Context

Football is more than just a game — it’s data-rich and decision-driven. From match results to player statistics, the English Premier League (EPL) offers a goldmine of insights for analysts, fans, and data scientists.

This dataset is part of a personal data preprocessing project designed to transform messy raw data into a clean, structured format — enabling meaningful analysis, modeling, or visualization. Whether you're predicting match outcomes, exploring season trends, or learning data science, this dataset gives you a strong starting point.

Content

This dataset was originally sourced from football-data.co.uk, a trusted source for historical football data. The raw data was downloaded in CSV format and carefully cleaned using Python. The resulting dataset is ready for analysis and includes statistics such as:

• Match dates

• Full-time and half-time results

• Goals, corners, shots, fouls

• Yellow and red cards

It’s ideal for building machine learning models, dashboards, or practicing sports analytics.

Notes on Specific Variables

• Season: The football season (e.g., 2020–2021)
• MatchDate: The date when the match was played
• HomeTeam: Name of the home team
• AwayTeam: Name of the away team
• FullTimeHomeGoals: Goals scored by the home team (full time)
• FullTimeAwayGoals: Goals scored by the away team (full time)
• FullTimeResult: Match result (H = Home win, A = Away win, D = Draw)
• HalfTimeHomeGoals: Goals scored by the home team (half time)
• HalfTimeAwayGoals: Goals scored by the away team (half time)
• HalfTimeResult: Half-time result (H = Home win, A = Away win, D = Draw)
• HomeShots: Total shots by the home team
• AwayShots: Total shots by the away team
• HomeShotsOnTarget: Shots on target by the home team
• AwayShotsOnTarget: Shots on target by the away team
• HomeCorners: Number of corners won by the home team
• AwayCorners: Number of corners won by the away team
• HomeFouls: Number of fouls committed by the home team
• AwayFouls: Number of fouls committed by the away team
• HomeYellowCards: Yellow cards received by the home team
• AwayYellowCards: Yellow cards received by the away team
• HomeRedCards: Red cards received by the home team
• AwayRedCards: Red cards received by the away team

License

This dataset is for educational and non-commercial use only. Raw data sourced from football-data.co.uk. Please credit the source if you use or share this dataset.

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.

General Info

This is a collection of over 50,000 ranked EUW games from the game League of Legends, as well as json files containing a way to convert between champion and summoner spell IDs and their names. For each game, there are fields for:

• Game ID
• Creation Time (in Epoch format)
• Game Duration (in seconds)
• Season ID
• Winner (1 = team1, 2 = team2)
• First Baron, dragon, tower, blood, inhibitor and Rift Herald (1 = team1, 2 = team2, 0 = none)
• Champions and summoner spells for each team (Stored as Riot's champion and summoner spell IDs)
• The number of tower, inhibitor, Baron, dragon and Rift Herald kills each team has
• The 5 bans of each team (Again, champion IDs are used)

This dataset was collected using the Riot Games API, which makes it easy to lookup and collect information on a users ranked history and collect their games. However finding a list of usernames is the hard part, in this case I am using a list of usernames scraped from 3rd party LoL sites.

Possible Uses

There is a vast amount of data in just a single LoL game. This dataset takes the most relevant information and makes it available easily for use in things such as attempting to predict the outcome of a LoL game, analysing which in-game events are most likely to lead to victory, understanding how big of an effect bans of a specific champion have, and more.

Dataset Overview

Dataset Contains the Following: * Fixtures and Scores: fixtures.csv * Player possession stats: player_possession_stats.csv * Team possession stats: team_possession_stats.csv * Player Salaries: player_salaries.csv * Team salaries: team_salary.csv * Basic player stats: player_stats.csv * Basic Team stats: team_stats.csv * League Standings: standings.csv

Position Glossary: * GK: Goalkeepers * DF: Defenders * MF: Midfielders * FW: Forwards * FB: Fullbacks * LB: Left Backs * RB: Right Backs * CB: Center Backs * DM: Defensive Midfielders * CM: Central Midfielders * LM: Left Midfielders * RM: Right Midfielders * WM: Wide Midfielders * LW: Left Wingers * RW: Right Wingers * AM: Attacking Midfielders

fixtures.csv

Features: * week: week of match * Day: weekday of match * Date: date of match * Time: time of kickoff * Home: home team * HomeScore: home team score * Away: away team * AwayScore: away team score * Attendance: match attendance *Venue: stadium * Referee: head official

Info: ```

English Premier League matches from 2023/2024 season, will be updated weekly. Data is scraped from https://fbref.com/en/

Unnamed: 0: An index or identifier column.

Date: The date when the match took place.

Time: The kickoff time of the match.

Comp: The competition name, which is the Premier League for the rows displayed.

Round: The matchweek or round of the competition.

Day: The day of the week the match was played.

Venue: Indicates whether the team was playing at home or away.

Result: The outcome of the match from the perspective of the team mentioned at the end (W = Win, D = Draw, L = Loss).

GF (Goals For): The number of goals scored by the team.

GA (Goals Against): The number of goals conceded by the team.

Opponent: The name of the opposing team.

xG: Expected goals for the team.

xGA: Expected goals against the team.

Poss: Possession percentage during the match.

Attendance: The number of spectators present at the venue.

Captain: The name of the team captain.

Formation: The team's formation.

Referee: The name of the match referee.

Match Report: A link or reference to a detailed match report.

Notes: Any additional notes about the match.

Sh (Shots): Total number of shots taken by the team.

SoT (Shots on Target): Number of shots on target.

Dist: Average distance (likely in meters) from which shots were taken.

FK: Number of free kicks taken.

PK (Penalty Kicks): Number of penalty kicks scored.

PKatt (Penalty Kicks Attempted): Number of penalty kicks attempted.

Season: The season year.

Team: The team the data row is about.

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

Data was created using the fbref.com website and soccerdata library.

team_season_stats file contains aggregated season stats for all teams in the English Premier League. team_match_stats contains the match logs for all teams in the English Premier League.

Champions League 2023/2024 Dataset

Overview

This dataset provides detailed statistics for the UEFA Champions League 2023/2024 season, focusing on team performance across various metrics. The data is sourced from FBref, a comprehensive platform for football statistics. This single-table dataset includes metrics such as matches played, wins, losses, goals scored, expected goals (xG), and more for each team participating in the Champions League.

Dataset Content

The dataset is structured as a single CSV file with the following headers:

• Rk: Rank of the team based on the stage of the competition reached.
• Country: The country of the club.
• Squad: The name of the club.
• MP: Matches played.
• W: Matches won.
• D: Matches drawn.
• L: Matches lost.
• GF: Goals for - total goals scored by the team.
• GA: Goals against - total goals conceded by the team.
• GD: Goal difference (GF - GA).
• Pts: Total points accumulated by the team
• xG: Expected goals - a metric that estimates the number of goals a team should have scored based on the quality of their chances.
• xGA: Expected goals against - a metric that estimates the number of goals a team should have conceded based on the quality of chances they allowed.
• xGD: Expected goal difference (xG - xGA).
• xGD/90: Expected goal difference per 90 minutes.
• Last 5: Results of the last 5 matches (e.g., WWDWL for 3 wins, 1 draw, and 1 loss).
• Attendance: Average attendance for home matches.
• Top Team Scorer: The name of the top scorer for the team.
• Goalkeeper: The name of the main goalkeeper for the team.

Data Source

The data has been scraped from FBref, a well-known source for football statistics. FBref provides detailed and historical data for various football competitions worldwide, including the UEFA Champions League.

Acknowledgements

• FBref: For providing the comprehensive data used to compile this dataset.
• Kaggle: For hosting and facilitating data science competitions and datasets.

🏆 Premier League Market Value Dataset (2025)

Welcome to my first real-world football dataset, scraped from Transfermarkt, containing detailed market value data for 499 Premier League players (2025).

📦 What This Dataset Provides

This dataset includes the following attributes for each player:

• 🧍‍♂️ Player Name
• 🧬 Age
• 🏟️ Club
• 🌍 Nationality
• 🧠 Position
• 💰 Current Market Value (in € millions)

Each field was carefully extracted and cleaned from public sources using custom Python scripts (available on GitHub below).

🔭 My Vision for This Dataset

This is just Phase 1. My goal is to:

• 📈 Add player form stats, contract data, and historical market values in future versions
• ⚽ Scrape other leagues (La Liga, Bundesliga, Serie A, etc.)
• 🤖 Build a machine learning model to predict future market values
• 📊 Enable research in sports analytics, scouting, and value forecasting

💡 Potential Use Cases

• Sports business & economics research
• Fantasy football value analysis
• ML model training (value prediction, clustering by position/value)
• Tableau / Power BI dashboards
• Scouting & recruitment simulations
• NLP + data fusion from other sources

📈 Update Frequency

• Dataset will be updated monthly
• Upcoming updates will include:
  • Player performance stats
  • Contract duration
  • Injury/transfer status
  • Form trend over time

Explore match day statistics of every game and every team during the 2021-2022 season of the English Premier League!

Data includes data, teams, referee, and stats by home and away side such as fouls, shots, cards, and more! Also included is a dataset of the weekly rankings for the season.

The 2021–22 Premier League was the 30th season of the Premier League, the top English professional league for association football clubs since its establishment in 1992, and the 123rd season of top-flight English football overall. The start and end dates for the season were released on 25 March 2021, and the fixtures were released on 16 June 2021.

Manchester City successfully defended their title, securing a sixth Premier League title and eighth English league title overall on the final day of the season; it was also the club's fourth title in the last five seasons.

The data was collected from the official website of the Premier League. I then cleaned the data using google sheets

This dataset contains weekly performance statistics for football (soccer) players from the Israeli Premier League (Ligat Ha`Al). It tracks individual player metrics across various match weeks, providing a granular view of player performance throughout the season.

The data covers 15 weeks of match play and includes statistics for 381 unique players.

Content The dataset is structured with the following columns:

Name: Name of the player (in Hebrew).

Week: The match week number (currently covering weeks 1 through 14).

goals: Number of goals scored by the player in that specific week.

assists: Number of assists provided by the player.

minutes: Total minutes played during the match.

threats: Total Shots on target.

dribbles: Number of successful dribbles completed.

tackles: Number of tackles made by the player.

fouls: Number of fouls committed by the player.

Inspiration This dataset is ideal for sports analytics enthusiasts and data scientists looking to explore:

Fantasy League Analysis: Identify high-performing players, "sleeper" picks, and consistent point scorers.

Player Performance Tracking: Visualize how a specific player's form changes over the course of the season.

Position Clustering: Use metrics like tackles vs. dribbles/goals to cluster players into positions (Defenders, Midfielders, Forwards) unsupervised.

Impact Analysis: Determine the correlation between minutes played and offensive output (goals/assists).

Dataset Summary Format: CSV

Columns: 9

Language: Hebrew (Player Names)

Title: 70 Football Leagues Data (2019-2023)

Dataset Description: This dataset provides comprehensive data on 70 football leagues from various countries around the world. The dataset covers the period from 2019 to 2023, offering a rich collection of football-related information for data analysis, research, and visualization purposes.

Content: The dataset contains a wealth of football-related data, including match statistics, team information, player details, and league standings. The dataset covers a diverse range of leagues, encompassing top-tier competitions as well as lower divisions, allowing users to explore football data at various levels.

Key Features:

Match Results Home Goals Away Goals Home Goals in First Half Away Goals in First Half Match Odds for 1X2 and O/U 2.5 Goals Total Goals in the Match

Potential Use Cases: - Statistical Analysis: Analyze match data, team performance, and player statistics to identify trends, patterns, and insights. - Predictive Modeling: Utilize historical data to build predictive models for match outcomes, goal predictions, or player performance. - Visualizations: Create visualizations, graphs, and charts to present key football data in an easily understandable format.

Data Source: The data for this dataset is collected from reliable sources, including official football websites, sports news portals, and reputable football data providers. The dataset is carefully curated and quality-checked to ensure accuracy and reliability.

Updates and Maintenance: The dataset will be periodically updated to include new seasons, leagues, and any necessary data corrections. User feedback and contributions are welcome to improve the dataset and keep it up-to-date.

Disclaimer: While utmost care has been taken to ensure the accuracy and reliability of the data, errors or inconsistencies may still exist. Users are encouraged to verify the data with official sources before making any critical decisions based on the dataset.

Acknowledgments: We would like to acknowledge the contributions of the data providers, football organizations, and sports enthusiasts whose efforts have made this dataset possible. Their dedication to collecting and sharing football data is greatly appreciated.

Note: Please be respectful of the data usage policy and terms of service of the dataset. Use the data responsibly and ensure compliance with any applicable legal requirements.

Dive into the intricate world of League of Legends with "Champions of the Rift," an extensive dataset that compiles detailed in-game statistics for all champions. This dataset includes vital information such as base health, mana, armor, attack damage, ability power, and gold efficiency for each champion, categorized by their primary roles. Whether you're a data analyst, a game developer, or an avid gamer looking to deepen your understanding of champion mechanics, this dataset provides a comprehensive foundation for analysis and strategy development in the ever-evolving battlefield of Summoner's Rift. Explore the strengths and weaknesses of your favorite champions and gain a competitive edge with this meticulously curated collection of champion statistics.

Column Descriptions:

• Champion Name: The name of the champion in League of Legends.

• Role : The primary role or lane typically played by the champion. Common roles include Top, Jungle, Mid, ADC (Attack Damage Carry), and Support.

• Base Health: The initial health points (HP) of the champion at level 1.

• Base Mana: The initial mana points (MP) of the champion at level 1. Some champions do not use mana, in which case this value may be zero.

• Base Armor: The initial armor value of the champion at level 1, which reduces incoming physical damage.

• Base Attack Damage: The initial attack damage (AD) of the champion at level 1, which affects the amount of physical damage dealt by basic attacks.

• Gold Efficiency: A relative measure of how cost-effective the champion's base stats are, expressed as a ratio. A higher value indicates better gold efficiency.

Premier League 2024 Season Team Statistics

Simple Dataset for beginners to Analyze Data

Description

This dataset provides an overview of team performances during the 2024 Premier League season. It contains key metrics such as goals scored, goals conceded, and match outcomes like wins, draws, and losses. This dataset is useful for understanding team rankings and analyzing performance trends across the league.

Columns

• team: The name of the football team.
• goals_scored: The total number of goals scored by the team during the season.
• goals_conceded: The total number of goals conceded by the team during the season.
• wins: The number of matches won by the team.
• draws: The number of matches that ended in a draw.
• losses: The number of matches lost by the team.
• points: The total number of points accumulated by the team (based on wins and draws).
• goal_difference: The difference between goals scored and goals conceded (goals scored minus goals conceded).
• rank: The team's final rank in the league standings.

This dataset is perfect for analyzing team performance, building prediction models, or exploring football statistics throughout the Premier League season.

Context

This dataset contains all the player stats of UEFA Champions League season 2021-22 .

Challenges

• Discover the weak points of any team.
• Suggest players need to be sold, based on performance analysis.
• Nominate Player of the season

About UEFA Champions League

The UEFA Champions League is an annual club football competition organised by the Union of European Football Associations and contested by top-division European clubs, deciding the competition winners through a round robin group stage to qualify for a double-legged knockout format, and a single leg final.

https://m.media-amazon.com/images/M/MV5BNTViYjI5M2MtNDYzZS00MDZkLTkzOWItMzkyM2FmMDhhZjc4XkEyXkFqcGdeQXVyNDg4MjkzNDk@._V1_.jpg" alt="UCL">

Content

• attacking.csv
• attempts.csv
• defending.csv
• disciplinary.csv
• distributon.csv
• goalkeeping.csv
• key_stats.csv

Related Datasets

• EPL ⚽ 2021-22 ⭐ English Premier League
• ⚽ Cristiano Ronaldo ⭐ All Club Goals 📈📊
• ⚽ Lionel Messi ⭐ All Club Goals 📈📊

Related Notebooks

• CR7 - Extensive EDA & Analytics-Cristiano Ronaldo
• "https://www.kaggle.com/code/azminetoushikwasi/lionel-messi-extended-eda-goals?scriptVersionId=97083243">▶️ Lionel Messi 📊 Extended EDA ⚽ Goals

Download

• kaggle API Command !kaggle datasets download -d azminetoushikwasi/ucl-202122-uefa-champions-league

About UEFA Champions League 2021-22

The 2022 UEFA Champions League Final was the final match of the 2021–22 UEFA Champions League, the 67th season of Europe's premier club football tournament organised by UEFA, and the 30th season since it was renamed from the European Champion Clubs' Cup to the UEFA Champions League. It was played at the Stade de France in Saint-Denis, France, on 28 May 2022, between English club Liverpool and Spanish club Real Madrid. It was the third time the two sides have met in the European Cup final, after 1981 and 2018, the third final held here, after the 2000 and 2006 finals, and the first time the same two teams have met in three finals.

This was the first final to be played in front of a full attendance since the 2019 final, as the previous two finals were affected by the COVID-19 pandemic.The final was originally scheduled to be played at the Allianz Arena in Munich, Germany. After the postponement and relocation of the 2020 final, the final hosts were shifted back a year, so the 2022 final was given to the Krestovsky Stadium in Saint Petersburg. Following the Russian invasion of Ukraine on 24 February, UEFA called an extraordinary meeting of the executive committee, where it was expected to officially pull the match out of Russia.[8][9] A day later, it announced the final would move to the Stade de France in Saint-Denis, located just north of Paris.

Real Madrid won the match 1–0 via a 59th-minute goal from Vinícius Júnior for a record-extending 14th title, and their 5th in nine years. As the winners of the 2021–22 UEFA Champions League, Real Madrid earned the right to play against the winners of the 2021–22 UEFA Europa League, Eintracht Frankfurt, in the 2022 UEFA Super Cup. Additionally, the winners typically qualify for the annual FIFA Club World Cup. However, the tournament's status remains uncertain, following FIFA's proposal for a format overhaul.

Disclaimer

• The data collected are all publicly available and it's intended for educational purposes only.

Acknowledgement

• Cover image taken from internet.

Appreciate, Support, Share

This dataset provides data on Premier League teams from the past 10 years for analysis. This dataset also includes teams that were in the top division and got relegated.

About Columns:

Team_id: id of the team, the name can be found in the file team_overview.csv.

Season: its data type is text.

ClubBadge, HomeKit, AwayKit andThirdKit: URL of the team's badge and jerseys from the current season.

An example of the dataset on Power Bi

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19031542%2F5d4b5d95f9eb6ff0d3643ac77254f72f%2Fimage_2024-05-24_194113007.png?generation=1716597675063464&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19031542%2Fd5c22b212b8a490cbe4a5246ba6d3598%2Fimage_2024-05-24_194306460.png?generation=1716597788518681&alt=media" alt="">

Football Leagues Dataset

This dataset provides an extensive collection of match statistics and team performance metrics from various football leagues around the world. It includes detailed information for each league such as:

Team Name: The name of the football team. Season: The football season for which the data is collected. Matches Played: The total number of matches played by the team. Wins: The number of matches won. Draws: The number of matches that ended in a draw. Losses: The number of matches lost. Goals For: The total number of goals scored by the team. Goals Against: The total number of goals conceded by the team. Goal Difference: The difference between goals scored and goals conceded. Points: The total points accumulated by the team based on match results.

The dataset covers a wide range of leagues, including but not limited to: Belgium Belgian Pro League Brazil Brazilian Serie A Colombia Colombian Primera A Croatia Croatian Football League Czech Republic Czech First League Denmark Danish Superliga England Premier League, Championship, League One, League Two France Ligue 1 Germany Bundesliga Greece Greek Super League Israel Ligat Al Italy Serie A Mexico Liga MX Netherlands Eredivisie Portugal Liga Portugal Scotland Scottish Premiership Spain La Liga Sweden Allsvenskan Turkey Super Lig USA MLS, MLB, NBA, NFL, NHL

Features description

• date : the date of the game
• time : the time of the game
• comp : the competition of the game
• round : the round of the game
• day : the day of the week of the game
• venue : the venue of the game
• result : the result of the game
• gf : the goals for the home team
• ga : the goals for the away team
• opponent: the opponent of the home team
• xg : the expected goals for the home team
• xga : the expected goals for the away team
• poss : the possession of the home team
• captain : the captain of the home team
• formation : the formation of the home team
• referee : the referee of the game
• sh : the shots of the home team
• sot : the shots on target of the home team
• dist : the average distance of the shots of the home team
• fk : the free kicks of the home team
• pk : the penalty kicks of the home team
• pka : the penalty kicks attempted of the home team
• season : the season year of the match
• team: the home team

This dataset provides detailed statistics of football players participating in the 2025 UEFA Champions League season. It includes a wide array of performance metrics, offering a comprehensive view of players’ skills, contributions, and performance throughout the tournament.

Player Data - player_name: Full name of the player. - field_position: Player’s field position (e.g., forward, midfielder, defender, goalkeeper). - club: The football club the player represents during the 2025 Champions League season. - nationality: Player’s country of origin. - age: Player’s age as of the 2025 season.

Key Stats Data - matches_appareance: Total number of games played by the player in the tournament. - top_speed(km/h): Maximum sprint speed of the player, measured in km/h. - distance_covered(km): Total distance run by the player, measured in kilometers. - minutes_played: Total minutes the player has participated in.

Attacking Data - assists: Total assists provided by the player. - corners_taken: Number of corners taken by the player. - offsides: Number offsides committed by the player. -dribbles: Number of times the player has successfully dribbled.

Attempts Data - total_attempts: Number of shots made by the player. - attempts_on_target: Number of shots on target throughout the tournament. - attempts_off_target: Number of shots off target throughout the tournament.

Distribution Data - passing_accuracy(%): Percentage of successful passes made by the player. - passes_attempted: Total number of passes attempted by the player. - passes_completed: Total number of passes successfully completed by the player. - crossing_accuracy(%): Percentage of successful crosses made by the player. - crosses_attempted: Total number of crosses attempted by the player. - crosses_completed: Total number of successful crosses completed by the player.

Defending Data - balls_recovered: Total number of balls recovered by the player. - tackles: Total number of tackles attempted by the player. - tackles_won: Total number of tackles successfully won by the player. - tackles_lost: Total number of tackles lost by the player. - clearance_attempted: Total number of clearances attempted by the player.

Goals Data - goals: Total goals scored by the player. - inside_area: Number of goals scored by the player inside the penalty area. - outside_area: Number of goals scored by the player outside the penalty area. - right_foot: Number of goals scored by the player with their right foot. - left_foot: Number of goals scored by the player with their left foot. - head: Number of goals scored by the player with their head. - other: Number of goals scored by the player with other parts of the body. - penalties_scored: number of penalties scored by the player.

Goalkeeping Data - saves: Total number of shots saved by the goalkeeper. - goals_conceded: Total number of goals conceded by the goalkeeper. - saves_on_penalty: Total number of penalty shots saved by the goalkeeper. - clean_sheets: Total number of matches in which the goalkeeper did not concede any goals. - punches_made: Total number of punches made by the goalkeeper to clear the ball.

Team Information: - team_name: Name of the club the player represents. - country: Team's country of origin. - logo: URL link to the team's logo image.

Data Source The dataset is compiled from official UEFA statistics, and data scraping from the Champions League website. It aims to provide an in-depth analysis of player performances and contributions in one of the most prestigious football competitions in the world.

Usage This dataset is perfect for: - Football Analytics: Analyzing player performance trends and team dynamics. - Machine Learning Projects: Building models to predict player performance based on historical data. - Football Strategy: Understanding the top-performing players in key areas like goal-scoring, assists, and defensive actions. - Fan Insights: Engaging with Champions League stats to identify star players and top performers.

Whether you're a football fan, data analyst, or researcher, this dataset provides an invaluable resource for understanding the top players in the 2025 UEFA Champions League season.

This datasets contains soccer matches of the English football league Premier League. Specifically on the last season that started at: 11/08/2023 and ended at: 19/05/2024.