The graph shows the results of an Ipsos survey on the level of interest in soccer worldwide as of May 2018. During the survey fielded in April and May 2018, ** percent of respondents worldwide stated that they were passionate soccer followers and would watch as many games as possible at any given time.

This dataset contains detailed soccer match data in 2024-2025 season, compiled from ESPN soccer data API.
This dataset contains multiple csv files. The csv files include the following data:
- 30,000+ Match fixtures information, including - Match lineups - Play-by-play information - Key events - Commentary - Team statistics - Player statistics - 400+ unique leagues worldwide - 3,000+ Teams/clubs information - 45,000+ Player information - 1,200+ Teams with team roster

Data is updated daily and covers major soccer leagues world wide  

Files are organized in 5 zip archives by category, plus one archive for base files.

About Dataset – TL;DR

Comprehensive football (soccer) data lake from Transfermarkt, clean and structured for analysis and machine learning.

• 93,000+ players worldwide
• 2,200+ clubs across all major leagues
• 5.7M+ total records across 10 categories
• 902,000+ market valuations
• 1.9M+ player performance stats
• 1.2M+ player transfer histories
• 144,000+ injuries & 93,000+ national team appearances
• 1.3M+ teammate relationships

Everything in raw CSV format – perfect for EDA, ML, and advanced football analytics.

⚽ The Most Comprehensive Transfermarkt Football Dataset

A complete football data lake covering players, teams, transfers, performances, market values, injuries, and national team stats. Perfect for analysts, data scientists, researchers, and enthusiasts.

🗺 Entity-Relationship Overview

Here’s the high-level schema to help you understand the dataset structure:

https://i.imgur.com/WXLIx3L.png" alt="Transfermarkt Dataset ER Diagram">

📊 Key Coverage

• Players: 93,000+ professional players
• Teams: 2,200+ clubs, 7,700+ club relationships
• Data Volume: 5.7M+ total records
• Global Scope: Major leagues and competitions worldwide

🗂 Data Structure

Organized into 10 well-structured CSV categories:

Player Data (7 categories)

• Player Profiles
• Performances (matches, goals, assists, cards, minutes)
• Market Values (historical valuations)
• Transfer Histories
• Injury Records
• National Team Performances
• Teammate Networks

Team Data (3 categories)

• Team Details (club info)
• Competitions & Seasons
• Parent/Child Team Relations

🔗 What’s Inside?

• 902K+ market value records to track valuation trends
• 1.1M+ transfer histories with fees and movement
• 1.9M+ performance stats across seasons and competitions
• 144K+ injury records with days and matches missed
• 93K+ national team appearances
• 1.3M+ teammate relationships for chemistry analysis

💡 Why This Dataset?

Most football datasets are pre-processed and restrictive. This one is raw, rich, and flexible:

• Build custom KPIs and models
• Perform deep exploratory analysis (EDA)
• Train machine learning and prediction pipelines
• Combine with other football data sources

🚀 Example Use Cases

• Predictive Modeling – Player ratings, transfer value forecasts, injury risk
• Data Visualization & Dashboards – Club comparisons, performance analytics
• Scouting & Recruitment – Discover undervalued talent
• Network Analysis – Teammate relationships and synergy

🖥 Technical Details

• Format: CSV files, UTF-8 encoded
• Easy to Use: Ready for Python (pandas, numpy), R, SQL, BI tools
• Scalable: 5.7M+ rows for big-data analysis

💡 Working on a Cool Project?

I’m always excited to collaborate on innovative football data projects. If you’ve got an idea, let’s make it happen together!

📬 Contact Me

• GitHub: @salimt
• Issues: Feel free to use GitHub Issues if you’ve got dataset-specific questions.

⭐ Support & Visibility

If this dataset helps you:
- Upvote on Kaggle
- Star the GitHub repo
- Share with others in the football analytics community

Tags

football analytics soccer dataset transfermarkt sports analytics machine learning football research player statistics

🔥 Analyze football like never before. Your next AI or analytics project starts here.

In 2023-24, over 850,000 high schoolers in the United States played soccer, with boys accounting for nearly 55 percent of participants. Overall, participant numbers grew by around three percent compared to the previous year.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

American College Football network of Girvan and Newman Mark Newman provides a football.gml file which contains the network of American football games between Division IA colleges during regular season Fall 2000. The file asks you to cite M. Girvan and M. E. J. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002). There are are two issues with the original GN file. First three teams met twice in one season so the graph is not simple. This is easily dealt with if required. Secondly, the assignments to conferences, the node values, seem to be for the 2001 season and not the 2000 season. The games do appear to be for the 2000 season as stated. For instance the Big West conference existed for football till 2000 while the Sun Belt conference was only started in 2001. Also there were 11 conferences and 5 independents in 2001 but 10 conferences and 8 independents in 2000. I have provided a set of files footballTSE* which define a simple graph with the correct conference assignments in the archive here. There is a read me file included with more details. Further information about the problems with this data and the solutions are given in T.S. Evans, “Clique Graphs and Overlapping Communities”, J. Stat. Mech. (2010) P12037 [arXiv:1009.0638] which would be the appropriate source to cite along with the original GN publication.Note that Gschwind et al, 2015, Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques, independently finds similar errors in this data.

Women's Football (European Leagues)

Team and Player Performance Statistics

By [source]

About this dataset

This dataset includes comprehensive female football-related performance data and player statistics from the top 5 European leagues: Serie A in Italy, Liga Femenina in Spain, Women's Super League in England, Bundesliga Frauen in Germany, and Division 1 Feminin in France. Gathered throughout each season of the respective leagues, the dataset tracks teams, players, matches and a range of important performance metrics. The recently released data provides intriguing insight into team success and player form - covering parameters such as goals scored per game (xGHome), clean sheets (CS), number of opponents' passes allowed (Sweeper_#OPA) as well as individual performance stats such as tackles made per goal kick (Crosses_Stp). Analyze this insightful data to gain further insight on how female football is developing across Europe's major leagues!

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How to use the dataset

This dataset can be used to analyze and compare the performance of teams and players across the top five European leagues: Serie A in Italy, Liga Femenina in Spain, Women's Super League in England, Bundesliga Frauen in Germany, and Division 1 Feminin in France. The dataset provides records of each individual match that occurred within these leagues during the tracked season(s), as well as a range of performance metrics for both teams and players.

To use this dataset effectively it is important to understand which columns are available, as described above. By exploring different combinations of team-level versus player-level data you will be able to identify correlations between certain performance metrics for teams or players that provide insights about female football success across Europe.

Once you’re ready to start exploring the data there are several approaches you may take from visualizing your data via bar or line graphs with Python Matplotlib or Seaborn packages; correlating team-level versus player-level statistics such as number of wins (W) compared against goalkeeper saves (Saves); or performing more complicated regression analyses on your data that explore how different features like time played (Min) can predict goals scored (Goals_FK). Each approach provides unique insights into trends within female football success.

No matter how you choose to analyze this dataset it is important to note that trendlines may shift from year-to year -- so make sure you use consistent periods when comparing changes between seasons! It is also helpful to break down aggregate results by country when analyzing different trends across Europe so consider running separate analyses for each country instead aggregating them all together at once. Using this stepwise approach we hope that through careful exploration of the female football success will begin ‘uncovering’!

Research Ideas

• Analyzing the effect of player performance metrics on team success and vice versa: Using this dataset, it is possible to analyze how changes in different player performance metrics might affect overall team performance (e.g. goals scored or allowed, clean sheets). With further analysis, correlations can be drawn between teams’ and players’ performances under different match-day conditions such as travel distance or surface type.

• Examining trends in the development of female football: This data set spans multiple seasons, making it possible to evaluate any general trends in aspects such as the average age of the players across countries and how that affects their performances; or identifying any underused opportunities available for young talented footballers in specific countries which could be benefitted from improvisations by these countries' governing bodies;

• Benchmark positions used among teams versus outside experts’ opinions: One clever use for this dataset can be to compare positional performances between expert opinions from scouts with actual field results from teams using those positions within each country's top leagues and analyzing areas where consensus is reached upon versus discrepancies found throughout the analyzed data samples . For example, one may cross-examine national team call up rosters with squad selections for clubs’ top female divisions - finding anomalies not spotted prior by those making roster decisions - thereby potentially deriving more informed decisions with regards to selecting position holders based on tangible facts rather than focusing merely on biased subjective eye tests over which player should officially take ...

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

A huge share of consumers in the United States do sports in their free time. The popularity of soccer as a sport activity in the U.S. shows the following changes over time. Looking at the most recent data points there has been an increase from 2023 Q1 to 2024 Q1. The share of respondents grew from 13 percent to 15 percent during this time. These consumers playing soccer are choosing to stay active within this discipline. Like most sports, it requires not only motivation but also the matching equipment. If you want to know how consumers in the U.S. commonly stay active, you can check out the most popular sports activities in the U.S. next to find out how consumers playing soccer benchmark against other disciplines. The survey was conducted online among 5436 to 22626 respondents per quarter in the United States, between 2022 and 2024. Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than 2,000,000 interviews.

This graph depicts the top 10 soccer leagues based on World Cup players in 2010. 75 soccer players from the Italian league Seria A participated in the World Cup in 2010.

The graph shows the favorite international Mexican soccer players among fans in 2019. As of June of that year, **** percent of respondents in Mexico said Javier "Chicharito" Hernández was their favorite Mexican soccer player in foreign clubs.

Network of 46 papers and 67 citation links related to "Moral Atmosphere and Athletic Aggressive Tendencies in Young Soccer Players".

Context

This dataset contains European football team stats. Only teams of Premier League, Ligue 1, Bundesliga, Serie A and La Liga are listed.

Content

Auxiliary datasets: * 2021-2022 Football Player Stats * 2021-2022 Football Team Stats

Acknowledgements

Data from Football Reference. Image from Wyscout.

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IntroductionThis study aimed to assess the development of speed, endurance and power in young football players and to create percentile charts and tables for standardized assessment.MethodsCross-sectional data were collected from 495 male players aged 12–16 years at RKS Raków Częstochowa Academy in 2018–2022. Players participated in a systematic training in which running time 5 m, 10 m, 30 m, lower limb power (standing long jump), and Maximum Aerobic Speed (MAS) were measured using the 30–15 Intermittent Fitness Test. All tests were performed under constant environmental conditions by qualified personnel. Statistical analysis included ANOVA and percentile distribution for P3, P10, P25, P50, P75, P90, P97.ResultsResults indicated that the most significant improvements occurred between the ages of 13 and 14, with increased speed over all distances and a significant increase in power. Percentile tables were developed, highlighting improvements in speed 5 m: 0.087–0.126 s; 10 m 0.162–0.215 s; 30 m: 0.438–0.719 s and power in the long jump test: 31–48 cm. Improvements in MAS ranged from 0.3 to 0.6 m/s across the percentiles.DiscussionThe results highlight the need for individual training programs tailored to the biological maturity of players. The developed percentile charts and tables offer a valuable tool for coaches and sports scientists to monitor progress, optimize training loads, and minimize the risk of injury, providing a frame of reference for assessing the physical development of young soccer players. Future research should focus on extending these charts and tables to other age groups and genders to refine training methodologies further.

The graph shows the monthly average viewership per game of Major League Soccer in the United States in 2016 and 2017. The data shows that in June 2017, an average of over 290 thousand people watched a Major League Soccer game, a figure which stood at over 478 thousand in the same month of 2016.

The graph shows the citations of ^'s papers published in each year.

This graph presents soccer fan's opinion on the utilization of video assistant referee (VAR) during soccer games in France in a survey from January 2018. The survey reveals that ** percent of respondents declared they were in favor of the utilization of the VAR in order to validate a goal.

this graph was created in R:

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Football, or soccer as it's known in some regions, is more than just a sport; it’s a global phenomenon that brings together millions of fans and players alike. Each year, leagues around the world strive to showcase the best talent, thrilling matches, and unforgettable moments. In 2023, several leagues stand out for their competitive nature, historical significance, and the quality of players they attract. This comprehensive guide explores the top football leagues in 2023, detailing what makes each competition unique and essential for players and fans alike.

1. English Premier League (EPL) The English Premier League (EPL) continues to reign supreme as one of the most popular and competitive football leagues globally. Known for its intense atmosphere, diverse fan base, and high-paced matches, the EPL attracts some of the best talents from around the world. Teams like Manchester City, Liverpool, and Chelsea have consistently pushed the boundaries of the game, setting new records for points and goals.

Key Features: High Competition: The league's competitive nature means that any team can win on any given matchday, making it exciting for both players and fans. Star Players: The EPL features world-class players, including Kevin De Bruyne, Mohamed Salah, and Harry Kane, making it a hub for aspiring footballers. Global Reach: With millions of viewers worldwide, playing in the EPL not only enhances a player's career but also increases their global profile. 2. La Liga La Liga, Spain’s top professional football division, is renowned for its technical style of play and has been the breeding ground for some of football’s greatest talents. With iconic clubs like FC Barcelona and Real Madrid leading the way, La Liga showcases a blend of attacking football and tactical acumen.

Key Features: Tactical Mastery: La Liga is known for its emphasis on skill and tactics, with teams often employing intricate passing styles that challenge players' technical abilities. Historic Rivalries: The El Clásico rivalry between Barcelona and Madrid captivates fans and players alike, representing one of the most-watched fixtures in sports. Youth Development: Spanish clubs excel in youth academies, producing top talents like Pedri and Gavi, who have quickly risen to prominence. 3. Bundesliga Germany's Bundesliga is celebrated for its attacking football, strong fan culture, and financial stability. Known for its high-scoring matches and passionate supporters, the Bundesliga emphasizes both player development and competitive balance.

Key Features: Fan Engagement: Bundesliga clubs boast some of the highest average attendances in the world, showcasing the passionate support of German fans. Player Development: The league is renowned for developing young talent, with clubs like Borussia Dortmund leading the way in nurturing future stars. Exciting Matches: The Bundesliga consistently produces thrilling matches, characterized by fast-paced gameplay and an attacking mentality. 4. Serie A Italy's Serie A is famous for its tactical sophistication and defensive mastery. With a rich history and iconic clubs such as Juventus, AC Milan, and Inter Milan, Serie A has seen a resurgence in recent years, attracting both domestic and international talents.

Key Features: Tactical Nuance: Known for its strategic depth, Serie A emphasizes defensive organization, providing players with invaluable lessons in positioning and game intelligence. Historical Significance: Serie A boasts a rich legacy with numerous clubs having a strong tradition of success in European competitions. Cultural Impact: Italian football is intertwined with the country’s culture, making matches not just sporting events but also social occasions. 5. Major League Soccer (MLS) In recent years, Major League Soccer (MLS) has gained significant traction, particularly in North America. With a growing fan base and investment in top talents, MLS is becoming an attractive destination for both established players and emerging stars.

Key Features: Rapid Growth: MLS has expanded rapidly, adding new teams and increasing its global profile, making it an exciting league for players looking to build their careers. Diverse Talent Pool: The league attracts players from around the world, including established stars like Lionel Messi, who have j...