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 provides an archive of Fantasy Premier League (FPL) player performance data for eight seasons, spanning from 2016-2024.

The data was originally collected from https://github.com/vaastav/Fantasy-Premier-League, a public repository for FPL data.

The dataset has been meticulously cleaned and processed to ensure accuracy and consistency. This may include handling missing values, correcting inconsistencies, and standardizing formats.

The dataset includes a wide range of player statistics captured on a gameweek-by-gameweek basis. This allows you to analyze trends, identify patterns, and gain valuable insights into player performance.

This dataset can be a powerful tool for FPL enthusiasts and data scientists alike. Here are some potential applications: - Trend Analysis: Identify historical trends in player performance across different seasons and positions. - Predictive Modeling: Develop machine learning models to predict player points, performance, and transfers. - Informed Team Selection: Make data-driven decisions to optimize your FPL team for each gameweek. - Comparative Analysis: Compare player statistics across seasons and positions to uncover hidden gems and potential breakout stars.

Using this dataset, you can gain a deeper understanding of FPL player performance and enhance your decision-making for the upcoming season.

This dataset consists of the Premier League team stats for seasons 2022/2023, 2021/2022 and 2022/2021. The data was scraped from fbref.com and formatted into a csv file.

Columns:

date = Date of the match time = Kick-off time of the match comp = Competition of the match (i.e English Premier League) round = The match week the match took place on day = The day the match took place on (i.e Monday, Tuesday etc) venue = Whether team was Home, Away or Neutral venue result = Whether the team Won, Lost or Drew (W, L, D) gf = How many goals the team scored ga = How many goals the team conceded opponent = Who the team faced that day xg = Expected goals xa = Expected goals allowed poss = Possession attendance = How many people attended the match captain = Captain of the team for match formation = Formation the team used for match referee = The referee for the match match report = Please ignore notes = Please ignore sh = Shots total sot = Shots on target dist = average distance by shot fk = shots from free kicks pk = Penalty kicks made pkatt= Penalty kicks attempted season = The year the season took place (i.e for 2022/2023 season year would be 2023) team = The team the stats belong to (i.e Manchester City)

Analysis of ‘English Premier League stats 2019-2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/idoyo92/epl-stats-20192020 on 12 November 2021.

--- Dataset description provided by original source is as follows ---

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?

--- Original source retains full ownership of the source dataset ---

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.

Analysis of ‘Premier League’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zaeemnalla/premier-league on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

Official football data organised and formatted in csv files ready for download is quite hard to come by. Stats providers are hesitant to release their data to anyone and everyone, even if it's for academic purposes. That was my exact dilemma which prompted me to scrape and extract it myself. Now that it's at your disposal, have fun with it.

Content

The data was acquired from the Premier League website and is representative of seasons 2006/2007 to 2017/2018. Visit both sets to get a detailed description of what each entails.

Inspiration

Use it to the best of your ability to predict match outcomes or for a thorough data analysis to uncover some intriguing insights. Be safe and only use this dataset for personal projects. If you'd like to use this type of data for a commercial project, contact Opta to access it through their API instead.

--- Original source retains full ownership of the source dataset ---

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

Context The English Premier League stands out as the preeminent and fiercely competitive soccer league on a global scale.

This dataset is comprised of four CSV files: league, matches, players, and teams. These files collectively offer a comprehensive overview of all the matches that took place during the 2018-2019 season of the Premier League, a season notably marked by the stellar performances of Manchester City and Liverpool in their quest for the league title.

The League file provides a season-wide summary, the Match file furnishes results and statistics for each individual match, the Player file encapsulates performance metrics throughout the season, and the Team file offers insights into game outcomes both at home and away.

Acknowledgement https://footystats.org/download-stats-csv

In the 2023/24 season, Manchester City's Kevin De Bruyne was the highest paid player in the Premier League, earning an estimated annual salary of over 20 million British pounds. De Bruyne's teammate Erling Haaland ranked second, with an annual salary of around 19.5 million British pounds.

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
• Goal assist
• Clean sheet
• Appearances
• Mins played
• Yellow cards
• Red cards
• Total pass

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.

As of August 2024, Gareth Barry held the record for the most appearances in the English Premier League, with a career total of 653 games. The former midfielder played for a number of Premier League clubs, including Aston Villa, Manchester City, and Everton.

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

Comprehensive dataset of Premier League player wages, transfer fees, and club finances

In 2022/23, the average matchday attendance in the Premier League was just over 40,000, representing a slight increase on the previous year. In the same season, Bundesliga games saw an average attendance of nearly 43,000.

The Premier League distributes billions of British pounds to its members each year. Each club receives equal shares of broadcasting and commercial revenues alongside payments based on performance and the frequency of televised matches. In 2023/24, the Premier League paid out around 2.85 billion British pounds to clubs, representing a slight increase on the previous season.

Context

I was working on FPL points prediction model and I thought that opponent team stats matters a lot, so I started collecting this data. But, this data is stored in around 550 different csv files and I wanted the data in a single csv file. So, I merged all the csv files according to the stats. And now I'm sharing these files with all of you so that you could save your precious time.

Content

These files contain data of three seasons 17/18, 18/19 and 19/20. If you want the data for more seasons you can visit to this link mentioned below. - https://fbref.com/en/comps/9/1631/2017-2018-Premier-League-Stats

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..

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. For this study, participants of all Premier League teams were given the opportunity to participate to prevent the research purpose being revealed. However, the study was predominantly advertised to the relevant teams’ fan groups (five consistent winners, and five perennial losers). An online questionnaire was advertised across social media (i.e. Facebook, Twitter), on online fan forum groups, dedicated fan blogs and across student networks. The online nature of the study allowed the research to reflect the cross-national diversity of the cohort, as teams from across the UK were included. A £100 prize was offered as an incentive to complete the study.

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.

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