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!

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.

Analysis of NFL team offensive and defensive Expected Points Added (EPA) per play performance

Comprehensive dataset of 1 Football clubs in Free municipal consortium of Trapani, Italy as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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

This dataset contains data for last 10 seasons of Spanish La Liga including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.u...

Notes for Football Data

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use (in particular odds from specific bookmakers no longer used) and refer to data collected in earlier seasons. For a current list of what bookmakers are included in the dataset please visit http://www.football-data.co.uk/matches.php

Key to results data:

• Div = League Division
• Date = Match Date (dd/mm/yy)
• Time = Time of match kick off
• HomeTeam = Home Team
• AwayTeam = Away Team
• FTHG and HG = Full Time Home Team Goals
• FTAG and AG = Full Time Away Team Goals
• FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win)
• HTHG = Half Time Home Team Goals
• HTAG = Half Time Away Team Goals
• HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available)

• Attendance = Crowd Attendance
• Referee = Match Referee
• HS = Home Team Shots
• AS = Away Team Shots
• HST = Home Team Shots on Target
• AST = Away Team Shots on Target
• HHW = Home Team Hit Woodwork
• AHW = Away Team Hit Woodwork
• HC = Home Team Corners
• AC = Away Team Corners
• HF = Home Team Fouls Committed
• AF = Away Team Fouls Committed
• HFKC = Home Team Free Kicks Conceded
• AFKC = Away Team Free Kicks Conceded
• HO = Home Team Offsides
• AO = Away Team Offsides
• HY = Home Team Yellow Cards
• AY = Away Team Yellow Cards
• HR = Home Team Red Cards
• AR = Away Team Red Cards
• HBP = Home Team Bookings Points (10 = yellow, 25 = red)
• ABP = Away Team Bookings Points (10 = yellow, 25 = red)

Note that Free Kicks Conceeded includes fouls, offsides and any other offense commmitted and will always be equal to or higher than the number of fouls. Fouls make up the vast majority of Free Kicks Conceded. Free Kicks Conceded are shown when specific data on Fouls are not available (France 2nd, Belgium 1st and Greece 1st divisions).

Note also that English and Scottish yellow cards do not include the initial yellow card when a second is shown to a player converting it into a red, but this is included as a yellow (plus red) for European games.

Key to 1X2 (match) betting odds data:

• B365H = Bet365 home win odds
• B365D = Bet365 draw odds
• B365A = Bet365 away win odds
• BSH = Blue Square home win odds
• BSD = Blue Square draw odds
• BSA = Blue Square away win odds
• BWH = Bet&Win home win odds
• BWD = Bet&Win draw odds
• BWA = Bet&Win away win odds
• GBH = Gamebookers home win odds
• GBD = Gamebookers draw odds
• GBA = Gamebookers away win odds
• IWH = Interwetten home win odds
• IWD = Interwetten draw odds
• IWA = Interwetten away win odds
• LBH = Ladbrokes home win odds
• LBD = Ladbrokes draw odds
• LBA = Ladbrokes away win odds
• PSH and PH = Pinnacle home win odds
• PSD and PD = Pinnacle draw odds
• PSA and PA = Pinnacle away win odds
• SOH = Sporting Odds home win odds
• SOD = Sporting Odds draw odds
• SOA = Sporting Odds away win odds
• SBH = Sportingbet home win odds
• SBD = Sportingbet draw odds
• SBA = Sportingbet away win odds
• SJH = Stan James home win odds
• SJD = Stan James draw odds
• SJA = Stan James away win odds
• SYH = Stanleybet home win odds
• SYD = Stanleybet draw odds
• SYA = Stanleybet away win odds
• VCH = VC Bet home win odds
• VCD = VC Bet draw odds
• VCA = VC Bet away win odds
• WHH = William Hill home win odds
• WHD = William Hill draw odds

• WHA = William Hill away win odds

• Bb1X2 = Number of BetBrain bookmakers used to calculate match odds averages and maximums

• BbMxH = Betbrain maximum home win odds

• BbAvH = Betbrain average home win odds

• BbMxD = Betbrain maximum draw odds

• BbAvD = Betbrain average draw win odds

• BbMxA = Betbrain maximum away win odds

• BbAvA = Betbrain average away win odds

• MaxH = Market maximum home win odds

• MaxD = Market maximum draw win odds

• MaxA = Market maximum away win odds

• AvgH = Market average home win odds

• AvgD = Market average draw win odds

• AvgA = Market average away win odds

Key to total goals betting odds:

• BbOU = Number of BetBrain bookmakers used to calculate over/under 2.5 goals (total goals) averages and maximums
• BbMx>2.5 = Betbrain maximum over 2.5 goals
• BbAv>2.5 = Betbrain average over 2.5 goals
• BbMx<2.5 = Betbrain maximum under 2.5 goals

• BbAv<2.5 = Betbrain average under 2.5 goals

• GB>2.5 = Gamebookers over 2.5 goals

• GB<2.5 = Gamebookers under 2.5 goals

• B365>2.5 = Bet365 over 2.5 goals

• B365<2.5 = Bet365 under 2.5 goals

• P>2.5 = Pinnacle over 2.5 goals

• P<2.5 = Pinnacle under 2.5 goals

• Max>2.5 = Market maximum over 2.5 goals

• Max<2.5 = Market maximum under 2.5 goals

• Avg>2.5 = Market average over 2.5 goals

• Avg<2.5 = Market average under 2.5 goals

Key to Asian handicap betting odds:

• BbAH = Number of BetBrain bookmakers used to Asian handicap averages and maximums ...

This dataset contains data for last 10 seasons of Italian Serie A including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.u...

This dataset contains data for last 10 seasons of German Bundesliga including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co...

Data Sources :- The following data sources were used for this model: - Player attributes - FIFA 16-21 data - Injury history - Transfermarkt injury history data. Pulled and scraped from there using worldfootballR R package

Players/seasons in scope :- - Original scope was all players who have played in the British Premier League at any point between 2016/17 season and 2020/21 season - Due to complications and difficulties in joining 3 datasets from entirely different sources, this came out to a total of 685 rows of data, consisting of 317 players

Training Data :- - 3 separate data sources were combined to create a datset which included player attributes (i.e. - pace, height, weight), player injury history and player game time - Data was grouped on a player-year level