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.

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

📂 About This Dataset This dataset combines detailed player performance statistics from WhoScored with team and player meta-data from Transfermarkt. It covers over 1,500 players from top European leagues and includes metrics such as:

Expected Goals (xG) & xG per 90

Tackles, Interceptions, Key Passes, Assists

Pass Accuracy, Crosses, Long Balls

Total Minutes Played & Formations

Player Age, Height, Positioning

🧩 Use Cases Player Rating Prediction

Team Formation Impact Analysis

Identifying Underrated Players via xG vs. Goals

Clustering Players by Style or Efficiency

Fantasy Football Recommendations

🏗️ Data Sources WhoScored.com: Player match stats, tactical analysis.

Transfermarkt.com: Player bio, team formations.

📊 Features Snapshot 32 Columns

Over 20 numerical performance metrics

Cleaned, ready-to-analyze format

Small number of missing values (mostly in passing stats)

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.

Scraped player stats per game from Understat from 2014/2015 to 2024/2025 (still in progress) seasons.

This contains more detailed information than the dataset from https://www.kaggle.com/datasets/codytipton/understat-data, which includes the individual player stats per game for the English Premier League, La Liga, Bundesliga, Serie A, Ligue 1, and the Russian Football Premier League. In particular, it contains each player's xG, xGBuildup, goals, and shots per game. Furthermore, it has the events for each shot in the events table, clubs and their stats per season in the clubs table, and each game with who lost, won, shots, possession, probabilities of who wins, ect..

This is for educational purposes in our data science bootcamp project.

lineup_stats

• match_id: the id for the match they played
• goals: number of goals for this match
• own_goals: number of own goals for this match
• shots: number of shots for this match
• xG: players xG for this match
• **time*: total amount of time this player played in this match
• player_id: player id
• team_id: id for the players team
• position: players position in this match (SUB means they were substituted in)
• player: player's name
• h_a: 'h' if they are in the home team and 'a' if they are in the away team
• yellow_card: number of yellow cards for this match
• red_card: number of red cards for this match
• **roster_in*: (there is roster information in another table that I did not get, will update later)
• roster_out: (same as roster_in)
• key_passes: number of key passes for this match
• assists: number of assists for this match
• xA: expected assists for this match
• xGChain: total xG for every possession the player is involved in this match
• xGBuildup: Total xG for every possession the player is involved in without key passes and shots in this match
• positionOrder: ordering in the lineup

general_game_stats

• id: this game id
• fid: not sure what this is
• h_id: home team id
• a_id: away team id
• date: date of this game
• league_id: id for the league
• season: which season which game was for
• h_goals: number of goals for the home team
• a_goals: number of goals for the away team
• team_h: home team name
• team_a: away team name
• h_xg: home xG
• a_xg: away xG
• h_w: home win probability
• h_d: home draw probability
• h_l: home loss probability
• league: league name
• h_shot: number of shots by the home team
• a_shot: number of shots by the away team
• h_shotOnTarget: number of shots on target by the home team
• a_shotOnTarget: number of shots on target by the away team
• h_deep:home team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• a_deep: away team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• h_ppda: home team passes allowed per defensive action in the opposition half.
• a_ppda:away team passes allowed per defensive action in the opposition half.

game_events

• id: id for event
• minute: minute the event happend
• result: result (blocked shot, saved shot, ect..)
• X: x-coordinate where the player took the shot
• Y: y-coordinate where the player took the shot
• xG: the xG for the shot
• player: player's name
• h_a: h for home team or a for away team
• player_id: player's id
• situation: situation where this shot happend (direct free kicks, set piece, open play, ect..)
• season: the match season
• shotType: what type of shot (left foot, right foot, head, ect..)
• ** match_id**: id for the match
• h_team: home team name
• ** a_team**: away team name
• ** h_goals**: number of home goals at this time
• ** a_goals**: number of away goals at this time
• date: date of the match
• ** player_assisted**: player who assisted
• lastAction: the last action before this shot

clubs

• club_id: id for the club
• ** club**: club name
• ** league_id** : league id
• ** league**: league name
• ** season**: which season these stats are from
• ** wins**: number of wins that season
• ** draws**: number of draws that season
• ** losses**: number of losses that season
• ** pts**: number of points for that season
• ** avg_xG**: average xG throughout the season
• ** total_goals**: total amount of goals for this season
• total_goals_cond: total amount of goals conceded this season

⚽ Explore an extensive dataset featuring detailed player statistics exclusively from the top 7 European football leagues:

EPL (English Premier League)

Bundesliga 🇩🇪

La Liga 🇪🇸

Serie A 🇮🇹

Ligue 1 🇫🇷

Eredivisie 🇳🇱

Primeira Liga 🇵🇹

This dataset provides comprehensive insights into player performances, including attributes like goals, assists, minutes played, and other key metrics. Uncover in-depth player analyses and comparisons across leagues to fuel your football data-driven strategies and player evaluations! 📈🥅⚽

Comprehensive Football Player Statistics: 2023-2024 Season This dataset contains detailed player statistics from top football leagues for the 2023-2024 season. Sourced from FBref, the dataset includes a wide range of metrics covering various aspects of player performance, such as defense, goalkeeping, passing, and shooting.

Key Features Detailed Player Metrics: Statistics for individual players across multiple performance areas. Structured Data: Organized into tables focusing on different aspects of the game for easy analysis. Top Leagues: Includes data from prominent leagues that provide comprehensive detailed stats.

Github Repository link of the project : https://github.com/GuechtouliAnis/Football-Data-Scraping

By: Guechtouli Anis

La Liga 2023/24: Match, Player, and Team Performance Insights

Dataset Description

This dataset provides an in-depth look at the 2023/24 La Liga season, covering various aspects of team and player performances across all matchdays. With over 50 individual CSV files, the dataset includes statistics on passing accuracy, goal-scoring, defensive actions, possession metrics, and player ratings, among others. Whether you're interested in analyzing top scorers, understanding team strengths, or delving into player-specific contributions, this dataset offers a rich foundation for football analytics enthusiasts and professionals.

In addition to the core dataset, we have now added more files related to the league table, expanding the dataset with essential information on match outcomes, league standings, and advanced metrics.

Contents

The dataset contains the following types of data:

• Team Performance Metrics: Information on accurate passes, crosses, goals conceded, interceptions, and other team stats.
• Player Performance Metrics: Individual stats including expected goals (xG), assists, clearances, fouls committed, and tackles won.
• Match-Specific Insights: Detailed metrics on goals scored, scoring attempts, possession percentages, and cards issued per match.
• Match Details (New): Information about rounds, match IDs, teams, scores, and match statuses.
• League Tables (New):
  • Overall standings including matches played, wins, draws, losses, goals scored, goal differences, and points.
  • Separate breakdowns for home and away performances.
  • Advanced metrics including expected goals (xG), expected goals conceded, and expected points.

The file details provide an overview of each dataset, including a brief description of the data structure and potential uses for analysis. This helps users quickly navigate and understand the data available for analysis.

This dataset is ideal for statistical analysis, data visualization, and machine learning applications to uncover patterns in football performance.

Suggested Analysis

This dataset opens up multiple avenues for data analysis and visualization. Here are some ideas:

1. Team Performance Analysis

• Analyze team performance trends, such as comparing passing accuracy, possession, and expected goals (xG) across teams.
• Visualize which teams generate the most scoring opportunities and miss the most big chances.
• Identify the strongest and weakest defenses based on goals conceded, clean sheets, and clearances.

2. Player Performance Analysis

• Identify top-performing players by goals scored, assists, expected goals, and expected assists.
• Explore defensive contributions by analyzing tackles won, interceptions, and clearances per player.
• Assess attacking efficiency by comparing total attempts vs. on-target attempts for each player.

3. Goalkeeping and Defensive Analysis

• Compare goalkeepers on metrics like saves made, goals conceded, and clean sheets to highlight the top performers of the season.
• Evaluate defensive strength by analyzing interception rates and clearances by both teams and players.

4. League Table Insights (New)

• Analyze overall league standings to determine team performance trends.
• Explore home and away performance and identify strengths and weaknesses in different scenarios.
• Utilize advanced metrics to evaluate under- and overperforming teams.

5. Advanced Metrics Exploration

• Examine possession-based metrics, such as possession percentage and possessions won in the attacking third, to identify possession-dominant teams.
• Use expected goals and expected assists data to build profiles highlighting efficient playmaking and finishing among players and teams.

This dataset is a valuable resource for football enthusiasts, data scientists, and analysts interested in uncovering patterns, building predictive models, or generating insights for La Liga 2023/24.

License and Disclaimer

License

This dataset is shared for non-commercial, educational, and personal analysis purposes only. It is not intended for redistribution, commercial use, or integration into other public datasets.

Disclaimer

This dataset was sourced from FotMob, a proprietary provider of football statistics. All rights to the original data belong to FotMob. The dataset is a restructured collection of publicly viewable data and does not claim ownership over FotMob's data. Users should reference FotMob as the original source when using this dataset for research or analysis.

Terms of Use

By using this dataset, you agree to the following: - Non-commercial Use: This dataset is only for educational, analytical, and personal use. It may not be used for commercial purposes or integrated into other public datasets. - Proper Attribution: Please attribu...

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.

Dataset

This dataset was created by Jaseem Mohammed

Contents

It contains the following files:

Context

This is a data dump of the football section of Statbunker's searchable football statistics database. I have uploaded League data for these European leagues:

1. Premier League
2. Bundesliga
3. La Liga
4. French Ligue 1
5. Eredivisie
6. Serie A
7. Scottish Premiership

Content

I have pulled data for the following seasons:

• 2014-15
• 2015-16
• 2016-17
• 2017-18 (Current)

Based on the following disciplines:

• Player stats
• Away attendance
• Home attendance
• League Nationalities
• League Tables
• Team Defense
• Team Offense

Acknowledgements

All data pulled can be found on the Statbunker website: https://www.statbunker.com/

Inspiration

For anyone who enjoys footbal, and analyzing football stats. Please feel free to run kernels!

So, I was trying to predict the rating of players in the FIFA21 game which is going to be released in the coming weeks by using their individual performance in the previous season and the rating in the previous edition of the game FIFA20. But, I couldn't find a dataset for this. So I had no option other than to scrape data from the PL website itself.

Each row in the datasets represents each player's performance in that particular season. It starts with Name, Position, Appearances, and the statistics of his performance throughout the season. Some entries are null because those attributes don't correspond to the position in which the player actually plays, for instance, a Forward will not have Number of saves; it doesn't make sense.

To all those football freaks like me, Feel free to use this dataset

Let me know if there's an error

This dataset is undertaken to create a predictive model for the transfer values of football players. We will utilize data from football players and construct a model to predict transfer fees based on that data. Player data includes basic information such as age, height, playing position, as well as professional statistics like goal scoring, assists (in 2 season 2021-2022 and 2022-2023), injuries, along with total individual and team awards in their career.

We had gathered information on players competing in several top-tier global football leagues:

11 European leagues, including the Premier League and Championship in England, Bundesliga in Germany, La Liga in Spain, Serie A in Italy, Ligue 1 in France, Eredivisie in the Netherlands, Liga NOS in Portugal, Premier Liga in Russia, Super Lig in Turkey, and Bundesliga in Austria.

4 American leagues, including Brasileiro in Brazil, Major League Soccer in the United States, Primera División in Argentina, and Liga MX in Mexico.

1 African league, namely the DStv Premiership in South Africa.

4 Asian leagues, comprising J-League in Japan, Saudi Pro League in Saudi Arabia, K-League 1 in South Korea, and A-League in Australia.

Context

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

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

Content

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

The dataset is organized in 3 files:

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

Past Research

I have used this data to:

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

Inspiration

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

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

And many many more...

Context

I had the need to collect Europe's top 5 leagues' dataset for my own undergraduate project. The idea was to eliminate human bias from the player scouting process.

More Details: https://github.com/Suwadith/Winning-Eleven-Scout-Evaluation-and-Analysis-to-Enhance-Football-Player-Recommendations-ML-Flask

Content

This dataset contains individual player statistics from Europe's top 5 leagues 2009 - 2018. Leagues included: La Liga, Bundesliga, Serie A, Ligue 1, Premier League Types of stats: Offensive, Defensive, Passing, Overall Summary

Acknowledgements

This dataset was compiled from the https://www.whoscored.com website

NFL passing statistics since 2001. Contains record of every player who attempted a pass within the time period. Tracked metrics include passing yards, passing touchdowns, pass attempts, completions, interceptions, and touchdown/interception/completion percentages. More advanced metrics like yards per attempt, adjusted net yards per attempt, and other similar metrics are also included. I used this dataset, accompanied with the NFL Rushing Statistics dataset to predict the NFL MVP winner in 2024.

Dataset

This dataset was created by Suv Sanjit Patnaik

Contents

It contains the following files:

Context

This dataset has an exhaustive list of player statistics for each season from 2013 - 2020

Content

Each row is associated with a player and a season. Eg. You will have 7 rows for Lionel Messi: 1 for each season he played

Each row will have 103 unique stats you can look at (Eg. No of Goals Scored, Passing accuracy in %, Minutes Played etc)

This dataset contains detailed league performance statistics for the nominees of the 2024 Ballon D'Or across major European football leagues. The stats cover the 2023-2024 season, showcasing metrics such as goals, assists, expected goals (xG), expected assists (xAG), progression metrics, and more.

Men's Ballon d'Or 2024 Nominees:

• Jude Bellingham (England, Real Madrid)
• Hakan Çalhanoğlu (Turkey, Inter)
• Dani Carvajal (Spain, Real Madrid)
• Rúben Dias (Portugal, Manchester City)
• Artem Dovbyk (Ukraine, Dnipro / Girona / Roma)
• Phil Foden (England, Manchester City)
• Alejandro Grimaldo (Spain, Bayer Leverkusen)
• Erling Haaland (Norway, Manchester City)
• Mats Hummels (Germany, Borussia Dortmund)
• Harry Kane (England, Bayern Munich)
• Toni Kroos (Germany, Real Madrid)
• Ademola Lookman (Nigeria, Atalanta)
• Emiliano Martínez (Argentina, Aston Villa)
• Lautaro Martínez (Argentina, Inter )
• Kylian Mbappé (France, Paris Saint-Germain / Real Madrid)
• Martin Ødegaard (Norway, Arsenal)
• Dani Olmo (Spain, Leipzig / Barcelona)
• Cole Palmer (England, Manchester City / Chelsea)
• Declan Rice (England, Arsenal)
• Rodri (Spain, Manchester City)
• Antonio Rüdiger (Germany, Real Madrid)
• Bukayo Saka (England, Arsenal)
• William Saliba (France, Arsenal)
• Federico Valverde (Uruguay, Real Madrid)
• Vinícius Júnior (Brazil, Real Madrid)
• Vitinha (Portugal, Paris Saint-Germain)
• Nico Williams (Spain, Athletic Club)
• Florian Wirtz (Germany, Bayer Leverkusen)
• Granit Xhaka (Switzerland, Bayer Leverkusen)
• Lamine Yamal (Spain, Barcelona)

The winner of the Men's Ballon d'Or goes to the best male player voted by a panel of soccer journalists representing the top 100 countries in the FIFA Men's Rankings.

The Ballon d'Or ceremony will be held on Oct. 28, 2024.

For the first time since 2003, though, Cristiano Ronaldo and Lionel Messi were not included among the nominees!

Dataset of Arsenal matches and player statistics from 2017/18 season until 28.02.2023.

Content This dataset includes 214 results of Arsenal FC matches starting from 2017/18 season up to 28.02.2022, 2741 record of every player, who played in these matches with advanced statistics and 218 records of every goalkeeper.

Dataset has three files: matches.csv - every Arsenal FC match between the 2017-18 season and February 28, 2023. players.csv - every player who played for Arsenal between the 2017-18 season and February 28, 2023. goalkeepers.csv - every goalkeeper who played for Arsenal between the 2017-18 season and February 28, 2023.

All columns descriptions you can find in README file.