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.

Premier League 2023/24: Match, Player, and Team Performance Insights

Dataset Description

This dataset offers an in-depth analysis of the 2023/24 Premier League season, capturing comprehensive data on team and player performances across all matchdays. With over 50 individual CSV files, this collection includes stats on passing accuracy, goal-scoring, defensive actions, possession metrics, and player ratings. Whether you're looking to analyze top scorers, assess team strengths, or delve into individual player contributions, this dataset provides a rich foundation for football analytics enthusiasts and professionals alike.

In addition to the core dataset, we have now added more files related to the league table, expanding the dataset with essential information on match outcomes, league standings, and advanced metrics.

Contents

The dataset contains the following types of data:

• Team Performance Metrics: Information on accurate passes, crosses, goals conceded, interceptions, and other team stats.
• Player Performance Metrics: Individual stats including expected goals (xG), assists, clearances, fouls committed, and tackles won.
• Match-Specific Insights: Detailed metrics on goals scored, scoring attempts, possession percentages, and cards issued per match.
• Match Details (New): Information about rounds, match IDs, teams, scores, and match statuses.
• League Tables (New):
  • Overall standings including matches played, wins, draws, losses, goals scored, goal differences, and points.
  • Separate breakdowns for home and away performances.
  • Advanced metrics including expected goals (xG), expected goals conceded, and expected points.

The file details provide an overview of each dataset, including a brief description of the data structure and potential uses for analysis. This helps users quickly navigate and understand the data available for analysis.

This dataset is ideal for statistical analysis, data visualization, and machine learning applications to uncover patterns in football performance.

Suggested Analysis

This dataset opens up multiple avenues for data analysis and visualization. Here are some ideas:

1. Team Performance Analysis

• Analyze team performance trends, such as comparing passing accuracy, possession, and expected goals (xG) across teams.
• Visualize which teams generate the most scoring opportunities and miss the most big chances.
• Identify the strongest and weakest defenses based on goals conceded, clean sheets, and clearances.

2. Player Performance Analysis

• Identify top-performing players by goals scored, assists, expected goals, and expected assists.
• Explore defensive contributions by analyzing tackles won, interceptions, and clearances per player.
• Assess attacking efficiency by comparing total attempts vs. on-target attempts for each player.

3. Goalkeeping and Defensive Analysis

• Compare goalkeepers on metrics like saves made, goals conceded, and clean sheets to highlight the top performers of the season.
• Evaluate defensive strength by analyzing interception rates and clearances by both teams and players.

4. League Table Insights (New)

• Analyze overall league standings to determine team performance trends.
• Explore home and away performance and identify strengths and weaknesses in different scenarios.
• Utilize advanced metrics to evaluate under- and overperforming teams.

5. Advanced Metrics Exploration

• Examine possession-based metrics, such as possession percentage and possessions won in the attacking third, to identify possession-dominant teams.
• Use expected goals and expected assists data to build profiles highlighting efficient playmaking and finishing among players and teams.

This dataset is a valuable resource for football enthusiasts, data scientists, and analysts interested in uncovering patterns, building predictive models, or generating insights into the Premier League 2023/24 season.

License and Disclaimer

License

This dataset is shared for non-commercial, educational, and personal analysis purposes only. It is not intended for redistribution, commercial use, or integration into other public datasets.

Disclaimer

This dataset was sourced from FotMob, a proprietary provider of football statistics. All rights to the original data belong to FotMob. The dataset is a restructured collection of publicly available data and does not claim ownership over FotMob's data. Users should reference FotMob as the original source when using this dataset for research or analysis.

Terms of Use

By using this dataset, you agree to the following: - Non-commercial Use: This dataset is only for educational, analytical, and personal use. It may not be used for commercial purposes or integrated into other public datasets. - **Proper Attri...

This dataset was generated as part of Practical Exercise 1 of the Data Typology and Lifecycle course, within the UOC's Master's in Data Science.

The objective of the project is to demonstrate the operation of an automated scraper developed with Python and Selenium to extract historical statistics of Premier League players from the 2007/08 season to 2023/24.

This file contains simulated data.
To avoid potential conflicts with intellectual property or privacy rights, the original personal and sports data has been replaced with automatically generated fictitious values. Although masked, private use is preferred. The structure, format, and statistical consistency have been maintained for educational and demonstration purposes.

The original scraper dynamically accessed the official Premier League website (https://www.premierleague.com/stats) to extract information such as:

• Player name
• Position
• Nationality
• Date of birth
• Height
• Season
• Club

Seasonal statistics (goals, assists, appearances, minutes, cards, etc.)

This simulated dataset retains that structure but does not contain any real data.
It can be used as a basis for testing, data analysis training, or documentation of the scraping process.

This dataset provides comprehensive Premier League statistics covering:

• 9 full seasons (2016/2017 to 2024/2025)
• Weekly club performance tables (GW1-38 for each season)
• Detailed club statistics (goals, xG, shots, touches, etc.)
• Complete player profiles (2024/2025 season)
• Player performance metrics (goals, assists, xG, xA, defensive stats)
• Home/away performance breakdowns

Data Sources: Official Premier League website (premierleague.com) Collection Method: Python Selenium web scraping scripts Potential Use Cases:

• Performance trend analysis across seasons
• Player valuation models
• Team strength comparisons
• Predictive modeling for match outcomes
• Fantasy Premier League optimization

Context

This dataset contains 2021-2022 football player stats per 90 minutes. Only players of Premier League, Ligue 1, Bundesliga, Serie A and La Liga are listed.

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

Content

+2500 rows and 143 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
• PasPress : Passes made while under pressure from opponent
• Sw : Passes that travel more than 40 yards of the width of the pitch
• PasCrs : Crosses
• CK : Corner kicks
• CkIn : Inswinging corner kicks
• CkOut : Outswinging corner kicks
• CkStr : Straight corner kicks
• PasGround : Ground passes
• PasLow : Passes that leave the ground, but stay below shoulder-level
• PasHigh : Passes that are above shoulder-level at the peak height
• PaswLeft : Passes attempted using left foot
• PaswRight : Passes attempted using right foot
• PaswHead : Passes attempted using head
• TI : Throw-Ins taken
• PaswOther : Passes attempted using body parts other than the player's head or feet
• PasCmp : Passes completed
• PasOff : Offsides
• PasOut : Out of bounds
• PasInt : Intercepted
• 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 t...

Dataset

This dataset was created by Giovanni Segovia

Contents

Description

This dataset contains detailed player performance statistics for the 2023-2024 season from the Big 5 European soccer leagues: Premier League, La Liga, Serie A, Bundesliga, and Ligue 1. The data has been meticulously scraped from FBref.com, a comprehensive source for soccer statistics.

Columns and Metrics:

• Rank: The rank of the player based on performance metrics.
• Player: Name of the player.
• Nation: Nationality of the player.
• Position: Playing position of the player.
• Squad: Club the player belongs to.
• Competition: League the player is competing in.
• Age: Age of the player.
• Year_Born: Year the player was born.
• Playing Time_MP: Matches played.
• Playing Time_Starts: Matches started.
• Playing Time_Min: Minutes played.
• Playing Time_90s: Equivalent of 90-minute matches played.
• Performance_Gls: Goals scored.
• Performance_Ast: Assists.
• Performance_G+A: Goals plus assists.
• Performance_G-PK: Goals excluding penalties.
• Performance_PK: Penalty kicks made.
• Performance_PKatt: Penalty kicks attempted.
• Performance_CrdY: Yellow cards.
• Performance_CrdR: Red cards.
• Expected_xG: Expected goals.
• Expected_npxG: Non-penalty expected goals.
• Expected_xAG: Expected assists.
• Expected_npxG+xAG: Non-penalty expected goals plus expected assists.
• Progression_PrgC: Progressive carries.
• Progression_PrgP: Progressive passes.
• Progression_PrgR: Progressive dribbles.
• Per 90 Minutes_Gls: Goals per 90 minutes.
• Per 90 Minutes_Ast: Assists per 90 minutes.
• Per 90 Minutes_G+A: Goals plus assists per 90 minutes.
• Per 90 Minutes_G-PK: Goals excluding penalties per 90 minutes.
• Per 90 Minutes_G+A-PK: Goals plus assists excluding penalties per 90 minutes.
• Per 90 Minutes_xG: Expected goals per 90 minutes.
• Per 90 Minutes_xAG: Expected assists per 90 minutes.
• Per 90 Minutes_xG+xAG: Expected goals plus expected assists per 90 minutes.
• Per 90 Minutes_npxG: Non-penalty expected goals per 90 minutes.
• Per 90 Minutes_npxG+xAG: Non-penalty expected goals plus expected assists per 90 minutes.

I am passionate about soccer and have created this dataset in the hope that it can be useful for others who share my love for the game. Whether you're conducting analysis, building models, or just exploring player stats, I hope this dataset provides valuable insights and serves as a helpful resource.

As of 2025, Ryan Giggs had won more Premier League titles than any other player, with a total of 13. This was two more than the player with the second-most, Paul Scholes. Both players played for Manchester United under Alex Ferguson.

Premier League, Serie A, La Liga, Bundesliga, Ligue 1 from 2017-2018 to 2020-2021. 1 collection for each league of a certain season. 1 document for each player. Within each document:
- name, age, nationality, height, weight, team, position. - general stats: games, time, yellow cards, red cards. - offensive stats: goals, assists, xG, xA, shots, key passes, npg, npxG, xGChain, xGBuildup. - defensive stats: Tkl, TklW, Past, Press, Succ, Block, Int. - passing stats: Cmp, Cmp%, 1/3, PPA, CrsPA, Prog.

Three data resources were used: Understat, api-football and Fbref. For more information on the data acquisition phase, I recommend reading the Football players notebook in the Code section.

This dataset is built with the aim of supporting an analysis to try to identify the most probable top performance age range of a player knowing the league in which he plays, his physical characteristics, his role and his nationality.

This dataset consists in 22 JSON files representing a season of the Spanish Football League ("La Liga").

The dataset represents several hierarchically related elements, however, only the Match, Event and Player elements contain relevant information for analysis. The rest of the elements simply serve to keep the data structured, by seasons and matchdays. The dataset collects information from several seasons between the years 2000 and 2022. The attributes of each of the elements that make up the dataset are described below:

Season: JSON documents represent a season, their root contains the following information:

• competition: Name by which the competition is known
• country: Country where the competition is held
• season_id: Identifier of the season, example: Season 2021/22
• season_url: Relative URL of the season's web page
• rounds: List of Round elements, the days into which the championship is divided

Rounds: (or matchdays) Collection of matches:

• number: Name of the matchday, e.g.: Matchday 1.
• matches: List of Match elements, matches that are played on the same day/s of the championship.

Match: contains relevant match information.

• id: Match identifier used at BeSoccer.com
• status: Code representing the status of the match: Played (1), Not Played (0)
• home_team: Name of the home team
• away_team: Name of the away team
• result: List of two integers representing the match score
• date_time: Date and time at which the match started
• referee: First and last name of the referee of the match
• href: URL relative to the match page
• home_tactic: Tactical arrangement of the home team, e.g.: 4-3-3
• home_lineup: List of players in the starting lineup of the home team
• home_bench: List of the home team's substitute players
• away_tactic: Tactical arrangement of the away team, e.g. 4-3-3
• away_lineup: List of players in the home team's starting lineup
• away_bench: List of substitute players of the away team

Event: contains information that defines each of the relevant actions that occur during a soccer match. Events can be described by the following attributes:

• player: Player identifier. Relative URL
• team: Team of the player who participates in the event
• minute: Minute of the match in which the event occurs
• type: Event type (Enumeration)

Players: Player information:

• name: First name
• fullname: Player's full name
• dob: Date of birth
• country: Nationality
• position: Position the player usually occupies: GOA (GoalKeeper), DF (Defender), MID (Midfielder), STR (Striker)
• foot: Dominant Foot: Right-footed, Left-footed, Two-footed, Unknown
• weight: Weight of player in kilograms
• height: Player height in centimeters
• elo: Measurement of the player's skills on a scale of 1 to 100
• potential: Estimate of the maximum ELO that a player can reach on a scale of 1 to 100.
• href: Relative URL of the player's record

About this Dataset

The provided dataset contains a comprehensive set of data about football players from the top 42 European first leagues. The dataset encompasses various statistics and information related to these players, providing valuable insights into their performance, skills, and backgrounds. The data covers a wide range of categories, including player details, club information, performance metrics, awards and achievements, transfer history, youth career, social media presence, and much more.

The dataset includes the following key information for each player:

Player Information: Full name, age, date of birth, place of birth, position, sub-position, nationality, height, and outfitter.

Club Details: Club name, league country, league, market value, contract expiry date, and transfermarkt URL.

Performance Metrics: Appearances, goals, assists, yellow cards, red cards, starting eleven appearances, minutes played, and goal participation.

Player Performance Ratings: Seasonal performance rating and overall performance rating.

Awards and Achievements: Accolades, team achievements, youth trophies, and continental trophies.

Transfer History: Transfer fees, transfer dates, left clubs, and joined clubs.

Social Media Presence: Facebook, Twitter, and Instagram links along with followers, following, likes, and other related metrics.

Domestic and Continental Competitions: Appearances, goals, assists, yellow cards, red cards, minutes played, goal participation, clean sheets, and conceded goals in domestic league competitions, UEFA Champions League, UEFA League, and UEFA Conference League.

Domestic Cup Performances: Appearances, goals, assists, yellow cards, red cards, starting eleven appearances, minutes played, and goal participation in domestic cup competitions.

Player Attributes and Skills: Scoring frequency, accurate passes, successful dribbles, tackles, interceptions, shots on target, ground and aerial duels won, accurate long balls, clearances, dispossessed, possession lost and won, touches, fouls, saves, punches, high claims, crosses not claimed, and much more.

The dataset also provides injury-related information such as missed matches and days injured, allowing analysis of a player's injury history.

This comprehensive dataset serves as a valuable resource for football analysts, clubs, researchers, and enthusiasts to gain in-depth insights into the performance and profiles of football players from the top 42 European first leagues.

Category

Sports

Keywords

soccer,football,sport,transfer

Row Count

15633

Price

$2000.00

As of 2025, three players held the record for the most combined goals and assists in a single Premier League season: Alan Shearer, Andrew Cole, and Mohamed Salah. Each player made 47 goal contributions, with Salah being the only player to do so in a 38-match season.

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

As of 2024, the combined value of all Premier League players amounted to over 11.3 billion euros, significantly more than any other league in the world. England's second-tier, the EFL Championship, had a combined player value of over 1.5 billion euros - more than any other top-tier league outside of the Big Five.

Due to the coronavirus (i.e. covid-19) pandemic, some premier league football clubs may struggle financially over the next few months, due to the cancellation and/or postponement of football fixtures. During a representative survey of the British adult population, undertaken on the 31st March 2020, respondents were asked their opinions on whether premier league football players should be prepared to take a 'pay cut' during the COVID-19 pandemic. Responses have subsequently been categorized by the gender of the panelist.

The vast majority of British adults hold the opinion that Premier League football players should be prepared to take a pay cut during the COVID-19 outbreak, corresponding to 91 percent and 92 percent of male and female respondents respectively.

This Dataset is a merge of two EPL datasets I found online.

First, make sure to look up https://github.com/vaastav/Fantasy-Premier-League who has done an amazing job of collecting stats from the FPL app. There are further players' stats that I might share in the future. The second source is https://datahub.io/sports-data/english-premier-league, where some additional stats are available souch as referee name and betting odds (I kept 365 in the data, you might want to compare odds, etc)

Each row is a summary of a EPL game from one team's perspective. Among the stats you can find shots on target, xG Index, PPDA (measures pressing play) and more.

Notice: I added induvidual players stats. see the attached csv.

Acknowledgements:

As mentioned above, the collecting was done by others. Make sure you take a look and upvote the Github repo that is trully great.

So the EPL is currently shut down, we don't know when it'll be back. By that time, could you predict results? find trends?

As of August 2025, Gareth Barry held the record for the most appearances in the English Premier League, with a career total of 653. The former midfielder won the league with Manchester City in 2011/12.

Dataset

This dataset was created by Jaseem Mohammed

Contents

It contains the following files:

In the English Premier League, the Golden Boot is awarded to the player(s) who scores the most league goals throughout the season. Since the 1888/89 season, the year of the first top flight season in English football, 109 different individuals have been named "top goal scorer" over 127 seasons. In the 2024/25 season, Liverpool's Mohamed Salah won the Golden Boot for the fourth time in eight seasons. Manchester City's Erling Haaland was the top goalscorer in the previous two seasons, including his record-breaking tally of 36 goals in his debut season in 2022/23. Premier League records Current records are generally given in the context of the past three decades, as the total number of games was reduced from 42 to 38 per season in 1995 (in the Premier League's fourth season). In the Premier League era, Thierry Henry and Mohamed Salah have won the Golden Boot more times than anybody else, winning this accolade four times each. Alan Shearer, who won three consecutive Golden Boots in the 90s, is the Premier League's all-time top goal scorer, with 260 goals. Interestingly, Wayne Rooney, who is the Premier League's third-highest goal scorer of all time, never won a Golden Boot. All-time records Outside the Premier League era, Jimmy Greaves has been the top scorer in England more times than any other player, appearing at the top of the list six times between 1958 and 1969, during his career with Chelsea and Tottenham Hotspurs. Derby County's Steve Bloomer finished five seasons as the league's top scorer between 1895 and 1904. The highest ever tally in a single season was sixty goals, which was scored by Everton's Dixie Dean in the 1927/28 season. Greaves, Bloomer, and Dean are also the three top goalscorers of all time in the English league, with 357, 314 and 310 goals respectively. Players from Tottenham have been named top scorer more than players from any other club, appearing 13 times on this list. 20 different nationalities are represented here, and although the vast majority of players are English, there were 16 times where the top scorer in the First Division was Scottish. A much wider variety of nationalities has been represented in recent years, including in the 2018/19 season where it was shared between three players from different African nations.

As of August 2025, four teams in the Premier League had spent 100 million British pounds or more on a player: Liverpool, Chelsea, Manchester City, and Arsenal. Meanwhile, Burnley's most expensive signing cost ** million British pounds.