. [Dataset]. http://doi.org/10.1371/journal.pone.0277437.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Jason Moran; Norodin Vali; Ben Drury; Raouf Hammami; Jamie Tallent; Helmi Chaabene; Rodrigo Ramirez-Campillo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Training load during the 2-day, 1-day and control group.

Share
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Statista (2022). Average player age of teams at the World Cup 2022 [Dataset]. https://www.statista.com/statistics/1298094/average-player-age-national-teams-qatar-world-cup/
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Average player age of teams at the World Cup 2022

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Dataset updated
Dec 19, 2022
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
World
Description

Iran's squad was the oldest out of any team at the 2022 FIFA World Cup, with an average age of 28.9. This was over four years older than the team with the youngest squad, Ghana, with an average age of 24.7.

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