The revenue of the Big Five European soccer leagues has grown significantly since 1996/97, reaching a combined total of over 20 billion euros in the 2023/24 season. This has been forecast to rise to around 21.1 billion euros by 2025/26.

Context

Most publicly available football (soccer) statistics are limited to aggregated data such as Goals, Shots, Fouls, Cards. When assessing performance or building predictive models, this simple aggregation, without any context, can be misleading. For example, a team that produced 10 shots on target from long range has a lower chance of scoring than a club that produced the same amount of shots from inside the box. However, metrics derived from this simple count of shots will similarly asses the two teams.

A football game generates much more events and it is very important and interesting to take into account the context in which those events were generated. This dataset should keep sports analytics enthusiasts awake for long hours as the number of questions that can be asked is huge.

Content

This dataset is a result of a very tiresome effort of webscraping and integrating different data sources. The central element is the text commentary. All the events were derived by reverse engineering the text commentary, using regex. Using this, I was able to derive 11 types of events, as well as the main player and secondary player involved in those events and many other statistics. In case I've missed extracting some useful information, you are gladly invited to do so and share your findings. The dataset provides a granular view of 9,074 games, totaling 941,009 events from the biggest 5 European football (soccer) leagues: England, Spain, Germany, Italy, France from 2011/2012 season to 2016/2017 season as of 25.01.2017. There are games that have been played during these seasons for which I could not collect detailed data. Overall, over 90% of the played games during these seasons have event data.

The dataset is organized in 3 files:

• events.csv contains event data about each game. Text commentary was scraped from: bbc.com, espn.com and onefootball.com
• ginf.csv - contains metadata and market odds about each game. odds were collected from oddsportal.com
• dictionary.txt contains a dictionary with the textual description of each categorical variable coded with integers

Past Research

I have used this data to:

• create predictive models for football games in order to bet on football outcomes.
• make visualizations about upcoming games
• build expected goals models and compare players

Inspiration

There are tons of interesting questions a sports enthusiast can answer with this dataset. For example:

• What is the value of a shot? Or what is the probability of a shot being a goal given it's location, shooter, league, assist method, gamestate, number of players on the pitch, time - known as expected goals (xG) models
• When are teams more likely to score?
• Which teams are the best or sloppiest at holding the lead?
• Which teams or players make the best use of set pieces?
• In which leagues is the referee more likely to give a card?
• How do players compare when they shoot with their week foot versus strong foot? Or which players are ambidextrous?
• Identify different styles of plays (shooting from long range vs shooting from the box, crossing the ball vs passing the ball, use of headers)
• Which teams have a bias for attacking on a particular flank?

And many many more...

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.

Data recording the circumstances and results of the full 'A' international football matches involving Brazil, from 9th of August 1993 to 31st of December 2010, as listed in the Rec.Sport.Soccer Statistics Foundation (RSSSF) database. (FIFA commenced releasing national team rankings in 1993.) Excludes matches from the 1996 and 2003 CONCACAF, in which Brazil fielded their under-23 Olympic squad, three friendly matches not recognised by the opponent's federation, and five matches where Brazil was the lower ranked team. Does not take account of goals outside regulation time.

The National Women's Soccer League is the highest division of professional women's soccer in the United States. In the 2023 season, Angel City FC was the most valuable NWSL team, with a value of around 180 million U.S. dollars. The Los Angeles-based franchise was founded in July 2020 and is run by a majority female ownership group.

Context

Today, data is the new oil. And this also reflects on football.

The dataset constains data from 2013 to 2020.

Content

In here, you'll find a few team statistics of Brazil's National Football League. You can compare the average number of goals, cards, possesions and much more.

The equipe column have a number before the team's name. The equipe column have a number before the team's name. This number mean the position that the squad finished that season in the WhoScored ranking.

For example, in the 2020 dataset, the 1st row is like this: 1. Flamengo This means that Flamengo was the championship winner for 2020.

Acknowledgements

All data was gathered from www.whoscored.com, with a webscrapping python code. I have itentions to share in the future the source code.

The National Women's Soccer League is the highest division of professional women's soccer in the United States. In the 2023 season, Angel City FC was the NWSL team that generated the most revenue, estimated at around 31 million U.S. dollars. The Los Angeles-based franchise was founded in July 2020 and is run by a majority female ownership group.

During a May 2023 survey in the United States, 17 percent of respondents aged 18 to 34 stated that they were avid fans of soccer. Meanwhile, 78 percent of respondents aged 65 or older expressed no interest in the sport.

This dataset provides detailed information on football (soccer) shots, capturing various contextual and technical aspects of each attempt. It is designed for sports analytics, machine learning models, and tactical analysis. It was created with the objective to generate a basic xG model.

This dataset was generated through web scraping with the intention to provide data in order to analyze the teams performance.

This dataset features player statistics from the 2024-2025 season across the top five European leagues, sourced from FBref. Automatically updated weekly.

It includes two files:

players_data-2024_2025.csv – A comprehensive dataset with over 250 columns, covering detailed player statistics.

players_data_light-2024_2025.csv – A streamlined version containing the most crucial attacking, passing, defending, and goalkeeping stats for each player.

Let me know if you'd like further refinements!🚀

Columns

🔹Basic Player Information:

Player– Player's name Nation – Player's nationality Pos – Position (FW, MF, DF, GK) Squad – Club name Comp – League Age – Age of the player Born – Year of birth

📊Playing Time & Appearances

MP – Matches played Starts – Games started Min – Minutes played 90s – Number of full 90-minute matches played

⚽Attacking Stats

Gls – Goals scored Ast – Assists provided G+A – Goals + Assists xG – Expected goals xAG – Expected assists npxG – Non-penalty expected goals G-PK – Goals excluding penalties

🛡️Defensive Stats

Tkl – Total tackles TklW – Tackles won Blocks – Blocks made Int – Interceptions Tkl+Int – Combined tackles and interceptions Clr – Clearances Err – Errors leading to goals

🎯Passing & Creativity Stats

PrgP – Progressive passes PrgC – Progressive carries KP – Key passes (passes leading to a shot) Cmp%_stats_passing – Pass completion percentage Ast_stats_passing – Assists xA – Expected assists PPA – Passes into the penalty area

🧤Goalkeeping Stats

GA – Goals conceded Saves – Saves made Save% – Save percentage CS – Clean sheets CS% – Clean sheet percentage PKA – Penalties faced PKsv – Penalty saves

🔄Possession & Ball Control

Touches – Total touches of the ball Carries – Total ball carries PrgR – Progressive runs (carries moving the ball forward significantly) Mis – Miscontrols Dis – Times dispossessed

🚨Miscellaneous Stats

CrdY – Yellow cards CrdR – Red cards PKwon – Penalties won PKcon – Penalties conceded Recov – Ball recoveries

Financial overview and grant giving statistics of Soccer League Of Long Island Inc

Summary statistics, individual players (8,226 observations on 876 players).

During a May 2023 survey in the United States, 18 percent of male respondents stated that they were avid fans of soccer. Meanwhile, three quarters of female respondents expressed no interest in the sport.

Soccer is one of the most popular sports worldwide and is played by millions of people across Europe. During an April 2021 survey across five countries in Europe, 20 percent of respondents from the United Kingdom stated that they currently played football, while this figure fell to just nine percent among respondents in France.

Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

Key Benefits:

Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.

Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.

Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.

API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

Use Cases:

Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.

Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.

Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

Use our trusted SportMonks Football API to build your own sports application and be at the forefront of football data today.

Our Football API is designed for iGaming, media, developers and football enthusiasts alike, ensuring you can create a football application that meets your needs.

Over 20,000 sports fanatics make use of our data. We know what data works best for you, so we ensured that our Football API has all the necessary tools you need to create a successful football application.

• Livescores and schedules Our Football API features extremely fast livescores and up-to-date season schedules, meaning your app will be the first to notify its customers about a goal scored. This also works to further improve the look and feel of your website.

• Statistics and line-ups We offer various kinds of football statistics, ranging from (live) player statistics to team, match and season statistics. And that’s not all - we also provide pre-match lineups for all important leagues.

• Coverage and historical data Our Football API covers over 1,200 leagues, all managed by our in-house scouts and data platform. That means there’s up to 14 years of historical data available.

• Bookmakers and odds Build your football sportsbook, odds comparison or betting portal with our pre-match and in-play odds collated from all major bookmakers and markets.

• TV Stations and highlights Show your customers where the football games are broadcasted and provide video highlights of major match events.

• Standings and topscorers Enhance your football website with standings and live standings, and allow your customers to see the top scorers and what the season's standings are.

The global football analysis software market is experiencing robust growth, driven by the increasing adoption of data-driven strategies by football clubs, associations, and organizations. The market size in 2025 is estimated at $619.8 million. While the exact CAGR isn't provided, considering the technological advancements in sports analytics and the rising demand for performance optimization, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 12-15%. This growth is fueled by several factors. The increasing accessibility of advanced analytics tools, coupled with a growing understanding of their value in enhancing player performance, tactical decision-making, and talent scouting, significantly contributes to this upward trend. The prevalence of cloud-based solutions offers scalability and cost-effectiveness, further boosting market expansion. Furthermore, the rising popularity of football globally, coupled with increased investment in sports technology, fuels the demand for sophisticated analysis tools. Different segments within the market, such as cloud-based versus on-premise solutions and applications tailored to football clubs versus broader football organizations, offer diverse revenue streams and cater to specific needs. Competition is fierce amongst established players like Nacsport, Hudl, and Dartfish, along with emerging companies pushing innovation in areas such as AI-powered video analysis and advanced statistical modeling. The competitive landscape is dynamic, with both established players and new entrants vying for market share. Geographical distribution shows significant demand in regions like North America and Europe, driven by the mature football infrastructure and high levels of investment in sports technology. However, growth is also anticipated in emerging markets like Asia Pacific and Middle East & Africa, as football's popularity and the adoption of advanced analytical techniques expand. The market's continued expansion hinges on the ongoing development of more sophisticated analytics capabilities, including advanced AI algorithms and integration with wearable sensor technology. Furthermore, factors like improved user interfaces and easier access to training and support will also influence future market growth, driving adoption among a wider range of users. This suggests a promising future for the football analysis software market, underpinned by the continuing convergence of technology and sports performance optimization.

This dataset provides detailed information on the top 500 football players in 2024, including their market values, performance statistics, and demographics. Key features include:

1. Market values ranging from €200M (Haaland, Vinicius Jr.) to €20M
2. Player statistics including goals, assists, and appearances
3. Demographic data including age (17-37) and nationality
4. Club affiliations across major leagues
5. Position-specific information
6. Performance metrics including yellow/red cards and substitution patterns

This dataset contains a set of statistics regarding the spanish first division teams.

Each field value has been computed as the mean for the last 30 games played, for the following statistics:

Possession

Passes

Tackles

Corners

Shots - Total

Shots - On target

Shots - Off target

Shots - Blocked

Shots - Outside Box

Shots - Inside Box

Fouls

Offsides

Yellow Card

Red Card

Penalties

Data has been obtained from https://playerstats.football