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.

Context

Football is more than just a game — it’s data-rich and decision-driven. From match results to player statistics, the English Premier League (EPL) offers a goldmine of insights for analysts, fans, and data scientists.

This dataset is part of a personal data preprocessing project designed to transform messy raw data into a clean, structured format — enabling meaningful analysis, modeling, or visualization. Whether you're predicting match outcomes, exploring season trends, or learning data science, this dataset gives you a strong starting point.

Content

This dataset was originally sourced from football-data.co.uk, a trusted source for historical football data. The raw data was downloaded in CSV format and carefully cleaned using Python. The resulting dataset is ready for analysis and includes statistics such as:

• Match dates

• Full-time and half-time results

• Goals, corners, shots, fouls

• Yellow and red cards

It’s ideal for building machine learning models, dashboards, or practicing sports analytics.

Notes on Specific Variables

• Season: The football season (e.g., 2020–2021)
• MatchDate: The date when the match was played
• HomeTeam: Name of the home team
• AwayTeam: Name of the away team
• FullTimeHomeGoals: Goals scored by the home team (full time)
• FullTimeAwayGoals: Goals scored by the away team (full time)
• FullTimeResult: Match result (H = Home win, A = Away win, D = Draw)
• HalfTimeHomeGoals: Goals scored by the home team (half time)
• HalfTimeAwayGoals: Goals scored by the away team (half time)
• HalfTimeResult: Half-time result (H = Home win, A = Away win, D = Draw)
• HomeShots: Total shots by the home team
• AwayShots: Total shots by the away team
• HomeShotsOnTarget: Shots on target by the home team
• AwayShotsOnTarget: Shots on target by the away team
• HomeCorners: Number of corners won by the home team
• AwayCorners: Number of corners won by the away team
• HomeFouls: Number of fouls committed by the home team
• AwayFouls: Number of fouls committed by the away team
• HomeYellowCards: Yellow cards received by the home team
• AwayYellowCards: Yellow cards received by the away team
• HomeRedCards: Red cards received by the home team
• AwayRedCards: Red cards received by the away team

License

This dataset is for educational and non-commercial use only. Raw data sourced from football-data.co.uk. Please credit the source if you use or share this dataset.

This statistic shows a ranking of advertising categories based on their television screen time during FIFA World Cup games in the United Kingdom (UK) in June and July 2018. Throughout 30 World Cup games shown in ITV, betting ads were the most prominent with a total of 88 minutes of screen time, followed by motoring ads with 68 minutes and grooming ads with 39 minutes.

Data does not appear to be open but is substantial.

Context

Being a big-time football, I keep searching for good data sets on football and finally decided to share one to the Kaggle community!

Content

The data consists of some basic statistics about each football match that happened from 2000-2018 in the English Premier League.

Acknowledgements

This particular data was compiled using the datasets available on http://www.football-data.co.uk

Inspiration

It would be really interesting to see what all factors have correlations with a team winning or losing a match!

Data scrapped from Football-data.co.uk.

Some seasons from some leagues are not available because there is some error on the parser. I will fix it and upload a new version! =)

If you like it, please upvote here and in my github

Date, HomeTeam, AwayTeam: explicit FTHG, FTAG: Final Time Home Goals and Final Time Away Goals FTR: Final Time Result HTHG,HTAG: Half Time Home Goals and Half Time Away Goals HTR: Half Time Result HS, AS: Home Shoots and Away Shoots HST, AST: Home Shoots on Target and Away Shoots on Target HF, AF: Home Fouls and Away Fouls HC, AC: Home Corners and Away Corners HY, AY: Home Yellows and Away Yellows HR, AR: Home Reds and Away Reds PSH, PSD, PSA: Pinnacle closing-odds for Home (H), Draw (D) and Away (A) Div: League and Division

⚽ Club Football Match Data (2000 - 2025)

This dataset offers a simple entrance to the world of football match data analysis. It offers football match data from 27 countries and 42 leagues worldwide, including some of the best leagues such as the English Premier League, German Bundesliga, and Spanish La Liga. The data spans from the 2000/01 season to the most recent results from the 2024/25 season. The dataset also includes Elo Ratings for the given time period with snapshots of ~500 of the best teams in Europe taken twice a month, on the 1st and 15th.

Match results and statistics provided in the table are taken from Football-Data.co.uk. Elo data are taken from ClubElo.

📅 DATASET OVERVIEW

📂 Files number: 2

🔗 Files type: .csv

⌨️ Total rows: ~475 000 as of 07/2025

💾 Total size: ~51 MB as of 07/2025

The dataset is a great starting point for football match prediction, both pre-match and in-play, with huge potential lying in the amount of data and their accuracy. The dataset contains information about teams' strength and form prior to the match, as well as general market predictions via pre-match odds.

🔑 KEY FEATURES

1️⃣ SIZE - This is the biggest open and free dataset on the internet, keeping uniform information about tens of thousands of football matches, including match statistics, odds, and Elo and form information.

2️⃣ READABILITY - The whole dataset is tabular, and all of the data are clear to navigate and explain. Both tables in the dataset correspond to each other via remapped club names, and all of the formats within the table (such as odds) are uniform.

3️⃣ RECENCY - This is the most up-to-date open football dataset, containing data from matches as recent as July 2025. The plan is to update this dataset monthly or bi-monthly via a custom-made Python pipeline.

📋 COLUMNS AND DESCRIPTIONS

💪 TABLE 1 - ELO RATINGS.csv

This table is a collection of Elo ratings taken from ClubElo. Snapshots are taken twice a month, on the 1st and 15th day of the month, saving the whole Club Elo database. Some clubs' names are remapped to correspond with the Matches table (for example "Bayern" to "Bayern Munich").``

💪 TABLE 2 - MATCHES.csv

As of 2025, ******************* women working in football in the United Kingdom had experienced gender-based discrimination, with **** percent having experienced sexist banter/jokes. Nevertheless, ** percent of respondents reported feeling optimistic about the prospects for women in the football industry.

Data Source: https://www.football-data.co.uk/data.php

This dataset is a result of a very extensive effort of webscraping and integrating different data links within the source website. The data is uploaded as CSV files on the website and it is segmented seasonwise and leaguewise across the various countries in Europe. I was able to pull every information from these CSV files uploaded on the website. I did found some missing data in couple of CSV files on the website. However, rest of the data is correct on the source website and has been scraped 100% successfully here.

I have extracted the entire data from all the seasons, beginning from 1993-94 until 2022-23. I have only considered the top two divisions for every country's league.

The dataset provides a granular view of more than 110,000 games from the biggest 5 European football (soccer) leagues: England, Spain, Germany, Italy, France.

In case anyone wants similar data for other leagues as well, you can let me know, and I'll gladly share more datasets here.

The dataset is organized in 2 files:

• past-data.csv contains match data about each game.
• headers.txt contains the textual description of each categorical variable.

I have used this data to:

• create predictive models for football games
• make visualizations about teams' performance and scores
• build final standings table and compare teams

There are tons of interesting analysis a sports enthusiast can perform utilizing this dataset. For example:

• Which teams are the best or sloppiest at holding the lead?
• Which teams are the most clinical in front of goal?
• Which teams are most adept at defending/attacking?
• What is the current form of a team?
• In which leagues is the referee more likely to give a card?

And many many more…

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

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.

Annual release of statistics for football-related arrests and football banning orders. Breakdowns provided are by offence, club supported, overseas arrests and arrests by location (inside/outside stadium). Source agency: Home Office Designation: Experimental Official Statistics Language: English Alternative title: Statistics on football-related arrests and football banning orders

In the 2023-24 academic year, 40 percent of children in England participated in football. This represented little change on the previous year, which had a participation rate of 40.1 percent.

This statistic presents information on whether or not current owners of football clubs in the United Kingdom (UK) considered a full or partial exit within the upcoming twelve or eighteen months, as of 2019, broken down by league. Approximately 23 percent of English Premier League owners thought about an exit within the upcoming year to year and a half.

This statistic shows the number of people participating in American football in England from 2006/2007 to 2015/2016. In 2015/2016, approximately ****** people participated in American football in England. More information about sports in England can be found in the Dossier: Sport in England - Public funding and participation.