European Football Leagues Database 2023-2024

Overview This dataset provides comprehensive information about the top 5 European football leagues for the 2023-2024 season. It includes detailed statistics about matches, players, teams, coaches, referees, and more, making it an invaluable resource for sports analysts, researchers, and football enthusiasts.

Dataset Description Leagues Covered: - English Premier League - Spanish La Liga - German Bundesliga - Italian Serie A - French Ligue 1

Database Schema

The database follows a normalized schema design with proper relationships between tables. Here's a simplified view of the main relationships:

leagues
 ↑
teams → matches ← referees
 ↓     ↑
players   scores
 ↑
coaches

Usage Examples

SQL Queries

Here are some example SQL queries to get you started:

1. Get all matches for a specific team: sql SELECT m.*, t1.name as home_team, t2.name as away_team FROM matches m JOIN teams t1 ON m.home_team_id = t1.team_id JOIN teams t2 ON m.away_team_id = t2.team_id WHERE t1.team_id = [team_id] OR t2.team_id = [team_id];

2. Get current league standings: sql SELECT t.name, s.* FROM standings s JOIN teams t ON s.team_id = t.team_id WHERE s.league_id = [league_id] ORDER BY s.points DESC;

3. Get top scorers: sql SELECT p.name, p.team_id, COUNT(*) as goals FROM scores s JOIN players p ON s.scorer_id = p.player_id GROUP BY p.player_id, p.name, p.team_id ORDER BY goals DESC;

Python Example

import pandas as pd
import sqlite3

# Connect to the SQLite database
conn = sqlite3.connect('sports_league.sqlite')

# Read data into pandas DataFrames
matches_df = pd.read_sql('SELECT * FROM matches', conn)
players_df = pd.read_sql('SELECT * FROM players', conn)
teams_df = pd.read_sql('SELECT * FROM teams', conn)

# Analyze data
team_stats = matches_df.groupby('home_team_id')['home_team_goals'].agg(['mean', 'sum'])

Applications

This dataset can be used for: 1. Match outcome prediction 2. Player performance analysis 3. Team strategy analysis 4. Historical trend analysis 5. Sports betting research 6. Fantasy football insights 7. Statistical modeling 8. Machine learning projects

Data Files:

1. matches.csv

   • Match ID, Date, Home Team, Away Team
   • Final Score, Half-time Score
   • Stadium, Referee
   • League and Season information

2. players.csv

   • Player ID, Name, Position
   • Date of Birth, Nationality
   • Team affiliation
   • Personal details

3. teams.csv

   • Team ID, Name, Founded Year
   • Stadium information
   • League affiliation
   • Coach information
   • Team crest URL

4. coaches.csv

   • Coach ID, Name
   • Team affiliation
   • Nationality

5. referees.csv

   • Referee ID, Name
   • Nationality
   • Matches officiated

6. stadiums.csv

   • Stadium ID, Name
   • Location
   • Capacity

7. standings.csv

   • Current league positions
   • Points, Wins, Draws, Losses
   • Goals For/Against
   • Form and Performance metrics

8. scores.csv

   • Detailed match scores
   • Goal statistics
   • Match events

9. seasons.csv

   • Season information
   • League details
   • Year

10. sports_league.sqlite

    • Complete database in SQLite format
    • All tables and relationships included
    • Ready for immediate use

Data Quality

• Data is sourced from football-data.org API
• Regular weekly updates
• Consistent format across all leagues
• Complete historical record for the 2023-2024 season
• Verified and cleaned data

License

This dataset is released under the Creative Commons Zero v1.0 Universal license

Updates and Maintenance

• Dataset is updated weekly
• Last update: March 20, 2024
• Check the version history for detailed changes

Contributing

If you find any issues or have suggestions for improvements, please: 1. Open an issue on the dataset's GitHub repository 2. Submit a pull request with your proposed changes 3. Contact the maintainer directly

Acknowledgments

• Data provided by football-data.org
• Community contributions and feedback
• Open-source tools and libraries used in data collection and processing

Github

Project: https://github.com/kaimg/Sports-League-Management-System

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ff0d45220cad473000b1e59942548dd45%2Fanimated_bubble_chart.gif?generation=1705615116968842&alt=media" alt="">This comprehensive football dataset, derived primarily from Transfermarkt, serves as a valuable resource for football enthusiasts, offering structured information on competitions, clubs, and players. With over 60,000 games across major global competitions, the dataset delves into the performance metrics of 400+ clubs and detailed statistics for more than 30,000 players.

Structured in CSV files, each with unique IDs, users can seamlessly join datasets to perform in-depth analyses. The dataset encompasses market values, historical valuations, and detailed player statistics, including physical attributes, contract statuses, and individual performances. A specialized Python-based web scraper ensures consistent updates, with data meticulously processed through Python scripts and SQL databases.

To use the dataset effectively, users are encouraged to understand the relevant files, join datasets using unique IDs, and leverage compatible software tools like Python's pandas or R's ggplot2 for analysis. The guide emphasizes the potential for fantasy football predictions, tracking player value over time, assessing market value versus performance, and exploring the impact of cards on match outcomes.

Research ideas include player performance analysis for fantasy football or recruitment purposes, studying market value trends for economic insights, evaluating club performance for strategic decision-making, developing predictive models for match outcomes, and conducting social network analysis to understand interactions among clubs and players.

Acknowledging the dataset's unknown license, users are encouraged to credit the original authors, particularly David Cereijo, if used in research. The dataset's dedication to accessibility is evident through active discussions on GitHub for improvements and bug fixes.

In conclusion, this football dataset offers a wealth of information, empowering users to explore diverse analyses and research ideas, bridging the gap between structured data and the dynamic world of football.

📂 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)

Fantasy-Premier-League

A FPL dataset that gets all the basic stats for each player and season history of each player.

For playing Fantasy Premier League: https://fantasy.premierleague.com/

Source: https://github.com/vaastav/Fantasy-Premier-League/tree/master

About Fantasy Premier League(FPL) is the official global fantasy football game of English Premier League, played nearly by 8 million managers all across the globe. Every fantasy game is luck-based mostly with patches of skills and planning. It depends how much we can push back the luck factor by statistics, eye-tests etc.

Content Feature Description

Team - the club that the player belongs to Points - points scored by the player till date Cost - value of the player Position - position played by the player GoalsScored - number of goals scored by the player Assists - number of assists made by the player Saves - number of saves made by the player YellowCard - number of yellow cards received by the player RedCard - number of red cards received by the player MinPlayed - number of minutes played by the player TSB - percentage of teams possessing the player CS - number of clean sheets kept by the player ShotsOnTarget - number of shots on target by the player Goals_Conceded - number of goals conceded by the player BPS - total sum of bonus points scored by the players

🏆 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

Content

The file consists of per gameweek data of players in EPL. The data has been consolidated by combined the player's FPL and understat statistics. In addition to these, the player's team's form along with the opponent data has been merged.

Acknowledgements

Sincere thanks to: * https://github.com/vaastav/Fantasy-Premier-League for providing historical FPL data of each player * https://github.com/amosbastian/understat for providing a Python package for Understat

📊 Global Top Football Leagues – Player Performance Dataset 🏟️ Description: This dataset brings together detailed player statistics from 10 of the world’s top football leagues for the 2024–25 season. Each sheet in the Excel file represents a different league, and a combined sheet (AllCompDataset) merges them into a unified dataset for easy analysis.

The data is ideal for:

Sports analytics & scouting, Machine learning model training, Fantasy football insights, Comparative player analysis, Football dashboards & EDA projects.

📁 Included Leagues: Premier League (England), Serie A (Italy), La Liga (Spain), Bundesliga (Germany), Ligue 1 (France), Brasileirão (Brazil), Primeira Liga (Portugal), Belgian Pro League, MLS (USA), Argentine Primera División (Argentina).

📌 File Structure: AllCompDataset – Merged data from all leagues 1_EPL to 10_Argentina – League-specific sheets Columns may include: Player Name, Position, Club, Matches, Goals, Assists, Minutes, xG, xA, Cards, and more.

🔖 License: Distributed under CC BY 4.0 – You are free to use this dataset with proper attribution. Note: Data sourced and compiled for research and educational purposes. Original statistics adapted from public football resources.

Context

I love playing fantasy football, but I usually never make it to the playoffs. In an attempt to win my Fantasy this year, I will be gather fantasy football projections from as many websites as possible, and figure out how to combine them in a way that helps me make the best draft picks possible. This dataset currently includes all available 2019 Fantasy stats and 2020 Projections.

Content

The data is divided based on position. Quarterback, Running Back, Wide Receiver, Tight End, Defense, and Kicker are all fantasy positions. For each position, there may be slightly different stats, but for all positions, there are player names (or just team name for defense), and a projected amount of fantasy points for the 2020 season.

Please note: all data was scraped on July 17th, and 2020 projections are subject to change.

Acknowledgements

Thank you, ESPN, for publishing your 2020 NFL Fantasy Projections.

Inspiration

Can we make more accurate fantasy predictions than ESPN?

Which players are underrated going into the 2020 season?

Which players are overrated going into the 2020 season?

Assume a 12 team fantasy league with 14 draft rounds. Each week you can play 1 QB, 2RB, 2WR, 1TE, 1 FLEX (TE, RB or WR), 1 D/ST, 1 Kicker.. which positions should you draft first?

Fifa players dataset

These datasets contain detailed player statistics and attributes for football players as featured in the Sofifa database, which is a widely recognized source of football player data used for simulation and analysis purposes. The dataset includes player-specific information such as personal details, club affiliations, performance metrics, and skill ratings, providing a comprehensive overview of player profiles within the world of football.

Key Features:

• Player Information: Includes unique player identifiers, names (short and full), positions, nationality, date of birth, age, and physical attributes (height, weight).
• Club Information: Club team ID, club name, league information (name and level), club position, and contract details (joining date and contract duration).
• Player Performance and Skills: Ratings and metrics for various skills including pace, shooting, passing, dribbling, defending, and physicality. Additional data for specialized attributes like crossing, heading accuracy, and free kick accuracy.
• Player Traits and Tags: Traits that define a player's playstyle (e.g., "Speed Dribbler", "Clinical Finisher") and tags that categorize player capabilities.
• Mentality and Movement: Data on a player's mental attributes such as aggression, composure, and positioning, as well as movement statistics like acceleration, sprint speed, agility, and reactions.
• Goalkeeping Stats: For players in goalkeeping positions, specific data on goalkeeping attributes like diving, handling, and reflexes.
• International Data: Information about national team participation including national team ID, position, and jersey number.
• Visual Assets: URLs to player face images, club logos, and national flags, allowing for visual representation of players and teams.

Example:

The datasets include iconic players like Lionel Messi, providing comprehensive data such as:

• Overall Rating: 93
• Potential Rating: 95
• Market Value: €100.5M
• Wage: €550K/week
• Club: FC Barcelona (La Liga)
• Nationality: Argentina
• Position: Center Forward (CF)
• Key Attributes:
• Pace: 93
• Dribbling: 96
• Shooting: 89
• Passing: 86
• Physicality: 71

Additionally, player traits like "Clinical Finisher", "Speed Dribbler", and "One Club Player" offer insights into their playing style and impact on the pitch.

Applications:

• Football Simulation: Ideal for use in building football simulation models (e.g., for video games, fantasy football platforms, and performance analytics).
• Player Scouting: Offers an extensive resource for scouting players, analyzing potential transfers, or creating player rankings based on specific skillsets.
• Sports Analytics: Provides valuable data for performing statistical analysis on player performance, team dynamics, and league trends.

Data Columns:

• Personal Info: sofifa_id, player_url, short_name, long_name, age, dob, height_cm, weight_kg, nationality_name
• Club Info: club_name, league_name, club_position, club_jersey_number, club_joined, club_contract_valid_until
• Player Skills: pace, shooting, passing, dribbling, defending, physic
• Movement & Mentality: movement_acceleration, movement_sprint_speed, mentality_composure, mentality_aggression
• Goalkeeping (if applicable): goalkeeping_diving, goalkeeping_handling, goalkeeping_kicking
• Visual Assets: player_face_url, club_logo_url, nation_flag_url

These datasets are a valuable resource for sports data enthusiasts, analysts, and game developers aiming to enrich their football-related projects with rich, player-centric data.

Context

Fantasy basketball is a simple game. You select a team and fill out a roster. Each player has a price and you have a budget constraint that you should consider while building your team. You succeed or fail based on how well your players perform. Fantasy sport websites uses their own pricing algorithm and they mostly don’t tell people what their pricing algorithm looks like. In this case study, you will try to explore fantasy basketball data and the player pricing algorithm used for a fantasy basketball website.

Acknowledgements: Invent Analytics for providing data

This dataset contains information about various Premier League players. It includes player attributes, performance metrics, and points earned in specific gameweeks.

The data will be updated after every gameweek

How or why to use this dataset

• To import the averaged rankings into a fantasy football platform before drafting (I think Yahoo allows this) to have a custom rankings of 15 websites/experts and have an edge over those using the default.
• To compare the 15 websites/experts to see which are the best prognosticators of fantasy football, and therefore which you might want to take advice from throughout the season and/or next season.
• To analyze the data to find trends and insights that could help you "win" your draft or season
• To find potential "sleepers" to either draft or pick-up during the season

How I created the dataset

To create this dataset I scraped 14 websites for their fantasy football expert's overall PPR rankings heading into the 2022-23 season, and also used 2 CSVs of rankings provided by websites. I also scraped ADP (Average Draft Position) rankings from Yahoo and FFToday (which also had an expert's rankings, and I'm unsure what platform their ADP numbers are from, though they're not Yahoo's). Then, I averaged the 15 rankings for an ultimate consensus ranking, and averaged the 2 ADP rankings for a consensus there as well.

I used the players' name as the key to join all the scraped data together (which was often frustrating to clean and align), starting with a CSV that included 500 players and there positions, then I added the other CSV of 300 players, which included the players' teams. Therefore, around 200 players don't have a team associated with them, however they're mostly obscure players that aren't even drafted in most fantasy leagues.

With that foundation, I then scraped the players' name as the key and each website's respective ranking, then joined it onto the table. As I mentioned above, the most difficult part was tweaking the players' names for each website to align with the key (Football player names are so famously abnormal that Key and Peel did a series of skits about it on their comedy central show, it's worth checking out! However, for my purposes, the most frequent problem was sites not adding suffixes like Jr. or 'III'). I had to use anti_join countless times to see what names I needed to alter and where.

I'm working on a full code project breakdown that I'll link here when it's done. Because the websites are frequently updated (even though this is so close to the start of the season) and the rankings may be taken down soon altogether, I'm merely providing the code, though not running it, then when I get to the analysis section I'm using this uploaded dataset.

Why I created the dataset

I took on this project to: - Learn how to scrape in a fairly straightforward context (which isn't to say all the rankings were stored in well-structured, easy-to-scrape tables). - Have the best, overall rankings list imaginable by averaging the industry's top experts, and be able to import the list into Yahoo for a personal, custom draft ranking that's more optimized than the cookie-cutter one everyone else will be using. - Come back to this ranking table at the end of the year to see how well the averaged rankings performed, and even more so, to see how each individual website/expert performed for insight into the best prognosticators in the business.

Sources

To give credit where it's due, here's a list of all the websites I scraped or got CSVs from: The 2 rankings in CSV form came from: - Rotoballer.com - Fantasyalarm.com

The 13 websites I scraped for rankings were: - rotowire.com - ftnfantasy.com - sportingnews.com - fantasyfootballcalculator.com - si.com - nbcsportsedge.com - fantraxhq.com - football.pitcherlist.com (QBlist) - nfl.com - thescore.com - fftoolbox.com - rotoheat.com - fftoday.com (Rank & ADP) - Yahoo.com (ADP)

This is my first time uploaded a dataset, so please provide any and all feedback!

🏆 Ballon d'Or 2025 – Player Statistics Dataset (Game by Game & Total)

📅 Season Period: August 1, 2024 – July 31, 2025

This dataset gives you match-by-match and total season stats for 35 football players who are expected to be nominated for the Ballon d’Or 2025. These players had excellent performances throughout the season and are top candidates for the most important individual football award.

All stats are collected only from the matches that happened during the Ballon d'Or eligibility period, which is from August 1, 2024 to July 31, 2025.

👥 Who are these players?

Some of the world’s best players are included, such as:

• Kylian Mbappé
• Ousmane Dembélé
• Lamine Yamal
• Raphinha
• Erling Haaland
• Jude Bellingham
• Mohamed Salah
• ...and many more

These 34 players are competing closely for the 2025 Ballon d’Or, and this dataset helps you explore their performance in detail.

🧠 How You Can Use This Dataset

• Compare player performance over time
• Predict the 2025 Ballon d’Or winner
• Create visual dashboards and plots
• Build models for football analytics or fantasy points
• Explore how top players perform in different leagues or competitions

Context

I love football and wanted to gather a data-set of a list of football players along with their each game performance from various different sources.

Content

The csv file has the fantasy premier league data of all players who played in 3 seasons and a detailed spreadsheet of each player is provided.

Acknowledgements

Thanks to TURD from tableau for some of the data.

Inspiration

We all wondered if it is possible to predict the future! well with the player data against each team and conditions we get to check if the future prediction is truly possible!

About

Fantasy Premier League(FPL) is a global fantasy football game of English Premier League, played nearly by 7.5m managers all across the globe. An unfortunate outbreak of a pandemic has halted football and all other sporting events. In terms of FPL, it has provided us quite a time to ponder about our teams. And with Premier League set to resume on 17th June, its time to give a final push to our analysis of the season so far.

Content

Feature Description

Team - the club that the player belongs to Points - points scored by the player till date Cost - value of the player Position - position played by the player Goals_Scored - number of goals scored by the player Assists - number of assists made by the player Saves - number of saves made by the player Yellow_Card - number of yellow cards received by the player Red_Card - number of red cards received by the player Min_Played - number of minutes played by the player TSB - percentage of teams possessing the player CS - number of clean sheets kept by the player Shots_On_Target - number of shots on target by the player Goals_Conceded - number of goals conceded by the player BPS - total sum of bonus points scored by the players

What is FPL?

Fantasy Premier League (FPL) is an online fantasy football game based on the English Premier League. In the game, participants select a squad of real-life Premier League players and earn points based on their performances in actual matches.

Here are some facts about FPL:

• FPL has over 9 million registered users worldwide, making it one of the most popular fantasy sports games in the world.
• The budget for each FPL team is £100.0m, with the most expensive player being Mohamed Salah at £13 million for the current season.
• The highest-scoring FPL player of all time is again Mohamed Salah, who scored 303 points in the 2017/18 season.

Content

This dataset contains a collection of tweets with keywords Fantasy Premier League and FPL. The tweets were scraped using the snscrape library. Check out the Tutorial Notebook

The dataset includes the following information for each tweet:

• ID: The unique identifier for the tweet.
• Timestamp: The date and time when the tweet was posted.
• User: The Twitter handle of the user who posted the tweet.
• Text: The content of the tweet.
• Hashtag: The hashtags included in the tweet, if any.
• Retweets: The number of times the tweet has been retweeted as of the time it was scraped.
• Likes: The number of likes the tweet has received as of the time it was scraped.
• Replies: The number of replies to the tweet as of the time it was scraped.
• Source: The source application or device used to post the tweet.
• Location: The location listed on the user's Twitter profile, if any.
• Verified_Account: A Boolean value indicating whether the user's Twitter account has been verified.
• Followers: The number of followers the user has as of the time the tweet was scraped.
• Following: The number of accounts the user is following as of the time the tweet was scraped

The dataset provides a glimpse into the online chatter related to Fantasy Premier League and can be used for various natural language processing and machine learning tasks, such as sentiment analysis, topic modeling, and more. It allows an understanding of the community, the level of interest, and the experience of playing FPL.

Indian Super League

Detailed Team, Match, and Transfer Information

By Ajai Govind G [source]

About this dataset

The Hero Indian Super League dataset contains detailed information about the teams, managers, matches and transfers that have taken place within it. This includes a breakdown of team profiles such as year founded and home ground, records of each team's managers from past to present with their country of origin and date of birth details, a comprehensive summary of individual matches with details such as referee names and stadium location, as well as an overview of all transfers including transfer values.

In addition to providing comprehensive coverage on what has already taken place in the Hero Indian Super League (ISL), this dataset also holds promise for further analysis by hosting additional data points in its tables. These may include records on match attendance numbers or even more detailed information offering insight into players’ career arcs within the ISL itself. By delving deeper into this dataset now we can gain greater clarity into how different clubs have developed over time since its establishment in 2014 up until today

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Research Ideas

• Analyzing the performance of teams in relation to their coaches/managers to find out which personnel help ensure a team's success in the Hero Indian Super League. By looking at win ratios, points earned, goals scored and conceded, etc., it can be determined which managers are best suited for helping a team reach its goals.
• Comparing player transfers across seasons to observe any trends or patterns within the league regarding how many players change clubs each season and in what positions they are transferring too. This analysis could be used to predict which teams may have an advantage or disadvantage when it comes to transfers that year and make predictions as to who may finish higher/lower on the table due to this factor.
• Identifying top performing players within key statistical categories such as number of goals scored, assists provided, saves made by goalkeepers etc., This analysis could then be used by teams scouting for new talent or even used by fantasy football managers looking for potential bargain pickups!

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: Season_Teams.csv

File: Teams_Manager_History.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Ajai Govind G.

This is a basic dataset containing the aggregated fantasy premier league data taken from the official website Fantasy Premier League. The data will be updated daily.

Professional Detailed Description of the FIFA 21 Dataset Overview

This dataset provides a comprehensive collection of 18,979 professional football players featured in FIFA 21. It includes 77 attributes related to player demographics, skills, physical attributes, financial information, and club details. The dataset is structured to support in-depth analysis, including player performance evaluation, market value assessment, and talent scouting. 1. Player Demographics & Basic Information

This section provides general details about each player.

ID: Unique identifier for each player.
Name: Short name of the player.
Long Name: Full official name of the player.
Nationality: Country the player represents.
Age: Player’s age as of FIFA 21.
Preferred Foot: Indicates whether the player prefers to use their left or right foot.
Height & Weight: Physical measurements, given in metric and imperial units.

1. Player Performance & Ratings

The dataset contains individual skill ratings and overall performance metrics.

↓OVA (Overall Rating): General performance score (out of 100).
POT (Potential Rating): Maximum potential rating a player can achieve.
Total Stats & Base Stats: Aggregated numerical ratings of a player’s overall performance.
Best Position: The most suitable playing position based on the player's attributes.

Skill & Technical Attributes:

Dribbling (DRI)
Passing (PAS)
Shooting (SHO)
Defending (DEF)
Physical (PHY)
Skill Moves (SM): Rated from 1 to 5 stars.
Weak Foot (W/F): Effectiveness of the player’s non-dominant foot (rated from 1 to 5).

Attacking & Ball Control Attributes:

Crossing, Finishing, Heading Accuracy, Short Passing, Volleys
Curve, Free Kick Accuracy, Long Passing, Ball Control

Movement Attributes:

Acceleration, Sprint Speed, Agility, Reactions, Balance

Power & Physical Attributes:

Shot Power, Jumping, Stamina, Strength, Long Shots

Mentality Attributes:

Aggression, Interceptions, Positioning, Vision, Penalties, Composure

Defensive Attributes:

Marking, Standing Tackle, Sliding Tackle

Goalkeeping Attributes (for goalkeepers):

GK Diving, GK Handling, GK Kicking, GK Positioning, GK Reflexes

1. Player Position & Playstyle

   Positions: Lists all playable positions (e.g., ST, RW, CM, CB). A/W (Attacking Work Rate): Work rate classification (Low, Medium, High). D/W (Defensive Work Rate): Defensive effort classification (Low, Medium, High).

2. Financial & Contractual Information

This section provides financial insights on each player.

Value: Estimated market value of the player.
Wage: Weekly salary.
Release Clause: The amount required to buy out the player's contract.
Club: The current club the player is associated with.
Contract: Contract expiry year.
Loan Date End: End date for loaned players.

1. Club & Transfer History

   Joined: Date when the player joined their current club. Loan Date End: End date of loan (if applicable).

2. Popularity & Engagement Metrics

   Hits: Number of times the player’s profile has been viewed online.

Use Cases

This dataset is ideal for various analytical applications, such as:

Talent Scouting & Recruitment: Identifying promising players based on potential ratings.
Market Value Analysis: Evaluating player worth based on performance and club details.
Team Optimization: Analyzing squad composition for better strategy planning.
Machine Learning Applications: Predicting player performance trends.
Fantasy Football & Gaming Insights: Enhancing player selections based on data-driven insights.