The FIFA Football Players Dataset is a comprehensive collection of information about football (soccer) players from around the world. This dataset offers a wealth of attributes related to each player, making it a valuable resource for various analyses and insights into the realm of football, both for gaming enthusiasts and real-world sports enthusiasts.

Attributes:

• Player: The name of the football player.
• Country: The nationality or home country of the player.
• Height: The height of the player in centimeters.
• Weight: The weight of the player in kilograms.
• Age: The age of the player.
• Club: The club to which the player is currently affiliated.
• Ball Control: Player's skill in controlling the ball.
• Dribbling: Player's dribbling ability.
• Marking: Player's marking skill.
• Slide Tackle: Player's ability to perform slide tackles.
• Stand Tackle: Player's ability to perform standing tackles.
• Aggression: Player's aggression level.
• Reactions: Player's reaction time.
• Attacking Position: Player's positioning for attacking plays.
• Interceptions: Player's skill in intercepting passes.
• Vision: Player's vision on the field.
• Composure: Player's composure under pressure.
• Crossing: Player's ability to deliver crosses.
• Short Pass: Player's short passing accuracy.
• Long Pass: Player's ability in long passing.
• Acceleration: Player's acceleration on the field.
• Stamina: Player's stamina level.
• Strength: Player's physical strength.
• Balance: Player's balance while playing.
• Sprint Speed: Player's speed in sprints.
• Agility: Player's agility in maneuvering.
• Jumping: Player's jumping ability.
• Heading: Player's heading skills.
• Shot Power: Player's power in shooting.
• Finishing: Player's finishing skills.
• Long Shots: Player's ability to make long-range shots.
• Curve: Player's ability to curve the ball.
• Free Kick Accuracy: Player's accuracy in free-kick situations.
• Penalties: Player's penalty-taking skills.
• Volleys: Player's volleying skills.
• Goalkeeper Positioning: Goalkeeper's positioning attribute (specific to goalkeepers).
• Goalkeeper Diving: Goalkeeper's diving ability (specific to goalkeepers).
• Goalkeeper Handling: Goalkeeper's ball-handling skill (specific to goalkeepers).
• Goalkeeper Kicking: Goalkeeper's kicking ability (specific to goalkeepers).
• Goalkeeper Reflexes: Goalkeeper's reflexes (specific to goalkeepers).
• Value: The estimated value of the player.

Potential Uses:

Player Performance Analysis: Evaluate the performance of football players based on their attributes. Club Analysis: Investigate clubs, player distribution, and club statistics. Positional Insights: Explore the attributes specific to player positions. Player Valuation Trends: Analyze how player values change over time. Data Visualization:Create visualizations for better data representation. Machine Learning Models: Develop predictive models for various football-related forecasts.

Before using the dataset for analysis, it's advisable to preprocess the data, such as converting the "value" column into a numerical format, handling missing values, and ensuring consistency in column names. This dataset is a valuable resource for gaining insights into football, both in the context of the FIFA video game and real-world football.

All thanks and credit goes to FIFA Index

During the 2022 FIFA Men's World Cup in Qatar, the Australian team, the 'Socceroos', played a total of **** matches. During those matches, midfielder Aaron Mooy topped the team rank for the number of passes with *** passes, while Craig Goodwin made the most crosses with **.

Match stats for all UEFA Euro competition matches (including qualification matches) from 2002 to today (last update 29 June 2021) available on ESPN database. FIFA World Cup data can be found here: https://www.kaggle.com/kaito510/fifa-world-cup-match-stats

Match Stats include: - Score - Possession - Shots On Target - Shots - Yellow Cards - Red Cards - Fouls - Saves

The dataset column names' first character indicates home team and away team. For example, "hscore" and "ascore" columns contain goals scored by home teams and away teams respectively. But note some matches are played in neutral venues.

Data was scraped from: https://www.espn.com/soccer/scoreboard Firstly the match IDs were scraped with this code: https://www.kaggle.com/kaito510/euromatchidscraper Secodly the match stats were scraped with this code: https://www.kaggle.com/kaito510/euromatchstatsscraper

Description:

This comprehensive dataset offers detailed information on approximately 17,000 FIFA football players, meticulously scraped from SoFIFA.com.

It encompasses a wide array of player-specific data points, including but not limited to player names, nationalities, clubs, player ratings, potential, positions, ages, and various skill attributes. This dataset is ideal for football enthusiasts, data analysts, and researchers seeking to conduct in-depth analysis, statistical studies, or machine learning projects related to football players' performance, characteristics, and career progressions.

Features:

• name: Name of the player.
• full_name: Full name of the player.
• birth_date: Date of birth of the player.
• age: Age of the player.
• height_cm: Player's height in centimeters.
• weight_kgs: Player's weight in kilograms.
• positions: Positions the player can play.
• nationality: Player's nationality.
• overall_rating: Overall rating of the player in FIFA.
• potential: Potential rating of the player in FIFA.
• value_euro: Market value of the player in euros.
• wage_euro: Weekly wage of the player in euros.
• preferred_foot: Player's preferred foot.
• international_reputation(1-5): International reputation rating from 1 to 5.
• weak_foot(1-5): Rating of the player's weaker foot from 1 to 5.
• skill_moves(1-5): Skill moves rating from 1 to 5.
• body_type: Player's body type.
• release_clause_euro: Release clause of the player in euros.
• national_team: National team of the player.
• national_rating: Rating in the national team.
• national_team_position: Position in the national team.
• national_jersey_number: Jersey number in the national team.
• crossing: Rating for crossing ability.
• finishing: Rating for finishing ability.
• heading_accuracy: Rating for heading accuracy.
• short_passing: Rating for short passing ability.
• volleys: Rating for volleys.
• dribbling: Rating for dribbling.
• curve: Rating for curve shots.
• freekick_accuracy: Rating for free kick accuracy.
• long_passing: Rating for long passing.
• ball_control: Rating for ball control.
• acceleration: Rating for acceleration.
• sprint_speed: Rating for sprint speed.
• agility: Rating for agility.
• reactions: Rating for reactions.
• balance: Rating for balance.
• shot_power: Rating for shot power.
• jumping: Rating for jumping.
• stamina: Rating for stamina.
• strength: Rating for strength.
• long_shots: Rating for long shots.
• aggression: Rating for aggression.
• interceptions: Rating for interceptions.
• positioning: Rating for positioning.
• vision: Rating for vision.
• penalties: Rating for penalties.
• composure: Rating for composure.
• marking: Rating for marking.
• standing_tackle: Rating for standing tackle.
• sliding_tackle: Rating for sliding tackle.

Use Case:

This dataset is ideal for data analysis, predictive modeling, and machine learning projects. It can be used for:

• Player performance analysis and comparison.
• Market value assessment and wage prediction.
• Team composition and strategy planning.
• Machine learning models to predict future player potential and career trajectories.

Note:

Please ensure to adhere to the terms of service of SoFIFA.com and relevant data protection laws when using this dataset. The dataset is intended for educational and research purposes only and should not be used for commercial gains without proper authorization.

FIFA updates its ranking of national soccer teams several times each year, using real-life results to determine where each country places worldwide. As of July 2025, Argentina led the men's rankings, having won the World Cup in 2022. Brazil was in fifth place, while France ranked third. Meanwhile, the highest-ranked country in women's soccer, the United States, ranked 15th. Which country has won the most World Cups? Following the 2022 FIFA World Cup, Brazil remained the country with the most men’s World Cup titles, with a total of five. However, the Seleção’s last success came in 2002, and the team has mostly failed to progress past the quarter-final stage in recent tournaments. Meanwhile, two countries have won four World Cups: Germany and Italy. While Germany last won the competition in 2014, Italy failed to even qualify in 2018 and 2022. However, the Azzurri have had success in other areas, winning a second UEFA European Championship title in 2021. Who are the best soccer players worldwide? While past debates have largely focused on Lionel Messi and Cristiano Ronaldo, a number of younger players have taken the sport by storm in recent years. Erling Haaland, one of the most valuable soccer players in the world, has impressed many since joining Manchester City. In his first season at the club, Haaland broke the record for the most goals scored in a single Premier League season, with 36. Meanwhile, Haaland’s former teammate Jude Bellingham made an immediate impact at Real Madrid, scoring 10 goals in his first 10 games.

As of February 28, 2023, Cristiano Ronaldo and Lionel Messi were the most followed football players on social media, with the former having a total of 832 million followers across all platforms. Meanwhile, four out of the ten most followed soccer players played for Paris Saint-Germain Football on different online networks As of 2023, football clubs and players received the most engagement from fans on Instagram. This Meta platform was home to 63 percent of the social media audience of football players and 34 percent of followers of football clubs. Furthermore, football clubs also saw high followings on Facebook, X (formerly known as Twitter), and the China-based network Weibo.   Football stars, social media sensations In addition to being the most followed football players on social media, Lionel Messi and Cristiano Ronaldo have achieved other important milestones on online networks. As of April 2024, Cristiano Ronaldo did not only have the largest social media following in relation to other football players, but he was also the individual with the most Instagram followers in general, ranking second in total following only after Instagram’s official page. Messi ranked third after Ronaldo with 502 million followers on Instagram, placing him above celebrities such as Selena Gomez and Kylie Jenner on the Meta-owned platform. In addition, the most liked Instagram post on the platform as of April 2024 was of Lionel Messi and his teammates after winning the FIFA 2022 World Cup, which generated over 75 million likes. As of 2024, Messi was behind five of the top ten most popular posts of all time on Instagram.  

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

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

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

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

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

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

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

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Comprehensive football statistics on matches where both teams score, including goals and betting insights. Updated daily.

I didn't realise how many soccer games are played each year until I started collecting data. I've been collecting data for about two years now and have nearly 25,000 rows of data. Thats nearly 25,000 soccer games from all leagues all over the world

What makes this data set so detailed is that is contains 1) Statistics on the home and away teams 2) Home win, draw, away win odds and 3) Final result

The fields in the data set are: Columns A to E contains information about the league, home and away teams, date etc Columns F, G and H contain the odds for the home win, draw and away win Columns I to BQ contain the team statistics. Home team stats are prefixed with a "h" similarly, away team stats are prefixed with an "a". Examples include ladder position, games played, goals conceded, away games won etc
Columns BR to CA contain final result information. That is the result, the full time result and if available, the half time score aswell

The dataset ranges from January 2016 to October 2017 and the statistics have been sourced from a few different websites. Odds come from BET365 and the results have been manually entered from http://www.soccerstats.com

The motivations for publishing this data set is twofold: 1) Predictive Model - I am curious to know if a predictive model can be created from this dataset, or are results completely random! 2) Probability - Is it possible to calculate the probability of a home win, draw or away win based on this dataset.

As of 2023, Mexico had more professional soccer players than any other country in the world, with 9,464. Meanwhile, Spain's number of professional soccer players amounted to 8,560. Overall, FIFA estimated that there were 123,694 professional soccer players worldwide.

The dataset contains FIFA Women’s World Cup Stats from 1991 to 2019.

The data was collected from Sports Reference then cleaned for data analysis.

Tabular data includes: - squad - year - players - age - possession - matches_played - starts - min_playing_time - minutes_played_90s - goals - assists - non_penalty_goals - penalty_kicks_made - penalty_kicks_attempted - yellow_cards - red_cards - goals_per_90: Runs allowed - assists_per_90 - goals_plus_assists_per_90 - goals_minus_penalty_kicks_per_90 - goals_plus_assists_minus_penalty_kicks_per_90

This dataset provides comprehensive data on football players from various clubs around the world for the year 2024. It includes key player attributes such as name, age, height, nationality, and club affiliation, as well as positional details. The dataset is perfect for football analytics, performance tracking, and scouting purposes.

Context: The data is intended for football enthusiasts, analysts, and data scientists who are interested in exploring player statistics and trends in modern football. With information from players across different leagues and countries, this dataset can help in identifying patterns of player performance, comparing attributes, and understanding the distribution of talent across clubs.

Whether you’re interested in understanding how age affects player positions or comparing the height of defenders across leagues, this dataset provides the foundation for in-depth football analysis.

Data used for the paper entitled "Home advantage for tournament victory: Empirical evidence from FIFA Confederations and World Cups" Date Submitted: 2022-11-04

As of 2024, the combined value of all Premier League players amounted to over 11.3 billion euros, significantly more than any other league in the world. England's second-tier, the EFL Championship, had a combined player value of over 1.5 billion euros - more than any other top-tier league outside of the Big Five.

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.

The soccer market, one of the largest and most dynamic sectors in the sports industry, encompasses a diverse range of elements including professional leagues, grassroots programs, merchandise sales, broadcasting rights, and digital content. As the world's most popular sport, soccer attracts billions of fans globally

Global Football Market size was valued at USD 4.04 billion in 2022 and is poised to grow from USD 4.19 billion in 2023 to USD 5.65 billion by 2031, growing at a CAGR of 3.79% in the forecast period (2024-2031).

The Soccer Clubs market is a dynamic and growing segment of the global sports industry, driven by the immense popularity of soccer as a universal sport. With millions of fans worldwide, soccer clubs serve not only as teams but also as vibrant communities that foster social engagement and entertainment. As of 2023, t

Financial overview and grant giving statistics of The Global Foundation for Peace Through Soccer

The Soccer World Cup market represents one of the most dynamic and lucrative sectors in global sports, engaging millions of fans and generating substantial revenue from various streams including broadcasting rights, sponsorship deals, merchandising, and tourism. With a historical backdrop that began in 1930, the tou