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.

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 **** 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 *** billion euro. Further information about football in Europe can be found in the Dossier: UEFA.

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.

These two datasets were created to predict the World Cup. Web scraping is used to collect data from two websites. One of these datasets comes from sofifa.com, and it shows the overall, attack, mid, and defense scores for each national team based on FIFA Game analysis. Another is from national-football-teams.com, which provides us with the average age of players. from 2007 to 2022.

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.

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.

• 2021-2022 Football Player Stats
• 2021-2022 Football Team Stats
• 2022-2023 Football Team Stats

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, t...

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 **** years.

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†.

Estimated marginal mean values representative of the average time on ball and the relative frequency of ball touches and releases per minute of match duration, performed by professional soccer players in a match.

Women's Football (European Leagues)

Team and Player Performance Statistics

By [source]

About this dataset

This dataset includes comprehensive female football-related performance data and player statistics from the top 5 European leagues: Serie A in Italy, Liga Femenina in Spain, Women's Super League in England, Bundesliga Frauen in Germany, and Division 1 Feminin in France. Gathered throughout each season of the respective leagues, the dataset tracks teams, players, matches and a range of important performance metrics. The recently released data provides intriguing insight into team success and player form - covering parameters such as goals scored per game (xGHome), clean sheets (CS), number of opponents' passes allowed (Sweeper_#OPA) as well as individual performance stats such as tackles made per goal kick (Crosses_Stp). Analyze this insightful data to gain further insight on how female football is developing across Europe's major leagues!

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For more datasets, click here.

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How to use the dataset

This dataset can be used to analyze and compare the performance of teams and players across the top five European leagues: Serie A in Italy, Liga Femenina in Spain, Women's Super League in England, Bundesliga Frauen in Germany, and Division 1 Feminin in France. The dataset provides records of each individual match that occurred within these leagues during the tracked season(s), as well as a range of performance metrics for both teams and players.

To use this dataset effectively it is important to understand which columns are available, as described above. By exploring different combinations of team-level versus player-level data you will be able to identify correlations between certain performance metrics for teams or players that provide insights about female football success across Europe.

Once you’re ready to start exploring the data there are several approaches you may take from visualizing your data via bar or line graphs with Python Matplotlib or Seaborn packages; correlating team-level versus player-level statistics such as number of wins (W) compared against goalkeeper saves (Saves); or performing more complicated regression analyses on your data that explore how different features like time played (Min) can predict goals scored (Goals_FK). Each approach provides unique insights into trends within female football success.

No matter how you choose to analyze this dataset it is important to note that trendlines may shift from year-to year -- so make sure you use consistent periods when comparing changes between seasons! It is also helpful to break down aggregate results by country when analyzing different trends across Europe so consider running separate analyses for each country instead aggregating them all together at once. Using this stepwise approach we hope that through careful exploration of the female football success will begin ‘uncovering’!

Research Ideas

• Analyzing the effect of player performance metrics on team success and vice versa: Using this dataset, it is possible to analyze how changes in different player performance metrics might affect overall team performance (e.g. goals scored or allowed, clean sheets). With further analysis, correlations can be drawn between teams’ and players’ performances under different match-day conditions such as travel distance or surface type.

• Examining trends in the development of female football: This data set spans multiple seasons, making it possible to evaluate any general trends in aspects such as the average age of the players across countries and how that affects their performances; or identifying any underused opportunities available for young talented footballers in specific countries which could be benefitted from improvisations by these countries' governing bodies;

• Benchmark positions used among teams versus outside experts’ opinions: One clever use for this dataset can be to compare positional performances between expert opinions from scouts with actual field results from teams using those positions within each country's top leagues and analyzing areas where consensus is reached upon versus discrepancies found throughout the analyzed data samples . For example, one may cross-examine national team call up rosters with squad selections for clubs’ top female divisions - finding anomalies not spotted prior by those making roster decisions - thereby potentially deriving more informed decisions with regards to selecting position holders based on tangible facts rather than focusing merely on biased subjective eye tests over which player should officially take ...

The data was scraped from transfermarkt.com and pertains to the most valuable teams and players in the world of football.

First teams, In this dataset, data about the top 100 teams in the world is collected, and the data is collected based on Rank: team rank. Club: the name of the team. Competition: Name of the league in competition. Squad size refers to the number of team members. Ages: The average age of the players. Market Value: The market value of the team. Players' market value: Players' market worth. MV The top 18 players are as follows: A free market participant's values. MV share: the percentage of MV owned by the team.

Second Players, This dataset collects data on the top 100 players in the world, and the data is collected based on Rank: Player Rank. Name: The name of the player Position: Position of the player in the game Age: Player Age Matches: Total number of matches played Goals: The total number of goals scored. Assists: Total number of Assists scored Yellow_Cards: The total number of yellow cards issued this season. Red_Cards: The total number of red cards issued this season. Substitutions On: Total number for enter as Substitution Substitutions Offs: Total number for get out as Substitution

In the Saudi Pro League (SPL) ******* season, the average age of the incoming, permanent players with the league clubs recorded a seven-year low, at **** years old. In comparison, the average age of the football players joining SPL clubs in the ******* season was **** years.

The subjects included in this study were 18 male soccer players (mean age: 19.84±1.04 years, mean height: 176.41±7.25 cm, mean weight: 72.05±6.40 kg) affiliated with a university club. The top nine [A1] players who accumulated 50% or more of the total playing time in 11 official matches between May and July 2022 were classified into the R group.[A2] The bottom nine players who accumulated less than 50% of the total playing time in 11 official matches were classified into the NR group. Table 1 illustrates the practice and game time for each group. With the official and practice matches combined, the NR group played for approximately 62.9% of the time as compared with the R group. There were no differences in terms of training time between the R and NR groups. This study was conducted in accordance with the principles embodied in the Declaration of Helsinki [A3] and was approved by the Research Ethics Committee of Fukuoka University (approval no.: 22-01-03)[A4] . Additionally, informed consent was obtained from all participants. [A1]Numbers less than 10 are written out unless they are accompanied with a unit [A2]These abbreviations were already defined [A3]I have inserted this statement regarding compliance with the Declaration of Helsinki here in order to better reflect your study’s adherence to Science and Medicine in Football’s policies on research ethics, as expressed in the following underlined instructions from the formatting guidelines of your target journal. Please verify that my insertions here are acceptable for you. "Complying with Ethics of Experimentation Please ensure that all research reported in submitted papers has been conducted in an ethical and responsible manner, and is in full compliance with all relevant codes of experimentation and legislation. All original research papers involving humans, animals, plants, biological material, protected or non-public datasets, collections or sites, must include a written statement in the Methods section, confirming ethical approval has been obtained from the appropriate local ethics committee or Institutional Review Board and that where relevant, informed consent has been obtained. For animal studies, approval must have been obtained from the local or institutional animal use and care committee. All research studies on humans (individuals, samples, or data) must have been performed in accordance with the principles stated in the Declaration of Helsinki. In settings where ethics approval for non-interventional studies (e.g. surveys) is not required, authors must include a statement to explain this." [A4]For transparency, please also indicate the ethics approval number here.

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.

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...

This dataset contains a subset of the data that was collected looking at the inhibition of young amateur soccer players. All participants were male, with an average age of 16.4. Participants performed a stop signal task. The dataset contains anatomical and functional MRI images, and information about reaction times.

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.

Football Player Database – Top 5 European Leagues (Added Season 2025-2026)

A database of players from the top 5 leagues from the 1992-1993 season (Ligue 1 from 1995-1996), excluding goalkeeper statistics, with added columns for UEFA Champions League (UCL) appearances and individual awards. For seasons up to 2017-2018, with limited/reduced statistics. Source: https://fbref.com/en/

Player Info

• PlayerID – Unique identifier for the player
• Player – Player's full name
• Squad – Team/club the player belongs to
• League – League in which the player competes
• Nation – Player's nationality
• Pos – Playing position (e.g., FW, MF, DF)
• Age – Age during the season
• Born – Year of birth
• Season – Season of the data (e.g., 2022-2023)

Playing Time

• MP – Matches played
• Min – Minutes played
• Mn/MP – Minutes per match (average)
• Min% – Percentage of team minutes played
• Starts – Matches started
• Mn/Start – Minutes per start
• Subs – Appearances as a substitute
• Mn/Sub – Minutes per substitute appearance
• unSub – Unsubstituted appearances (played full match)
• 90s – Minutes played expressed in 90-minute units

Shooting / Scoring

• Sh – Total shots
• Sh/90 – Shots per 90 minutes
• SoT – Shots on target
• SoT% – Percentage of shots on target
• SoT/90 – Shots on target per 90 minutes
• G/Sh – Goals per shot
• G/SoT – Goals per shot on target
• Gls – Goals scored
• Ast – Assists
• G+A – Goals plus assists
• PK – Penalties scored
• PKatt – Penalty attempts
• PKcon – Penalties conceded
• OG – Own goals
• xG – Expected goals
• npxG – Non-penalty expected goals
• npxG/Sh – Non-penalty xG per shot
• G-xG – Goals minus expected goals (over- or underperformance)
• np:G-xG – Non-penalty goals minus non-penalty xG

Passing

• Pass – Total passes attempted
• Cmp – Passes completed
• Cmp% – Pass completion percentage
• PassLive – Completed live-ball passes that lead to a shot attempt
• PassDead – Completed dead-ball passes that lead to a shot attempt
• KP – Key passes
• Att – Passes Attempted
• Crs – Crosses attempted
• CrsPA – Crosses that lead to a shot
• A-xAG – Assists minus expected assists from key passes
• xAG – xAG: Exp. Assisted Goals Expected Assisted Goals xG which follows a pass that assists a shot
• xA – Expected assists
• PPA – Passes Penalty Arena
• Live – Live-ball Passes
• Dead – Set-piece passes leading to shots
• FK – Free kicks attempted
• TB – Through balls
• Sw – Switches Passes that travel more than 40 yards of the width of the pitch
• TI – Throw-ins Taken
• CK – Corners
• In – Inswinging Corner Kicks
• Out – Outswinging Corner Kicks
• Str – Straight Corner Kicks
• Compl – Completed progressive passes
• Mis – Misplaced passes

Defensive Actions

• Tkl – Tackles
• TklW – Tackles won
• Tkl% – Tackle success percentage
• Tkld – Tackles attempted in defensive third
• Tkld% – Tackle success in defensive third
• Tkl+Int – Tackles plus interceptions
• Int – Interceptions
• Blocks – Shots blocked
• Clr – Clearances
• Fls – Fouls committed
• Recov – Ball recoveries

Defensive / Positional Coverage

• Def – Defensive actions in total
• Def 3rd – Defensive actions in defensive third
• Mid 3rd – Defensive actions in middle third
• Att 3rd – Defensive actions in attacking third
• Att Pen – Actions in penalty area
• Off – Passes Offside
• Dis – Dispossessions

Duels / Possession

• Won – Duels won
• Won% – Duels win percentage
• Lost – Duels lost

On/Off Metrics

• +/- – Team goal difference when player is on pitch
• +/-90 – Goal difference per 90 minutes
• On-Off – Impact on team goal difference
• onG – Goals scored by team while player is on pitch
• onGA – Goals conceded while player is on pitch
• onxG – Expected goals while on pitch
• onxGA – Expected goals against while on pitch
• xG+/- – xG difference while player is on pitch
• xG+/-90 – xG difference per 90 minutes

Chance Creation / Progressive Play

• SCA – Shot-creating actions
• SCA90 – Shot-creating actions per 90 minutes
• PrgC – Progressive carries
• PrgDist – Progressive distance carried
• PrgP – Progressive passes
• PrgR – Progressive runs
• Rec – Recoveries
• Carries – Ball carries
• CPA – Carries into penalty area
• Touches – Number of touches
• Dist – Total distance covered with the ball
• TotDist – Total distance covered overall
• PPM – Points per Match

Individual Awards

• Ballon d’or – Ballon d’Or wins
• European Golden Shoe – European Golden Shoe wins
• League Won – Domestic league titles won
• UCL_Won – UEFA Champions League titles won
• The Best FIFA Mens Player – FIFA Best Men’s Pla...

All data taken from https://fbref.com/

GitHub to my project: https://github.com/emreguvenilir/fifa23-ml-ratingsystem

There is another statistics dataset here on Kaggle where the data is totally incomplete. So I took the time, mainly because of a final school project, to download the raw data from R. I then cleaned the data to the specifics of my project. The data contains only players from the big 5 leagues (prem, la liga, bundesliga, ligue 1, serie a.)

Column Description

squad: The team of a given player

comp: The league of the team, only includes the “big 5”

player: player name

nation: nationality of the player

pos: position of the player

age: age of the player

born: year born

MP: matches played

Minutes_Played: minutes played in the season

Mn_per_MP: minutes per match played

Mins_Per_90: minutes per 90 minutes (length of a soccer match)

Starts: matches started

PPM_Team.Success: avg # of point earned by the team from matches in which the player appeared with a minimum of 30 minutes

OnG_Team.Success: goals scored by team while on pitch

onGA_Team.Success: Goals allowed by team while on pitch plus_per_minus_Team.Success: goals scored minus allowed while on pitch

Goals: goals scored

Assists: assists that led to goal

GoalsAssists: goals + assists

NonPKG: non penalty kick goals

PK: penalty kicks made

PKatt: penalties attempted

CrdY: yellow cards

CrdR: red cards

xG: expected goals based on all shots taken

xAG: expected assisted goals

npxG+xAG: non penalty expected goals + assisted goals

PrgC: progressive carries in the attacking half of the pitch and went at least 10 yards

PrgP: progressive carries in the attacking half of the pitch and went at least 10 yards

Gls_Per90: goals per 90 minutes

Ast_Per90: assists per 90 minutes

G+A_Per90: goals + assists per 90

G_minus_PK_Per: goals excluding penalties per 90

G+A_minus_PK_Per: goals and assists excluding penalties per 90

xG_Per: xG per 90

xAG_Per: xAG per 90

xG+xAG_Per: xG+xAG per 90

Shots: shots taken

Shots_On_Target: shots on goal frame

SoT_percent: sh/SoT * 100

G_per_Sh: goals per shot taken

G_per_SoT: goal per shot on target

Avg_Shot_Dist: avg shot dist

FK_Standard: shots from free kicks

G_minus_xG_expected: goals minus expected goals

np:G_minus_xG_Expected: non penalty goals minus expected goals

Passes_Completed: passes completed

Passes_attempted: passes attempted

Passes_Cmp_percent: pass completion percentage

PrgDist_Total: progressive pass total distance

Passes_Cmp_Short: short passes completed (5 to 15 yds)

Passes_Att_Short: short passes Attempted (5 to 15 yds)

Passes_Cmp_Percent_Short: short passes completed percentage (5 to 15 yds)

Passes_Cmp_Medium: medium passes completed (15 to 30 yds)

Passes_Att_medium: medium passes Attempted (15 to 30 yds)

Passes_Cmp_Percent_Medium: medium passes completed percentage (15 to 30 yds)

Passes_Cmp_long: long passes completed (30+ yds)

Passes_Att_long : long passes Attempted (30+ yds)

Passes_Cmp_Percent_long : long passes completed percentage (30+ yds)

A_minus_xAG_expected: assists minus expected assists

Key_Passes: passes that lead directly to a shot

Final_third: passes that enter the final third of the field

PPA: passes into the penalty area

CrsPA: crosses into penalty area

TB_pass: through ball passes

Crs_Pass: number of crosses

Offside_passes: passes that resulted in an offside

Blocked_passes: passes blocked by an opponent

Shot_Creating_Actions: shot creating actions

SCA_90: shot creating actions per 90

TakeOnTo_Shot: take ons that led to shot

FoulTo_Shot: fouls draw that led to shot

DefAction_Shot: defensive actions that led to a shot (pressing)

GoalCreatingAction: goal creating actions

GCA90: goal creating actions per 90

TakeOn_Goal: take ons that led to a goal

Fld_goal: fouls drawn that led to a goal

DefAction_Goal: defensive actions that led to a goal (pressing)

Tackles: number of tackles made

Tackles_won: tackles won

Def_3rd_Tackles: tackles in the defensive 1/3 of the pitch

Mid_3rd_Tackles: tackles in the middle 1/3 of the pitch

Att_3rd_Tackles: tackles in the attacking 1/3 of the pitch

Tkl_percent_won: % of dribblers tackled

Lost_challenges: lost challenges, unsuccessful attempts to win the ball

Blocks: # of times blocking the ball by standing in path

Sh_blocked: shots blocked

Passes_blocked: number of passes blocked

Interceptions: interceptions

Clearances; clearances

ErrorsLead_ToShot: errors made leading to a shot

Att_Take: attacking take ons attempted

Succ:Take: attacking take ons successful

Succ_percent_take: percentage of attacking take ons successfully

Tkld_Take: times tackled during a take on

Tkld_percent_Take: percentage of times tackled during a take on

TotDist_Carries: total distance carrying the ball in any direction

PrgDist_carries: progressive carry distance total

Miscontrolls: # of times a player...

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 **** years. Further information about football in Europe can be found in the Dossier: UEFA.