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 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 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.

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 contains data and results from different Premier League matches from season 99/00 to the season. This data is extracted from a page called resultados-futbol.com. The data is extracted from the section of premier league, in the calendar section.

Detailed analysis of the business of the English Premier League, focusing on sponsorship and the media landscape Read More

Indian Premier League Dataset This dataset contains info on all of the IPL(Indian Premier League) cricket matches. Ball-by-Ball level info and scorecard info to be added soon. The dataset was scraped in July-2022.

  Mantainers:

Somya Gautam Kondrolla Dinesh Reddy Keshaw Soni

The displayed data on the interest in Premier League clubs shows results of the Statista European Football Benchmark conducted in England in 2018. Some ** percent of respondents stated that they are interested in Liverpool F.C..

Dataset

This dataset was created by mchinaka

Released under Apache 2.0

Contents

This spreadsheet contains anonymised force plate data for the following tests: countermovement jump, countermovement-rebound jump, and isometric mid-thigh pull.Following a dynamic warm-up, three trials of each of these tests were performed bilaterally and with maximum-effort by male B Team football players (Under 23s) from two English League One Football Clubs at the beginning of the 2023-24 football pre-season period. The tests were performed in a randomised order with around 30-60 seconds of rest between trials and at least 3-5 minutes of rest given between tests.The data were collected on Hawkin Dynamics force plates and analysed by their software. Here is the link to the Hawkin Dynamics metric database that explains how each metric included in the spreadsheet was measured: https://www.hawkindynamics.com/hawkin-metric-databaseEthics approval was granted from the author's institution and informed consent was provided by each player for their anonymised data to be uploaded to this repository for research use.

As of 2025, Alan Shearer was the Premier League's all-time top scorer, with a career total of 260 goals. The former Newcastle United striker lifted the league title with Blackburn Rovers in 1995. Meanwhile, Harry Kane scored his 200th Premier League goal in early 2023, becoming Tottenham Hotspur's all-time top scorer in his last season at the club.

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.

Results for group 0 v group 1 balanced data set (Best Average Test Performance = 67.9% and Best Average Test Error = 10.8% with a combination of nine variables) and group 0 v group 1 model variables as means and standard deviations for player groupings.

Biographical data represented as means and standard deviations for player groupings.

The Fjelstul English Football Database is a comprehensive database of football matches played in the Premier League and the English Football League from the inaugural season of the Football League (1888-89) through the most recent season (2021-22). The database was created by Joshua C. Fjelstul, Ph.D.

Traffic analytics, rankings, and competitive metrics for premierleague.com as of August 2025

British football fans completed this correlational survey. Willingness to lay down one’s life for a group of non-kin, well documented historically and ethnographically, represents an evolutionary puzzle. Building on research in social psychology, we develop a mathematical model showing how conditioning cooperation on previous shared experience can allow individually costly pro-group behavior to evolve. The model generates a series of predictions that we then test empirically in a range of special sample populations (including military veterans, college fraternity/sorority members, football fans, martial arts practitioners, and twins). Our empirical results show that sharing painful experiences produces “identity fusion” – a visceral sense of oneness – which in turn can motivate self-sacrifice, including willingness to fight and die for the group. Practically, our account of how shared dysphoric experiences produce identity fusion helps us better understand such pressing social issues as suicide terrorism, holy wars, sectarian violence, gang-related violence, and other forms of intergroup conflict.

Some of the greatest atrocities have been caused by groups defending or advancing their political aspirations and sacred values. In order to comprehend and address the wanton violence of war, terrorism and genocide, it is necessary to understand the forces that bind and drive human groups. This five year programme of research investigates one of the most powerful mechanisms by which groups may be formed, inspired, and coordinated: ritual. Studying how children learn the rituals of their communities will shed light on the various ways in which rituals promote social cohesion within the group and distrust of groups with different ritual traditions. Qualitative field research and controlled psychological experiments will be conducted in a number of troubled regions (including Northern Ireland, the Middle East, Nepal, and Colombia) to explore the effects of ritual participation on ingroup cohesion and outgroup hostility in both general populations and armed groups. New databases will be constructed to explore the relationship between ritual, resource extraction patterns, and group structure and scale over the millennia. These interdisciplinary projects will be undertaken by international teams of anthropologists, psychologists, historians, archaeologists, and evolutionary theorists.

As of 2025, Alex Ferguson had won the Premier League more times than any other manager, with a total of 13 titles. Meanwhile, Pep Guardiola picked up his sixth Premier League title in the 2023/24 season.

What is Cricsheet?

Cricsheet is a collection of projects focused on providing cricket data across various formats and competitions. Its primary offerings include:

• Ball-by-Ball Match Data:

  • Covers Men’s and Women’s cricket across:
  • Test Matches
  • One-Day Internationals (ODIs)
  • T20 Internationals (T20Is)
  • Other international T20 matches

• Domestic and Club Competitions:

  • Includes data from leagues such as:
  • Afghanistan Premier League
  • Big Bash League (BBL)
  • T20 Blaze
  • Bangladesh Premier League (BPL)
  • Bob Willis Trophy
  • County Championship
  • Charlotte Edwards Cup
  • Caribbean Premier League (CPL)
  • CSA T20 Challenge
  • FairBreak Invitational Tournament
  • The Hundred
  • International League T20
  • Indian Premier League (IPL)
  • Cricket Ireland Inter-Provincial Limited Over Cup
  • Cricket Ireland Inter-Provincial Twenty20 Trophy
  • Lanka Premier League (LPL)
  • Major League Cricket (MLC)
  • Mzansi Super League
  • T20 Blast
  • Plunket Shield
  • Pakistan Super League (PSL)
  • Rachael Heyhoe Flint Trophy
  • One-Day Cup
  • SA20
  • Super 50
  • Syed Mushtaq Ali Trophy
  • Sheffield Shield
  • Super Smash
  • Women’s Big Bash League (WBBL)
  • Women’s Caribbean Premier League (WCPL)
  • Women’s Premier League (WPL)
  • Women’s Cricket Super League (WCSL)
  • Women’s T20 Challenge

• Player Registry:

  • Maintains a database linking player identifiers across various platforms.

Cricsheet provides an invaluable resource for cricket enthusiasts, analysts, and developers looking for detailed cricket data.

Caribbean Premier League Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.