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

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?

This statistic illustrates the results of a survey among people from Russia on devices used to watch the 2018 FIFA World Cup. According to data provided by IPSOS, ** percent of Russian respondents stated that they will watch the games on a television set, whereas ** percent planned to watch parts of the World Cup on the internet.

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.

The Tampa Bay Rays, a professional baseball team, is a beloved organization in the world of sports. They were established in 1998 and have since become known for their commitment to innovation and community involvement. As a major league team, they have a strong presence in the Tampa Bay area, drawing in fans from all over the region.

For those interested in learning more about the Tampa Bay Rays, they can access a wide range of information through their website. From statistics and rosters to news and scores, the site provides fans with a wealth of knowledge about the team. Whether you're a die-hard supporter or just a casual fan, the Tampa Bay Rays' website is the perfect place to stay up-to-date on all the latest developments in the world of baseball.

Context

International football, also known as soccer, is a sport played by teams from countries around the world. The most prestigious international competition in football is the FIFA World Cup, which is held every four years and features teams from over 200 countries. Other major international tournaments include the UEFA European Championship, the AFC Asian Cup, the CAF Africa Cup of Nations, and the CONMEBOL Copa America.

Content

International football, or soccer as it is known in some countries, has a long and storied history that dates back to the late 19th century. The first international football match was played between Scotland and England in Glasgow, Scotland on November 30, 1872. The match ended in a 0-0 draw, and it was the first of many international contests that would be played between national teams around the world. Over the years, international football has grown in popularity and become a major global sport, with teams from more than 200 countries competing against each other in various tournaments and leagues. Some of the most well-known international football tournaments include the FIFA World Cup, the UEFA European Championship, and the AFC Asian Cup. The FIFA World Cup, which is held every four years, is the most prestigious international football tournament and attracts the best teams from around the world. The first World Cup was held in 1930 in Uruguay, and since then it has been held in a different country each time. Brazil has won the most World Cup titles, with a total of five victories, while Germany and Italy have each won four titles. The UEFA European Championship, which is also held every four years, is a major international football tournament that features teams from Europe. The first European Championship was held in 1960, and it has been held every four years since then. The most successful team in the history of the European Championship is Germany, which has won the tournament four times. In addition to these major tournaments, there are also many other international football competitions that are held around the world, including the AFC Asian Cup, the CAF Africa Cup of Nations, and the CONCACAF Gold Cup. As the sport has evolved over the years, it has also faced its share of controversies and challenges. In the early days of international football, there were often disputes over rules and regulations, and teams from different countries sometimes had difficulty agreeing on a common set of rules. In more recent years, issues such as doping, match-fixing, and racism have also plagued the sport. Despite these challenges, international football remains one of the most popular and widely-followed sports in the world, with millions of fans and players around the globe. As the sport continues to grow and evolve in the coming years, it is sure to remain a major part of the global sporting landscape. Here is a list of some football (soccer) teams that have changed their names in the past: 1. Manchester United FC (Old name: Newton Heath LYR FC) 2. FC Barcelona (Old name: Foot-Ball Club Barcelona) 3. Bayern Munich (Old name: FC Bayern Munich) 4. Juventus FC (Old name: Sport Club Juventus) 5. Paris Saint-Germain FC (Old name: Paris FC) 6. AC Milan (Old name: Milan Foot-Ball and Cricket Club) 7. AS Roma (Old name: Roman Football Club) 8. Ajax Amsterdam (Old name: Amsterdamsche Football Club Ajax) 9. Inter Milan (Old name: Internazionale Football Club Milan) 10. Liverpool FC (Old name: Everton FC)

Acknowledgements

The data is assemble from several sources along with but not limited to Wikipedia, rsssf.com, and individual football associations' websites

Inspiration

1. The passion and pride that players and fans feel for their national teams
2. The thrill of watching top-level international competition and seeing the world's best players face off against one another
3. The sense of unity and community that football can bring, as people from all walks of life come together to support their team
4. The opportunity to experience new cultures and ways of life through the lens of football, as the sport is played and followed in nearly every country on earth
5. The exciting atmosphere of major international tournaments, with packed stadiums and fans from all around the world cheering on their teams
6. The inspiring stories of underdogs and lesser-known nations who rise to the occasion on the international stage. # Columns details. decision.csv including columns: date: the date the game was played.

home_team: the team that played the game on their home field.
away_team: the team that played the game as the visiting team.
home_score: the number of goa...

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

Spectator violence is a relatively common phenomenon in various parts of the world, especially during football/soccer matches. Increasing violence has been cited as a reason for reduced fan attendance at football matches in Mexico. This study looks at Mexican fans' perception of violence to explore whether it is impacting attendance. The database includes responses from 406 fans of first or second division Mexican teams, 31% female, 69% male, 40% between 18 and 29, 40% between 30 and 49, 20% over 50, 41% have a university degree. 89% attend at least one match per year and 78% have witnessed some violence at the stadium, including verbal or physical aggression, or brawling. 70% say that have changed their attendance habits due to violence. The database is in Spanish. The data were collected via a survey distributed by a professional research firm.

The graph shows data on the share of FIFA World Cup fans worldwide in 2018, by gender. According to the source, ** percent of World Cup fans worldwide in 2018 were male.

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

Premier League Players Performance Dataset

This dataset provides a comprehensive overview of player performance in the Premier League capturing a wide array of metrics related to gameplay, scoring, passing, and defensive actions. With records detailing individual player statistics across different teams, this dataset is a valuable resource for analysts, data scientists, and fans who are interested in diving into player performance data from one of the world’s top soccer leagues.

Each entry represents a single player's profile, featuring data on expected goals (xG), expected assists (xAG), touches, dribbles, tackles, and more. This dataset is ideal for analyzing various aspects of player contribution, both offensively and defensively, and understanding their impact on team performance.

Dataset Columns

Player: Name of the player Team: Team the player belongs to '#' : Player's jersey number Nation: Nationality of the player Position: Primary playing position on the field Age: Age of the player Minutes: Total minutes played Goals: Number of goals scored Assists: Number of assists Penalty Shoot on Goal: Penalty shots taken on goal Penalty Shoot: Total penalty shots attempted Total Shoot: Total shots attempted Shoot on Target: Shots successfully on target Yellow Cards: Number of yellow cards received Red Cards: Number of red cards received Touches: Total ball touches Dribbles: Total dribbles attempted Tackles: Total tackles made Blocks: Total blocks Expected Goals (xG): Expected goals, calculated based on shooting positions and likelihood of scoring Non-Penalty xG (npxG): Expected goals excluding penalties Expected Assists (xAG): Expected assists, based on actions leading to an expected goal (xG) Shot-Creating Actions: Actions leading to a shot attempt Goal-Creating Actions: Actions leading to a goal Passes Completed: Successful passes completed Passes Attempted: Total passes attempted Pass Completion %: Pass completion rate, expressed as a percentage (some entries have missing values here) Progressive Passes: Passes advancing the ball significantly toward the opponent’s goal Carries: Total ball carries Progressive Carries: Carries advancing the ball significantly toward the opponent’s goal Dribble Attempts: Total dribbles attempted Successful Dribbles: Total successful dribbles Date: Date of record collection or game date

Potential Use Cases

Data Visualization: Explore relationships between various performance metrics to identify patterns.

Player Comparisons: Compare individual players based on goals, assists, xG, xAG, and other metrics.

Team Analysis: Evaluate contributions of players within the same team to gain insights into team dynamics.

Predictive Modeling: Use the dataset to build models for predicting game outcomes, goals, or assists based on player performance metrics.

Sports have been an integral part of human culture for centuries, and the world of sports is constantly evolving. This dataset provides a comprehensive analysis of the attributes that matter in different sports. It contains information on the physical, mental, and tactical attributes that contribute to success in different sports, from basketball to soccer to swimming.

This dataset is a valuable resource for coaches, trainers, and athletes who want to gain a deeper understanding of the specific attributes that are essential to success in their sport. By analyzing the data, they can identify areas where they need to focus their training and development efforts, and develop strategies to maximize their performance.

In addition, the dataset is of interest to sports researchers and analysts who want to gain insights into the factors that contribute to success in different sports. By examining the data, they can explore questions such as: What are the most important physical attributes for success in basketball? How do mental attributes contribute to success in soccer? Which tactical attributes are most important in swimming?

Overall, this dataset is an invaluable tool for anyone interested in sports, whether they are athletes, coaches, researchers, or fans. By analyzing the data, they can gain a deeper understanding of the attributes that matter most in different sports, and use this knowledge to improve performance, develop strategies, and gain a competitive edge.

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.

The Official Football Kits market has evolved significantly over the years, driven by a growing passion for the sport and an increasing number of fans around the globe. These kits, which are essential for professional teams and amateur clubs alike, are more than just uniforms; they represent team identity, fan loyal

NFL Wide Receiver Career Stats and Aging Trends

Examining Target, Yards-Per-Attempt, and Overall Rating Data

By FiveThirtyEight [source]

About this dataset

This repository contains a comprehensive database on the careers of NFL wide receivers, examining their performance over time to offer insights into physical changes and playing style over the years. With data stretching back all the way to 1990, it reveals key changes in stats and ratings -- including age_from/age_to, trypg_change, career_try/career_ranypa/career_wowy, and bcs_rating -- that provide essential information for football fans looking to understand the history and evolution of this position in American football. This dataset is made available under Creative Commons Attribution 4.0 International License as well as MIT License with hopes of facilitating more public understanding and transparency on this subject. We invite anyone who finds it useful to share their stories by contacting us at andrei.scheinkmanfivethirtyeight.com

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Featured Notebooks

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How to use the dataset

In order to get started using this dataset: - Read through the columns of data to better understand what is being measured and how it relates to an individual player's performance. - Explore the data by filtering it in different ways (such as looking at only high rated players or seeing how older players fared compared with younger ones). - See if there are any patterns in how certain traits (such as age) affect a player's performance over time by creating graphs or other visualizations that explore these relationships over time.
- Use these findings to draw your own conclusions about trends in NFL wide receiver aging curves or team strategies related to scouting opportunities for certain players throughout different stages of their career development journey from rookies all the way through veterans who are retiring from playing football professionally on any given year during an off-season year . . . or even beyond!

Research Ideas

• Analyzing the performance of NFL wide receivers over time by comparing their age-from and age-to stats.
• Comparing the AV rating of NFL wide receivers to their total career receiving yards per attempt.
• Comparing the career wowy stats of NFL wide receivers to their total career targets in order to assess efficiency levels across different players

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: try-per-game-aging-curve.csv | Column name | Description | |:-----------------|:------------------------------------------------------------------------------------------------------------| | age_from | Age when the career started. (Integer) | | age_to | Age when the career ended. (Integer) | | trypg_change | Change in the wide receiver's total receiving yards per game from the start to end of their career. (Float) |

File: advanced-historical.csv | Column name | Description | |:------------------|:-----------------------------------------------------------| | player_name | Name of the NFL wide receiver. (String) | | career_try | Total number of career targets. (Integer) | | career_ranypa | Average number of receiving yards per attempt. (Float) | | career_wowy | Average number of yards per target. (Float) | | bcs_rating | Player's overall rating according to BCS system. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit FiveThirtyEight.

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.

Context:

The FIFA World Cup (often simply called the World Cup ),  being the most prestigious association football tournament, as well as the most widely viewed and followed sporting event in the world, was one of the Top Trending topics frequently on Twitter while ongoing.

This dataset contains a random collection of tweets starting from the Knockout stages till the World Cup Final that took place on 15 July, 2018.
A preliminary analysis from the data (till the Knockout stages) is available at:
https://medium.com/@ritu_rg/nlp-text-visualization-twitter-sentiment-analysis-in-r-5ac22c778448

Content:

Data Collection:
The dataset was created using the Tweepy API, by streaming tweets from world-wide football fans before, during or after the matches.
Tweepy is a Python API for accessing the Twitter API, that provides an easy-to-use interface for streaming real-time data from Twitter. More information related to this API can be found at: http://tweepy.readthedocs.io/en/v3.5.0/

Data Pre-processing:
The dataset includes English language tweets containing any references to FIFA or the World Cup. The collected tweets have been pre-processed to facilitate analysis , while trying to ensure that any information from the original tweets is not lost.
- The original tweet has been stored in the column "Orig_tweet".
- As part of pre-processing, using the "BeautifulSoup" & "regex" libraries in Python, the tweets have been cleaned off any nuances as required for natural language processing, such as website names, hashtags, user mentions, special characters, RTs, tabs, heading/trailing/multiple spaces, among others.
- Words containing extensions such as n't 'll 're 've have been replaced with their proper English language counterparts. Duplicate tweets have been removed from the dataset.
- The original Hashtags & User Mentions extracted during the above step have also been stored in separate columns.

Data Storage:
The collected tweets have been consolidated into a single dataset & shared as a Comma Separated Values file.
Each tweet is uniquely identifiable by its ID, & characterized by the following attributes, per availability:
- "Lang" - Language of the tweet
- "Date" - When it was tweeted
- "Source" - The device/medium where it was tweeted from
- "len" - The length of the tweet
- "Orig_Tweet" - The tweet in its original form
- "Tweet" - The updated tweet after pre-processing
- "Likes" - The number of likes received by the tweet (till the time the extraction was done)
- "RTs" - The number of times the tweet was shared
- "Hashtags" - The Hashtags found in the original tweet
- "UserMentionNames" & "UserMentionID" -  xtracted from the original tweet

It also includes the following attributes about the person that the tweet is from:
- "Name" & "Place" of the user
- "Followers" - The number of followers that the user account has
- "Friends" - The number of friends the user account has

Acknowledgements:

The following resources have helped me through using the Tweepy API:
http://tweepy.readthedocs.io/en/v3.5.0/auth_tutorial.html
https://developer.twitter.com/en/docs/tweets/search/api-reference/get-search-tweets
https://www.safaribooksonline.com/library/view/mining-the-social/9781449368180/ch01.html

Inspiration:

This project gave me a fascinating look into the conversations & sentiments of people from all over the world, who were following this prestigious football tournament, while also giving me the opportunity to explore some of the streaming, natural language processing & visualizations techniques in both R & Python

The dataset created using the ESPN Football News Scraper aims to provide comprehensive, up-to-date information on football news. This scraper is designed to extract articles from ESPN, one of the leading sports news providers globally, ensuring that users have access to the latest football-related updates, match reports, player interviews, transfer news, and expert analysis.

The primary source for this dataset is ESPN's football section. ESPN is renowned for its extensive coverage of sports, including in-depth reporting on various football leagues, tournaments, and events. By leveraging the ESPN Football News Scraper, the dataset captures:

• Match Reports: Detailed analyses and summaries of football matches from major leagues and tournaments.
• Player Interviews and Profiles: Insights into players' careers, personal stories, and exclusive interviews.
• Transfer News: Updates on player transfers, rumors, and confirmed deals.
• Expert Analysis: Opinions and analyses from football pundits and experts.
• Injury Updates: Information on player injuries and their impact on teams.
• Team News: Updates on team strategies, management decisions, and other relevant news.

The inspiration behind creating this dataset is to cater to the needs of football enthusiasts, analysts, and content creators who require reliable and timely information about football. By aggregating news from a reputable source like ESPN, the dataset offers a rich repository of information that can be used for:

• Content Creation: Assisting bloggers, vloggers, and social media influencers in creating engaging football-related content.
• Data Analysis: Enabling sports analysts and researchers to perform data-driven analyses and generate insights.
• Fan Engagement: Providing fans with a one-stop source for all the latest football news and updates.
• Marketing and Promotion: Assisting marketers in creating targeted campaigns based on the latest trends and happenings in the football world.

This dataset aims to be a valuable resource for anyone interested in football, offering a consolidated view of the sport's dynamic and ever-changing landscape.

La Liga Players Performance Dataset

This dataset provides a comprehensive overview of player performance in the La Liga capturing a wide array of metrics related to gameplay, scoring, passing, and defensive actions. With records detailing individual player statistics across different teams, this dataset is a valuable resource for analysts, data scientists, and fans who are interested in diving into player performance data from one of the world’s top soccer leagues.

Each entry represents a single player's profile, featuring data on expected goals (xG), expected assists (xAG), touches, dribbles, tackles, and more. This dataset is ideal for analyzing various aspects of player contribution, both offensively and defensively, and understanding their impact on team performance.

Dataset Columns

Player: Name of the player Team: Team the player belongs to '#' : Player's jersey number Nation: Nationality of the player Position: Primary playing position on the field Age: Age of the player Minutes: Total minutes played Goals: Number of goals scored Assists: Number of assists Penalty Shoot on Goal: Penalty shots taken on goal Penalty Shoot: Total penalty shots attempted Total Shoot: Total shots attempted Shoot on Target: Shots successfully on target Yellow Cards: Number of yellow cards received Red Cards: Number of red cards received Touches: Total ball touches Dribbles: Total dribbles attempted Tackles: Total tackles made Blocks: Total blocks Expected Goals (xG): Expected goals, calculated based on shooting positions and likelihood of scoring Non-Penalty xG (npxG): Expected goals excluding penalties Expected Assists (xAG): Expected assists, based on actions leading to an expected goal (xG) Shot-Creating Actions: Actions leading to a shot attempt Goal-Creating Actions: Actions leading to a goal Passes Completed: Successful passes completed Passes Attempted: Total passes attempted Pass Completion %: Pass completion rate, expressed as a percentage (some entries have missing values here) Progressive Passes: Passes advancing the ball significantly toward the opponent’s goal Carries: Total ball carries Progressive Carries: Carries advancing the ball significantly toward the opponent’s goal Dribble Attempts: Total dribbles attempted Successful Dribbles: Total successful dribbles Date: Date of record collection or game date

Potential Use Cases

Data Visualization: Explore relationships between various performance metrics to identify patterns.

Player Comparisons: Compare individual players based on goals, assists, xG, xAG, and other metrics.

Team Analysis: Evaluate contributions of players within the same team to gain insights into team dynamics.

Predictive Modeling: Use the dataset to build models for predicting game outcomes, goals, or assists based on player performance metrics.

Liverpool Football Club's Matches (English League)

Manager Nationality, Games and Odds

By Ryan Estrellado [source]

About this dataset

This dataset reveals the history of Liverpool Football Club's success in the English League matches since 1892. Analyze this data to explore all aspects of their match performance, including their manager's experience, win percentage, and other statistics. Learn how each manager has contributed to the achievement of Liverpool FC in each season they have been at the helm while examining their nationality and tenure at Liverpool. See which managers helped Liverpool secure trophies such as FA Cup, League Cup, European Cup/Champions League and UEFA Super Cup triumphs over time. Study closely how changes in odds calculation were reflected in performances on field or understand how certain trends related to differences between away and home games' results also inform future decisions for any team aspiring to be a part of English football’s elite clubs without fail. Take advantage of this collection today and win more knowledge about the iconic successes that many legendary managers have brought forth from Anfield to delight fans from around world!

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How to use the dataset

This dataset contains the win ratios of Liverpool Football Club in English League matches for the period from 1892. It includes detailed information on each manager's role, tenure, win percentage and other metrics. This data can be used to provide key insights into the team’s performance and the strategies put in place by their managers throughout this time period.

Research Ideas

• Analyzing the tenure of a manager to identify the optimal amount of time for a successful reign. This can be done by plotting win percentages against the duration of their managerial term at Liverpool FC, with information about leagues and cups won or lost as further arguments for success or failure.
• Comparing win ratios from different eras in order to identify significant patterns and possible trends in the long-term history of LFC's performance on English League matches, taking into account factors such as changing legue rules and mentalities on football tactics over time
• Examining correlations between team performance, manager nationality and period in office, to assess whether there is any relationship between them that could help predict team outcomes in future matches based on these characteristics

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: wiki_lfc_mngrs.csv | Column name | Description | |:----------------|:------------------------------------------------------------------------------| | Name | Name of the manager. (String) | | Nationality | Nationality of the manager. (String) | | From | Start year of the manager's tenure. (Integer) | | To | End year of the manager's tenure. (Integer) | | P | Total number of matches played in the English League season. (Integer) | | W | Total number of wins in the English League season. (Integer) | | D | Total number of draws in the English League season. (Integer) | | Win % | Win percentage of the English League season. (Float) | | L1 | Total number of home wins in the English League season. (Integer) | | L2 | Total number of away wins in the English League season. (Integer) | | FA | Total number of FA Cup wins in the English League season. (Integer) | | **L...