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 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.

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...

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 *************, the quickest goal ever scored in the Premier League was Shane Long's opener against Watford in **********, coming in just **** seconds. The second-fastest goal was scored by Philip Billing, who scored **** seconds into AFC Bournemouth's match against Arsenal in **********.

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

This dataset contains detailed information on all Premier League matches played between the 2021 and 2025 seasons. It includes match dates, times, venues, results, goals scored (gf), goals against (ga), expected goals (xg), possession percentages, attendance figures, team formations, referees, and other relevant statistics. This data can be used for analysis, modeling predictions, or exploring trends in Premier League football.

Columns:

Mohamed Salah scored 32 Premier League goals for Liverpool in his first season at the club, marking his most prolific season to date. Meanwhile, Salah scored 18 league goals in 2023/24.

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.

In the season 2022/23, Harry Kane scored ** goals. His record-number of goals were reached in the seasons 2017/18 and 2022/23, when he scored ** goals. Find further Premier League statistics regarding the number of goals scored for players like Joe Cole, James Mccarthy, and Diogo Jota.

In the season 2022/23, Danny Welbeck scored *** goals. His record-number of goals was reached in the season 2013/14, when he scored **** goals. Find further Premier League statistics regarding the number of goals scored for players like Sebastian Larsson, Nemanja Vidic, and Hermann Hreidarsson.

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

In 2023/24, Erling Haaland won the race for the Premier League Golden Boot, with 27 goals. In the previous season, Haaland broke the record for the most goals scored in a single Premier League season, with 36.

Wins generated with goals and assists by Messi vs Ronaldo against Premier League's 'BIG SIX' in current season

In the season 2022/23, Roberto Firmino scored ** goals. His record-number of goals was reached in the season 2017/18, when he scored ** goals. Find further Premier League statistics regarding the number of goals scored for players like Abdoulaye Doucouré, Tugay, and Jordan Henderson.

Egyptian Premier League Match Data (2010-2024) This dataset contains detailed information about matches played in the Egyptian Premier League from 2010 to 2024. The dataset includes match statistics, team performance, referee decisions, and the outcome of each match.

Features Overview: 1. ID: Unique identifier for each match. 2. Season: The season in which the match took place. 3. Fixture: Details about the specific fixture in the league. 4. MatchDay: The match day number within the season. 5. Date: The date on which the match was played. 6. Time: The time of the match. 7. Home Team: The team playing at home. 8. Away Team: The visiting team. 9. Referee: The referee officiating the match. 10. Yellow Home: Number of yellow cards issued to the home team. 11. Yellow Away: Number of yellow cards issued to the away team. 12. 2nd Yellow Home: Number of second yellow cards (leading to a red card) for the home team. 13. 2nd Yellow Away: Number of second yellow cards for the away team. 14. Red Home: Number of red cards issued to the home team. 15. Red Away: Number of red cards issued to the away team. 16. Half Time Result: The score at halftime. 17. Full Time Result: The final score at the end of the match. 18. Home Goals: Goals scored by the home team. 19. Away Goals: Goals scored by the away team. 20. Winner: Indicates the winner of the match (Home, Away, or Draw). 21. Label: Various performance labels or categorization criteria. 22. Count: Frequency or count associated with certain labels.

Potential Use Cases: * Match Analysis: Track performance trends for different teams, referees, and players over multiple seasons. * Predictive Modeling: Create machine learning models to predict match outcomes based on past performance. * Referee Performance: Analyze the impact of referees on match outcomes and team discipline. * Team Strategy Insights: Examine the correlation between yellow/red cards and match results. * Time Series Analysis: Perform time-based analysis of matches and outcomes across different seasons. This dataset is ideal for soccer analysts, sports statisticians, and machine learning enthusiasts who are interested in exploring match data from the Egyptian Premier League.

Milestone goals by Messi vs Ronaldo against Premier League's 'BIG SIX' in the 2023-2024 season and many other statistics about the greatest rivalry in football history

Draws generated with goals by Messi vs Ronaldo against Premier League's 'BIG SIX' in the 2025-2026 season

I made create a data of liverpool football team 23/24 season stats by Mysql. There is no blank in any stats because of that you can use that data for analysis That dataset includes just Premier League stats of Liverpool

Content This dataset includes 36 players of Liverpool who is played in Premier League and those players stats illustrates in there. These stats are:

• First names
• Last names
• Nations
• Match Playing
• Positions
• Ages
• Minutes
• Match Starts
• Time left until the match ends
• Goals
• Assists

In the season 2022/23, Marcus Rashford scored ** goals. His record-number of goals were reached in the seasons 2019/20 and 2022/23, when he scored ** goals. Find further Premier League statistics regarding the number of goals scored for players like Oriol Romeu, Adama Traoré, and Yossi Benayoun.