This dataset contains detailed data on all footballers from the 2023/24 premier league season

• Player: The name of the player.
• Nation: The player's nationality.
• Pos: The player's position (e.g., forward, midfielder, defender).
• Age: The player's age.
• MP (Minutes Played): Total minutes played by the player.
• Starts: Number of matches the player started.
• Min (Minutes): Total minutes played by the player (this might be the same as MP).
• 90s (90s Played): The equivalent of 90-minute matches played by the player (e.g., 1.5 = 135 minutes).
• Gls (Goals): Total number of goals scored by the player.
• Ast (Assists): Total number of assists made by the player.
• G+A (Goals + Assists): Total number of goals and assists combined.
• G-PK (Goals - Penalty Kicks): Total number of goals scored excluding penalty kicks.
• PK (Penalty Kicks): Number of penalty goals scored by the player.
• PKatt (Penalty Kicks Attempted): Number of penalty kicks attempted by the player.
• CrdY (Yellow Cards): Number of yellow cards received by the player.
• CrdR (Red Cards): Number of red cards received by the player.
• xG (Expected Goals): The expected number of goals from the player's shots.
• npxG (Non-Penalty Expected Goals): Expected goals excluding penalties.
• xAG (Expected Assists): The expected number of assists from the player's passes.
• npxG+xAG (Non-Penalty xG + xAG): Total of non-penalty expected goals and expected assists.
• PrgC (Progressive Carries): Number of times the player carried the ball forward.
• PrgP (Progressive Passes): Number of passes made by the player that moved the ball forward.
• PrgR (Progressive Runs): Number of times the player made runs forward with the ball.
• Gls (Goals): (Repeated, already defined) Total number of goals scored.
• Ast (Assists): (Repeated, already defined) Total number of assists made.
• G+A (Goals + Assists): (Repeated, already defined) Total number of goals and assists combined.
• G-PK (Goals - Penalty Kicks): (Repeated, already defined) Goals scored excluding penalty kicks.
• G+A-PK (Goals + Assists - Penalty Kicks): Total goals and assists minus penalty goals.
• xG (Expected Goals): (Repeated, already defined) Expected number of goals from the player's shots.
• xAG (Expected Assists): (Repeated, already defined) Expected number of assists from the player's passes.
• xG+xAG (Expected Goals + Expected Assists): Total expected goals and assists.
• npxG (Non-Penalty Expected Goals): (Repeated, already defined) Expected goals excluding penalties.
• npxG+xAG (Non-Penalty xG + Expected Assists): Total of non-penalty expected goals and expected assists.

As of February 2022, only 4.4 percent of professional football managers in England were Black, as opposed to 43 percent of Premier League players. Less than two percent of executive and ownership roles were held by Black people.

9 MORE COMPLETE DATASETS FOR SALE ON ETSY (LINKTREE IN BIO)!!!!!

The 2 datasets in this post are composed of 25 different variables, seen below which hold historical data ranging from 1993/94 - 2024/25 (Mid Season). Perfect for understanding the history of the highest level of English football.

Credits to Joseph Buchdahl, X: https://x.com/12Xpert, Web, http://12xpert.co.uk/

1) Date, The date when the match was played

2) Season, The football season in which the match took place (usually spans across two years, e.g., 2023-24)

3) HomeTeam, The team playing at their home stadium

4) AwayTeam, The visiting team

5) FTH Goals, Full Time Home Goals (total goals scored by home team at the end of the match)

6) FTA Goals, Full Time Away Goals (total goals scored by away team at the end of the match)

7) FT Result, Full Time Result (typically shown as H for home win, A for away win, D for draw)

8) HTH Goals, Half Time Home Goals (goals scored by home team at half-time)

9) HTA Goals, Half Time Away Goals (goals scored by away team at half-time)

10) HT Result, Half Time Result (H for home team leading, A for away team leading, D for draw at half-time)

11) Referee, Name of the match official/referee

12) H Shots, Total shots attempted by the home team

13) A Shots, Total shots attempted by the away team

14) H SOT, Home Shots on Target (shots by home team that were on goal)

15) A SOT, Away Shots on Target (shots by away team that were on goal)

16) H Fouls, Number of fouls committed by the home team

17) A Fouls, Number of fouls committed by the away team

18) H Corners, Corner kicks awarded to the home team

19) A Corners, Corner kicks awarded to the away team

20) H Yellow, Yellow cards shown to home team players

21) A Yellow, Yellow cards shown to away team players

22) H Red, Red cards shown to home team players

23) A Red, Red cards shown to away team players

24) Display_Order, A numerical ordering system for displaying the matches (likely used for sorting or presentation purposes)

25) League, The competition or league in which the match was played

Despite being one of the most popular sports in the world, the level of interest in soccer in the United States still remains relatively low. During a 2019 survey, only 13 percent of Hispanic respondents stated that the English Premier League was their favorite European soccer league to follow, while 65 percent of respondents from the same category stated that they did not follow any European soccer league.

Context

This dataset is a collection of basic but crucial stats of the English Premier League 2020-22 season. The dataset has all the Team Stats that played in the EPL and their standard stats such as Team, Refree, xG, xA, Opponent, Captain and more!

Content

• Date : Date listed is local to the match
• Time : Time listed is local to match Venue
• Comp : Type of competition the team is playing
• Round : Round or phase of competition
• Day : Day of Week
• Venue : Home Ground or Away
• Result : Win or Loss of the Team
• gf : Goals For
• ga : Goals Against
• Opponent : The opponent Team
• xg : Expected Goals
• xga : Expected Goals Allowed
• poss : Posession(Calculated as percentage of passed attempted)
• Attendance : Audience for the match
• Captain : Captain of the team
• Formation : Number of players in each row from defender to forwards
• Refree : Refree for the match
• Match report : Report of the match
• Notes : Notes for the match
• sh : Shots total
• sot : Shots on Target
• dist : Average distance (in yards)
• fk : Shots from free kicks
• pk : Penalty kicks made
• pkatt : Penalty kicks Attempted
• Season : Season year

Inspiration:

You can do many things with this dataset 1. Machine Learning Algorithms can be used to predict the Winner of the match 2. Which Team got the most penalty kicks 3. Analysing the Team formations, And Many More......

The possibilities are endless, create a notebook and explore them!

Do upvote if you like this Dataset!

As of 2023, soccer players in the English Premier League were better compensated than many other professional sports leagues, with player salaries amounting to around ** percent of the league's total revenue. Meanwhile, player salaries in North America's Big Four sports leagues hovered around half of each league's revenue, while cricketers in the Indian Premier League only earned ** percent of league revenue.

Mean percentage accuracy per player for corners, crosses, free kicks and passes for each club included in the analysis in a winning, drawing and losing score line state.

Estimated models for free kick accuracy recorded as a percentage.

Football is not only the most popular sport to watch and spectate in the United Kingdom (UK) and England, but also the most popular team sport to participate in. Between November 2023 and November 2024, roughly 2.2 million people in England played the sport. Football nation Being home to not only the biggest football league but the biggest and most successful sports league in the world, the Premier League, England has many football fans who support the sport with famous clubs such as Manchester United, Liverpool FC, Arsenal FC or Manchester City. Champions League Some of these top tier clubs compete in the UEFA Champions League with other high division teams, primarily from the other ’Big Five’ football leagues in Europe, Germany, Spain, Italy and France. In 2023/24, Real Madrid came out as the victor, winning their 15th Champions League title that season.

Estimated models for passing accuracy recorded as a percentage.

Estimated models for corner accuracy recorded as a percentage.

This statistic shows the share of expatriate football players in top divisions clubs in Europe in 2019, by league. In 2019, approximately **** percent of the football players in Premier League clubs in England are expatriates.

As of February 2024, the English Premier League club Brighton & Hove Albion had the highest share of local supporters, with ** percent of its fan base coming from the region. In contrast, Manchester United had the lowest percentage of local supporters, with only ** percent residing in the region.

The English Premier League is the top division of professional football in England. In 2021, just over ** percent of viewers of the EPL in the United Kingdom were male. Meanwhile, ** percent of Premier League viewers were female.

Table Description: Premier League Shooting Statistics

This dataset contains shooting statistics for players in the English Premier League. It includes metrics such as goals scored, shots taken, shot accuracy, expected goals (xG), and more. The data is sourced from FBRef and covers the latest Premier League season.

Column Descriptions:

1. Rk: Index of the player in the list.
2. Player: Name of the player.
3. Nation: Nationality of the player.
4. Pos: Position of the player on the field.
5. Squad: Team the player belongs to.
6. Age: Age of the player at the time of Aug 1st 2023(season start).
7. Born: Birth year of the player.
8. 90s: Number of 90-minute intervals the player participated in.
9. Gls: Total goals scored by the player.
10. Sh: Total shots taken by the player.
11. SoT: Shots on target by the player.
12. SoT%: Shot accuracy percentage.
13. Sh/90: Shots per 90 minutes.
14. SoT/90: Shots on target per 90 minutes.
15. G/Sh: Goals per shot.
16. G/SoT: Goals per shot on target.
17. Dist: Average distance of shots taken by the player.
18. FK: Free kicks taken by the player.
19. PK: Penalty kicks made by the player.
20. PKatt: Penalty kick attempts by the player.
21. xG: Expected goals.
22. npxG: Non-penalty expected goals.
23. npxG/Sh: Non-penalty expected goals per shot.
24. G-xG: Difference between actual goals and expected goals.
25. np:G-xG: Difference between non-penalty actual goals and non-penalty expected goals.
26. Matches: Link to matches played as a str.
27. Birth Month: Month of birth of the player.

Context

I am an avid soccer fan and I thought it would be cool to observe various trends in statistics over the history of the English Premier League.

Content

The statistics of past Premier League Seasons is recorded in an easy to use JSON format. At the moment, I have only uploaded the past 5 seasons, but I will upload more data in the coming days.

The data is organized under different folders, each for a different season. For Premier League Seasons 2017-18 and onward, a lot more detailed data has been collected. The data represents different aspects of the game.

(Note that the italicized data is not available for seasons prior to the 2017-18 season.

• squadStd : Brief overview of statistics throughout the season. Contains data regarding goals scored/conceded, number of players used throughout the season, yellow cards and red cards accumulated through the course of the season, and so on.

• keeperStd : Statistics regarding goalkeepers. Shots faced, shots saved, clean sheets(a game where a keeper does not concede any goals) are all recorded here.

• keeperAdv : A modern goalkeeper does more than just stop shots on goal. They often act like an 11th outfield player, often getting involved in building up attacks by distributing the ball, or coming out of the penalty box to perform a defensive action (such as a tackle, interception, block, or even a tactical foul. The "#OPA" stat describes the number of defensive actions taken outside the penalty area, and the "AvgDist" metric measures the goalkeepers average position (in metres) away from the goal line.

• squadShoot : Actions relating to shooting and goal scoring. No of shots taken, shots on target, shot distance, freekicks and penalties attempted and so on

• squadPass : Stats related to passes. Passes attempted, completed, distance covered by passes etc. Progressive passes are passes which progress the ball towards the opponents goal. A pass towards a team's own goal covers 0 distance in this metric. Also contains assist statistics.

• squadPassTypes : Covers the nitty-gritty details of passes - what part of the body was used to make the pass (head, left foot, right foot). Whether it was a throw in or a dead ball situation (corner, free kick). The height of the pass (ground level, below shoulder height, or above shoulder height). Passes made under pressure, through balls (pass through two defenders), crosses, cross field balls (passes that switch the play across the width of the field). Also covers the outcome of the pass - completed, resulted in offside, went out of bounds, or was blocked or intercepted.

• squadGCA: Goal/Shot Creating Actions. The direct actions that resulted in a shot or goal. These actions include dribbles, passes or fouls drawn.

• squadDef : Defensive actions. Shows tackles attempted, successful tackles and what third of the field the tackle was made. Also shows number of dribblers tackled, times dribbled past, number of pressures, shots/passes blocked and intercepted, errors (events by own team leading to opponents shot).

• squadPossession: Possession play. Dribbles, carries, distance dribbled, nutmegs ( 😆 )

• squadPlayTime : Stats regarding subsitutions.

• squadMisc : Miscellaneous stats. Fouls drawn/committed. Aerial duels won/lost. These might not make much difference in the overall analysis, but are still worth noting.

Note about Expected Stats

Some statistics have an "x" before them: xG, npxG, xA among others. These are advanced metrics which have emerged by advances in match analysis and machine learning. These stats show how a team is expected to perform. xG indicates how many goals a team is expected to score, from the chances they had. xG, for instance, depends on the distance of the shot, the type of shot(free kick, penalty, header, etc). How a team is expected to perform can be vastly different from its actual performance. A team with high xG doesn't necessarily score more goals, it just takes shots from positions where it is highly likely to score from. The actual outcome may be very different, and depends on various external factors such as the position of goalkeeper, whether it took a deflection or not, the quality of the player and goalkeeper (a shot taken from the exact same position by an attacker and a defender could result in different outcomes. Also, better players can convert chances better than others), and luck. Note that these statistics are only available in the recent seasons.

For more info, please refer to Opta's Advanced Metrics.

Acknowledgements

All data was scraped through Football Reference

The relationship between sport, education and social class in Britain is extremely complex and variable across different sports and genders. This statistic presents the share of different educational structures attended by British professional athletes across a range of sporting disciplines. Men's cricket had the highest levels of privately educated professional athletes of the sports included within this statistic, with 43 percent of the English men's cricket team having received a private education. Female cricketers had the lowest rate of comprehensive attendance, with 35 percent having attended private schools. The educational backgrounds of British female rugby union internationals differ to those of the men. Where 37 percent of men's British rugby union internationals having attended private schools, with only 47 percent having attended a state comprehensive. The women's British rugby union internationals educational background was more in line with football but still double the national average, with 82 percent having attended comprehensive schools and 13 percent having received private education.
Football Men’s football has long been a game where professional players leave the education system at an early age, with the pathways to elite level participation largely through the club and league structures. Consequently male football professionals have the lowest rates of privately educated participants within this statistic. The differences between male and female football professionals provides an insight into the differing opportunities for financial reward. Although the school backgrounds were comparable between male and female football professionals, reflecting similar social groups playing the game at grassroots level. However, high university attendance amongst female football professionals is likely due to the lower levels of financial compensation in women’s sport. The England team at the 2019 Women’s Football World Cup is the first fully professional team the country has ever had, and the Scottish team still features many part-time players. Private Education Many of the sports within this statistic, particularly those with a history of amateur participation, include school or university competition as a step on the ladder to success. Sports which require expensive equipment or special facilities lead to a more socially exclusive participant base. Many private schools have sufficient funding to invest heavily in high quality indoor and outdoor facilities for cricket, rugby, hockey, rowing, cycling, sailing and equestrianism. Within these sports the pathway to elite level participation is heavily associated with school or university level participation Olympics The educational backgrounds of British Olympic medalists shown within this statistic is illustrates a complex relationship between the relationship between sport, education and social class in Britain. Within the international sporting tournaments, such as the Olympic Games, Team GB has historically excelled at ‘sitting down sports’, including rowing, cycling, sailing and equestrianism. These all involve specialized and frequently expensive equipment and facilities, and are sports historically associated with higher social classes. Funding has historically been targeted towards such sports, on the basis that they offer the best chance of medals. Whilst this has been a largely successful tactic with regards to international sporting accolades, it is at the expense of funding more widely played and accessible sports, potentially creating additional barriers to participation.

Scraped player stats per game from Understat from 2014/2015 to 2024/2025 (still in progress) seasons.

This contains more detailed information than the dataset from https://www.kaggle.com/datasets/codytipton/understat-data, which includes the individual player stats per game for the English Premier League, La Liga, Bundesliga, Serie A, Ligue 1, and the Russian Football Premier League. In particular, it contains each player's xG, xGBuildup, goals, and shots per game. Furthermore, it has the events for each shot in the events table, clubs and their stats per season in the clubs table, and each game with who lost, won, shots, possession, probabilities of who wins, ect..

This is for educational purposes in our data science bootcamp project.

lineup_stats

• match_id: the id for the match they played
• goals: number of goals for this match
• own_goals: number of own goals for this match
• shots: number of shots for this match
• xG: players xG for this match
• **time*: total amount of time this player played in this match
• player_id: player id
• team_id: id for the players team
• position: players position in this match (SUB means they were substituted in)
• player: player's name
• h_a: 'h' if they are in the home team and 'a' if they are in the away team
• yellow_card: number of yellow cards for this match
• red_card: number of red cards for this match
• **roster_in*: (there is roster information in another table that I did not get, will update later)
• roster_out: (same as roster_in)
• key_passes: number of key passes for this match
• assists: number of assists for this match
• xA: expected assists for this match
• xGChain: total xG for every possession the player is involved in this match
• xGBuildup: Total xG for every possession the player is involved in without key passes and shots in this match
• positionOrder: ordering in the lineup

general_game_stats

• id: this game id
• fid: not sure what this is
• h_id: home team id
• a_id: away team id
• date: date of this game
• league_id: id for the league
• season: which season which game was for
• h_goals: number of goals for the home team
• a_goals: number of goals for the away team
• team_h: home team name
• team_a: away team name
• h_xg: home xG
• a_xg: away xG
• h_w: home win probability
• h_d: home draw probability
• h_l: home loss probability
• league: league name
• h_shot: number of shots by the home team
• a_shot: number of shots by the away team
• h_shotOnTarget: number of shots on target by the home team
• a_shotOnTarget: number of shots on target by the away team
• h_deep:home team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• a_deep: away team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• h_ppda: home team passes allowed per defensive action in the opposition half.
• a_ppda:away team passes allowed per defensive action in the opposition half.

game_events

• id: id for event
• minute: minute the event happend
• result: result (blocked shot, saved shot, ect..)
• X: x-coordinate where the player took the shot
• Y: y-coordinate where the player took the shot
• xG: the xG for the shot
• player: player's name
• h_a: h for home team or a for away team
• player_id: player's id
• situation: situation where this shot happend (direct free kicks, set piece, open play, ect..)
• season: the match season
• shotType: what type of shot (left foot, right foot, head, ect..)
• ** match_id**: id for the match
• h_team: home team name
• ** a_team**: away team name
• ** h_goals**: number of home goals at this time
• ** a_goals**: number of away goals at this time
• date: date of the match
• ** player_assisted**: player who assisted
• lastAction: the last action before this shot

clubs

• club_id: id for the club
• ** club**: club name
• ** league_id** : league id
• ** league**: league name
• ** season**: which season these stats are from
• ** wins**: number of wins that season
• ** draws**: number of draws that season
• ** losses**: number of losses that season
• ** pts**: number of points for that season
• ** avg_xG**: average xG throughout the season
• ** total_goals**: total amount of goals for this season
• total_goals_cond: total amount of goals conceded this season

The English Premier League had more players called up for the 2022 World Cup than any other league, with 136 players initially making their way to the tournament. This represented nearly one in every six players at the tournament. The league with the second-most players was Spain's La Liga, with 83.

Description This dataset contains stats for 10 football players from Europe's top leagues. You will use this data to solve the following problem statement.

Case Study Manchester United football club wants to know which player they should sign for the Striker position from the list provided. You need to perform a comparative Analysis between players and suggest two players whom they should sign.

Additional Note 1. One of the players should be less than 25 years of age 2. One of the players should have preferably played in the English premier league

Column name & description 1. Player Name: Name of the player 2. Age: The age of the player 3. Current Club: Name of the club that the player currently plays for 4. Opponent: Name of the team that the player played against 5. Competition: Name of the competition. 6. Date: Date of the match played 7. Position: Playing position of the player 8. Mins: Minutes played 9. Goals: Total goals 10. Assists: Total assists 11. Yel: Yellow card 12. Red: Red card 13. Shots: Total shots 14. PS%: Pass success percentage 15. AerialsWon: Aerial duels won 16. Rating: Rating per match

• This dataset belongs to @dataanalystduo. Unauthorized use or distribution of this project prohibited @dataanalystduo
• Dataset has been downloaded from the internet using multiple sources. All the credit for the dataset goes to the original creator of the data