In a September 2024 survey, ** percent of respondents in the United States identified as college football (NCAA) fans. Meanwhile, ** percent of respondents described themselves as diehard fans.

There are millions of sports fans across the United States, from those religiously following an NFL team to avid tennis fans or those who watch every Formula One Grand Prix. During an April 2023 survey in the United States, 44 percent of male respondents stated that they were avid sports fans. Meanwhile, this figure was just 15 percent among female respondents.

National Football League fans in the U.S.

Football is a widely enjoyed sport in the United States, as is evident from the millions of fans who tune in to watch their favorite teams compete every Monday night. The sport enjoys a diverse viewer demographic, with more than two thirds of white, Hispanic, and Black participants in an online survey identifying as either an avid or casual fan of football in January 2023. The survey also investigated the level of interest in the NFL in the U.S. broken down by gender, with a significantly larger share of men identifying as avid fans of the sport than women.

Women’s professional sports fans in the U.S.

Women’s professional sports viewership in the U.S. has grown significantly in recent years, helped at least partially by tournament victories across a wide range of sporting categories. When asked about the reason behind their interest in women’s sport in the U.S., nearly a third of respondents highlighted international events such as the Olympics and the FIFA World Cup as primary motivators for their interest. Meanwhile, when asked about the role of advertisers in promoting the growth of women’s sport in the U.S., more than half of survey participants believed that media agencies had a responsibility to do so.

Have you ever found yourself with a football dataset that almost had it all, but left you short of happiness? Time after time, promising datasets failed to deliver the statistics that truly matter – match events, player performances, team results, and season standings.

That time is over!

This in-depth football dataset, curated straight from a RapidAPI endpoint, brings you the data points we've all been waiting for. From fixtures and injuries to goals, assists, and tactical breakdowns, this dataset unlocks the full picture of the beautiful game.

What You Get 🏆 - Fixture Stats & Events: Goals, assists, fouls, and match-defining moments across leagues up to 2024. - Player Performances: From tackles to dribbles, passes, and shots – every stat that makes a difference. - Season Stats & League Standings: Discover how teams dominate, stumble, or rise to glory each season. - Team Insights: Analyze home/away performance, goal-scoring patterns, and defensive strengths. - Match Highlights: Real-time events like own goals, red cards, and critical substitutions. - Injuries & Suspensions: Missing players and their impact on team dynamics. - Iconic Stadiums: Explore venues, capacities, and surfaces that set the stage for football's greatest moments.

Why It’s Exciting 🌟

This isn’t just another football dataset – it’s the ultimate resource for fans, analysts, and strategists who want to dig deeper. Whether you're predicting outcomes, analyzing player form, or crafting the next big football insights project, you now have all the tools you need.

Get ready to unlock stories, trends, and insights like never before – because this time, the stats you actually care about are all here. Let’s kick it off! ⚽✨

In terms of fixture stats for players, the endpoint provides data from 2015 up through the 2024 season and I plan to make one more update at the end of all league/cup seasons in June of 2025.

Disclaimer: This dataset is intended for non-commercial, academic purposes and does not infringe upon any intellectual property rights of the original data providers, including RapidAPI or associated sources. For full details, please refer to the respective terms of use provided by the data sources.

If you have questions about the data or simply want to connect, reach out on LinkedIn and if you plan on using this data for any type of analysis, can you please share that with me!

PS: I am a Ronaldo fan... Suiiiii !!!

Leagues/Cups in datasets: - La Liga - Ligue 1 - Serie A - World Cup - Bundesliga - NWSL Women - Pro League - Championship League - Copa America - Premier League - CONCACAF Gold Cup - Euro Championship - UEFA Europa League - MLS - Africa Cup Of Nations - CONCACAF Champions League

Other Datasets: - Spotify - Zillow

Football Manager Players Dataset

Overview

Dive into the ultimate treasure trove for football enthusiasts, data analysts, and gaming aficionados! The Football Manager Players Dataset is a comprehensive collection of player data extracted from a popular football management simulation game, offering an unparalleled look into the virtual world of football talent. This dataset includes detailed attributes for thousands of players across multiple leagues worldwide, making it a goldmine for analyzing player profiles, scouting virtual stars, and building predictive models for football strategies.

Whether you're a data scientist exploring sports analytics, a football fan curious about your favorite virtual players, or a game developer seeking inspiration, this dataset is your ticket to unlocking endless possibilities!

Dataset Description

This dataset is a meticulously curated compilation of player statistics from five CSV files, merged into a single, unified dataset (merged_players.csv). It captures a diverse range of attributes for players from various clubs, nations, and leagues, including top-tier competitions like the English Premier Division, Argentina's Premier Division, and lower divisions across the globe.

Key Features

• Rich Player Attributes: Over 70 columns covering essential metrics such as:
  • Basic Info: UID, Name, Date of Birth (DOB), Nationality, Height, Weight, Age
  • Club & Position: Club, Position (e.g., AM, DM, GK), Based (league/division)
  • Performance Stats: Caps, Appearances (AT Apps), Goals (AT Gls), League Appearances, League Goals
  • Technical Skills: Acceleration, Passing, Dribbling, Finishing, Tackling, and more
  • Mental Attributes: Work Rate, Vision, Leadership, Determination
  • Physical Attributes: Pace, Strength, Stamina, Agility
  • Market Value: Transfer Value (e.g., $0 to millions)
  • Miscellaneous: Preferred Foot, Media Handling, Injury Proneness
• Global Coverage: Players from diverse regions, including Europe (England, Spain, Italy), South America (Argentina, Brazil), Asia (South Korea, China), Africa (Ivory Coast, Burkina Faso), and North America (USA, Mexico).
• Varied Player Types: From young prospects (15–18 years old) to veteran stars (up to 45 years old), including amateurs, youth players, and professionals.
• Realistic Insights: Includes attributes like Media Description (e.g., "Young winger," "Veteran striker") and injury status, mirroring real-world football dynamics.

Dataset Size

• Rows: Thousands of player records (exact count depends on deduplication).
• Columns: 70+ attributes per player.
• File: merged_players.csv (UTF-8 encoded for compatibility with special characters).

Potential Use Cases

• Sports Analytics:
  • Analyze player attributes to identify key traits for success by position (e.g., what makes a top goalkeeper?).
  • Predict transfer values based on skills, age, and performance stats.
  • Cluster players by playing style or potential using machine learning.
• Scouting & Strategy:
  • Build a dream team by filtering players based on specific attributes (e.g., high Pace and Dribbling for wingers).
  • Compare young talents vs. experienced veterans for team-building strategies.
• Gaming & Modding:
  • Create custom Football Manager databases or mods.
  • Analyze game balance by studying attribute distributions.
• Visualization:
  • Develop interactive dashboards to explore player stats by league, nationality, or position.
  • Map player origins to visualize global football talent distribution.
• Education & Research:
  • Use as a teaching tool for data science, exploring data cleaning, merging, and analysis.
  • Study correlations between mental/physical attributes and in-game performance.

Why This Dataset Stands Out

• Comprehensive: Covers every aspect of a player's profile, from technical skills to personality traits.
• Diverse: Includes players from top-tier to lower divisions, offering a broad spectrum of talent.
• Engaging: Perfect for football fans and data enthusiasts alike, blending gaming with real-world analytics.
• Ready-to-Use: Merged and cleaned for immediate analysis, with consistent column structure across all records.

Getting Started

1. Download: Grab merged_players.csv and load it into your favorite tool (Python/pandas, R, Excel, etc.).
2. Explore: Check out columns like Transfer Value, Position, and Media Description to start your analysis.
3. Analyze: Use Python (e.g., pandas, scikit-learn) or visualization tools (e.g., Tableau, Power BI) to uncover insights.
4. Share: Build models, visualizations, or scouting reports and share your findings with the Kaggle community!

Example Questions to Explore

• Which young players (<18 years) have the highest poten...

College football takes place in organized leagues between teams of students from different universities in the U.S. and Canada. The leagues are organized by the NCAA, which is the sports association that organizes a wide range of sports for colleges and students. In a survey conducted in April 2023, around 22 of all respondents stated that they were avid fans of college football in the United States.

This data set provides data related to measuring consumer behavior in the context of sports marketing among football fans in the Indonesia Premier League. The survey was conducted online using a Google form with a Likert scale. Questions in the questionnaire include marketing variables represented by brand commitment (12 questions), brand trust (4 questions), brand satisfaction (8 questions), brand loyalty (3 questions), and brand attachment (4 questions). The survey was conducted in June–September 2022. A total of 258 football fans across Indonesia were selected using non-probability sampling techniques. Survey data is analyzed using structural equation modeling (SEM) using Smart PLS software to identify estimates of primary construction relationships in the data. The data can help football club managers and business operators in the field of football sports map and plan marketing strategies for organizational development and gain valuable economic benefits. There are three attachments: 1. Analysis of Smart PLS data (this data shows the results of data analysis in the Smart-PLS output format that is exported to Microsoft Excel) 2. Questionnaire: "Sports Marketing in Indonesia: Football Fans" (This data contains the distribution of questionnaire questions to respondents in Microsoft Excel.) 3. Data in Brief: Sports Marketing in Indonesia Soccer Fans_revision This data contains the results of the questionnaire's completion by respondents. Authors replace province-based clusters to facilitate data encoding and reading and avoid multiple interpretations of domicile location in homepage data. The research data was collected using an online survey questionnaire, using a likerts scale of 1-5 accessible through https://forms.gle/Ask9YzAnhKx6yy9. WhatsApp was used to distribute questionnaires to respondents because it is the 3rd largest WhatsApp user in the world [2] with the largest number of football fans reaching 69% [1], as well as considering the effectiveness of research coverage where the Indonesian region consists of diversity. The questions in the questionnaire use Indonesian to facilitate the understanding of respondents in filling out the questionnaire. The English questionnaire is provided as an additional file. The total sample in the study amounted to 258 respondents from various club fans who had their membership status verified by the club's fan leader chairman. Researchers designed survey instruments using research designs based on previous research [1]. Part A of the survey asks about the sociodemographic profile of respondents, including name (optional), gender, occupation, and place of residence. Meanwhile, part B contains questions to measure consumer behavior variables namely commitment, trust, satisfaction, loyalty, and attachment in the context of sports marketing. as shown in Table 1.

Quantitative data were collected using an online survey of football fans and spectators of Arminia Bielefeld from April to June 2022. The link to the survey was provided on the club’s social media platforms, in newsletters, and on the website of the collaborating university. Since the survey was anonymous, it is not possible to report the channel through which respondents were recruited. Overall, 1,021 respondents completed the survey and 1,019 answers could be used for the empirical analysis. The collected respondents’ characteristics and structure were discussed with club officials of Arminia Bielefeld to ensure that the sample is comparable to the perceived fan base of the club.

There are millions of sports fans across the United States, from religiously following an NFL team to being avid tennis fans or watching every Formula One Grand Prix. During an April 2023 survey in the United States, 29 percent of respondents stated that they were avid sports fans.

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.

Context

I am a really huge football fan and the Premier League is one of my favourite football (or soccer, whatever you like to call it) leagues. So, as my very first dataset, I thought this would be a great opportunity for me to make a dataset of player statistics of all seasons from the Premier League.

The Premier League, often referred to as the English Premier League or the EPL outside England, is the top level of the English football league system. Contested by 20 clubs, it operates on a system of promotion and relegation with the English Football League (EFL). Contested by 20 clubs, it operates on a system of promotion and relegation with the English Football League.

Home to some of the most famous clubs, players, managers and stadiums in world football, the Premier League is the most-watched league on the planet with one billion homes watching the action in 188 countries.The league takes place between August and May and involves the teams playing each other home and away across the season, a total of 380 matches.

Three points are awarded for a win, one point for a draw and none for a defeat, with the team with the most points at the end of the season winning the Premier League title. The teams that finish in the bottom three of the league table at the end of the campaign are relegated to the Championship, the second tier of English football. Those teams are replaced by three clubs promoted from the Championship; the sides that finish in first and second place and the third via the end-of-season playoffs.

Details about the dataset

• Some players of certain position may not have certain statistics - For example, A goalkeeper may not have a statistic for "Shot Accuracy"
• The format for the filename is - dataset - {yyyy-mm-dd Date} (The date is date when the file was last updated on)

Content

The data was acquired from:

https://www.premierleague.com/

I made a BeautifulSoup4 Web Scrapper in Python3 which automatically outputs a csv file of all the player statistics. The runtime of the file is about 20 minutes but it varies with the bandwidth of the Internet connection. I made this program so that this dataset could be updated weekly. The reason for weekly update is that the statistics change after each match played by the player so I felt that for the most up-to-date results, such a program is needed. Planning this project took 2 days. Making the program in Python3 took 7 days and the testing and bug fixing took another 5 days. The project was completed in the span of 2 weeks.

Acknowledgements

Source credits : https://www.premierleague.com/ Image credits : https://rb.gy/wuiwth

Inspiration

How do variables like age, nationality and club affect the player performance?

Known issues in the dataset

• Goals per match displays an abnormally high value for a few players as the HTML displays incorrect value during first few milliseconds of loading the page. I am trying to fix it analytically rather than scrapping directly from the website.

Fantasy Football Market Outlook

In 2023, the global fantasy football market size was valued at approximately USD 24.4 billion, and it is projected to reach USD 48.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.9%. This robust growth is driven by the increasing popularity of sports betting, the expansion of internet penetration, and the evolving digital landscape that has made fantasy sports more accessible to a global audience.

The burgeoning interest in fantasy football is significantly fueled by the thrill associated with virtual sports management and the competitive spirit it invokes among participants. The advent of high-speed internet and the proliferation of smartphones have considerably lowered entry barriers, enabling users from diverse demographics to engage with fantasy football platforms. Enhanced user interfaces and the strategic inclusion of real-time data and analytics have further enriched the user experience, making the game more immersive and engaging. Additionally, the growing partnerships between fantasy sports platforms and major sports leagues have enhanced the credibility and reach of the market.

Another crucial growth factor is the increasing monetization avenues within the fantasy football ecosystem. Platforms are leveraging ad revenues, subscription models, and in-app purchases to enhance their profitability. The introduction of innovative revenue streams like virtual goods, personalized content, and premium features provides substantial growth opportunities. Furthermore, the gamification of fantasy sports, including interactive features like social sharing and leaderboards, has significantly contributed to user retention and engagement.

Public perception and societal trends have also played a pivotal role in the market's growth. The cultural acceptance of fantasy sports as a mainstream activity has expanded its demographic reach beyond traditional sports enthusiasts. The integration of fantasy sports into mainstream media, including dedicated shows and podcasts, has increased visibility and user adoption. This cultural shift has also led to the formation of fantasy football communities, fostering a sense of camaraderie and collective enthusiasm.

American Football has played a pivotal role in the evolution of fantasy sports, particularly in North America, where the National Football League (NFL) stands as the most popular league for fantasy football. The deep-rooted passion for American Football among fans has translated into a robust fantasy football culture, with millions of participants engaging in both daily and season-long leagues. The NFL's extensive media coverage and the availability of detailed player statistics have made it an ideal sport for fantasy leagues, offering fans an opportunity to test their managerial skills and engage with the sport on a deeper level. This engagement is further amplified by the NFL's active promotion of fantasy football, which has helped to sustain and grow its fan base over the years.

From a regional perspective, North America continues to dominate the fantasy football market, driven by the high penetration of internet services and the strong sports culture in the region. The United States alone accounts for a significant portion of the market owing to the popularity of the National Football League (NFL). Meanwhile, Europe and the Asia Pacific are emerging as significant growth regions. The increasing popularity of soccer and the rising number of internet users in countries like India and China are expected to contribute to the market's expansion in these regions.

Platform Analysis

The fantasy football market is segmented into mobile applications and websites based on the platform. Mobile applications have revolutionized the fantasy sports experience by offering users the convenience of managing their teams on the go. The advent of sophisticated mobile apps with user-friendly interfaces, real-time updates, and interactive features has significantly enhanced user engagement. The integration of advanced analytics and personalized recommendations in mobile applications has made it easier for users to make informed decisions, thereby increasing user satisfaction and retention.

On the other hand, websites continue to be a popular platform among a segment of users who prefer a more detailed and expansive interface. Websites offer a broader range of features and functionalities compared to mobile applicatio

The Kaggle Premier League dataset is a comprehensive collection of data that covers the performance of Premier League football teams from the seasons 2019/2020 to 2022/2023. The dataset contains detailed information about each team's matches, including match scores, dates, venue, and other important statistics. The dataset is an invaluable resource for analysing the performance trends of individual teams and players over the years, identifying patterns in team and player behaviour, and making data-driven decisions based on the insights gained from the data. Whether you are a football fan, analyst, or researcher, this dataset provides an excellent opportunity to gain deep insights into the world's most popular sport.

Fifa Index is a website dedicated to providing in-depth information on the world of football. The company's primary focus is on gathering and presenting data on players, teams, and tournaments to fans and enthusiasts alike. As a leading provider of football data, Fifa Index is committed to accuracy, ensuring that the information presented is reliable and up-to-date.

Fifa Index's data covers a wide range of topics, including player statistics, team ratings, and tournament results. The company's extensive database provides a valuable resource for individuals looking to analyze and understand the sport. With its expertise in data analysis and presentation, Fifa Index is an essential destination for anyone seeking to stay informed on the world of football.

Context For most football fans, May - July represents a lull period due to the lack of club football. What makes up for it, is the intense transfer speculation that surrounds all major player transfers today. Their market valuations also lead to a few raised eyebrows, lately more than ever. I was curious to see how good a proxy popularity could be for ability, and the predictive power it would have in a model estimating a player's market value.

Content name: Name of the player

club: Club of the player

age : Age of the player

position : The usual position on the pitch

position_cat :

1 for attackers

2 for midfielders

3 for defenders

4 for goalkeepers

market_value : As on transfermrkt.com on July 20th, 2017

page_views : Average daily Wikipedia page views from September 1, 2016 to May 1, 2017

fpl_value : Value in Fantasy Premier League as on July 20th, 2017

fpl_sel : % of FPL players who have selected that player in their team

fpl_points : FPL points accumulated over the previous season

region:

1 for England

2 for EU

3 for Americas

4 for Rest of World

nationality

new_foreign : Whether a new signing from a different league, for 2017/18 (till 20th July)

age_cat

club_id

big_club: Whether one of the Top 6 clubs

new_signing: Whether a new signing for 2017/18 (till 20th July)

Inspiration To statistically analyse the beautiful game.

Context

To all the football fans out there, this dataset might interest you.

Content

The dataset contains information about how the interest of people has changed over the years with regards to football, what are the queries that they search for often and many other details.

Acknowledgements

The dataset is taken from the google trends website.

Inspiration

It will be interesting to analyse the trend in the interest of people in football over time and by region. What are some hot topics that people search for and other beautiful insights?

Financial overview and grant giving statistics of Roxbury Football Fan Club

Dataset and statistical report of our analyses as supplement to the scientific paper published in "Physiology & Behavior": Burk, C. L., Mayer, A., & Wiese, B. S. (in press). Nail-biters and thrashing wins: testosterone responses of football fans during World Cup matches. Physiology & Behavior. The Dataset is provided as SPSS file; the statistical report is provided in pdf and R Markdown formats. Key contents of the report: - the documentation of variables in the dataset - an ICC model without any variables except the dependent variable - the empty model with match phases, but without control variables - the basic model with essential control variables, but without moderators - moderation analyses - multi-group models for investigating sex differences - additional analyses for investigating pre match baseline variation - the double-check analysis excluding T values lying outside the triple interquartile range

This statistic displays the result of a survey on the common methods of football fans to get informed on global football updates in France in 2016. The survey was conducted online and gathered data from ***** self-identified football fance from France, asking them where they go to be informed on global football updates. In 2016, it was found that ** percent of respondents stated that they get information about global football updates on sport sites.

IntroductionIn the modern competitive landscape of football, clubs are increasingly leveraging data-driven decision-making to strengthen their commercial positions, particularly against rival clubs. The strategic allocation of resources to attract and retain profitable fans who exhibit long-term loyalty is crucial for advancing a club's marketing efforts. While the Recency, Frequency, and Monetary (RFM) customer segmentation technique has seen widespread application in various industries for predicting customer behavior, its adoption within the football industry remains underexplored. This study aims to address this gap by introducing an adjusted RFM approach, enhanced with the Analytic Hierarchy Process (AHP) and unsupervised machine learning, to effectively segment football fans based on Customer Lifetime Value (CLV).MethodsThis research employs a novel weighted RFM method where the significance of each RFM component is quantified using the AHP method. The study utilizes a dataset comprising 500,591 anonymized merchandising transactions from Amsterdamsche Football Club Ajax (AFC Ajax). The derived weights for the RFM variables are 0.409 for Monetary, 0.343 for Frequency, and 0.248 for Recency. These weights are then integrated into a clustering framework using unsupervised machine learning algorithms to segment fans based on their weighted RFM values. The simple weighted sum approach is subsequently applied to estimate the CLV ranking for each fan, enabling the identification of distinct fan segments.ResultsThe analysis reveals eight distinct fan clusters, each characterized by unique behaviors and value contributions: The Golden Fans (clusters 1 and 2) exhibit the most favourable scores across the recency, frequency, and monetary metrics, making them relatively the most valuable. They are critical to the club's profitability and should be rewarded through loyalty programs and exclusive services. The Promising segment (cluster 3) shows potential to ascend to Golden Fan status with increased spending. Targeted marketing campaigns and incentives can stimulate this transition. The Needs Attention segment (cluster 4) are formerly loyal fans whose engagement has diminished. Re-engagement strategies are vital to prevent further churn. The New Fans segment (clusters 5 and 6) are fans who have recently transacted and show potential for growth with proper engagement and personalized offerings. Lastly, the Churned/Low Value segment (clusters 7 and 8) are fans who relatively contribute the least and may require price incentives to potentially re-engage, though they hold relatively lower priority compared to other segments.DiscussionThe findings validate the proposed method's utility through its application to AFC Ajax's Customer Relationship Management (CRM) data and provides a robust framework for fan segmentation in the football industry. The approach offers actionable insights that can significantly enhance marketing strategies by identifying and prioritizing high-value segments based on the club's preferences and requirements. By maintaining the loyalty of Golden Fans and nurturing the Promising segment, football clubs can achieve substantial gains in profitability and fan engagement. Additionally, the study underscores the necessity of re-engaging formerly loyal fans and fostering new fans' growth to enable long-term commercial success. This methodology not only aims to bridge a research gap, but also equips marketing practitioners with data-driven tools for effective and efficient customer segmentation in the football industry.

European Top 5 Leagues Match Data 2023/24 Season

Hey football fans! 👋

I've put together a comprehensive dataset covering all matches from Europe's top 5 leagues for the 2023/24 season. This includes every game from the

• English Premier League
• Spanish La Liga
• German Bundesliga
• Italian Serie A
• French Ligue 1 (I was equally surprised seeing it was in the top 5 )

Data

The data is split into 5 separate CSV files - one for each league. For easier analysis, I've kept the format consistent across all leagues:

• date: Match date (YYYY-MM-DD)
• matchday: Round number in the season
• home_team: Name of the home team
• away_team: Name of the visiting team
• home_score_full_time: Home team's goals at full-time
• away_score_full_time: Away team's goals at full-time
• home_score_half_time: Home team's goals at half-time
• away_score_half_time: Away team's goals at half-time
• goal_diff: Goal difference
• winner: Name of the winning team
• defeat: Name of the defeated team
• ref: Name of the match's referee

How Can You Use This?

You can use this dataset to: - Track how teams perform before and after the break - Analyze home vs away form - Study scoring patterns across different leagues - Compare goal differences

Feel free to use it for your projects and don't hesitate to reach out if you spot anything that needs fixing!

Remember to credit if you use this data for your analysis or projects. SIUUUU! ⚽️

Note: All match data is sourced from official league records.