As of 2025, Henrik Larsson held the record for the most goals scored in the UEFA Europa League, with a total of 40. The Swedish forward played for a number of top clubs during his career, including Celtic, Barcelona, and Manchester United.

As of June 2025, Cristiano Ronaldo held the record for the most goals scored in the UEFA Champions League, with a total of 141. The Portuguese forward played for Manchester United, Real Madrid, and Juventus in the competition. Meanwhile, Argentinian forward Lionel Messi ranked in second place, with 129 goals. What is the Champions League? Established in 1955 as the European Cup, the Champions League is the pinnacle of European club soccer, with teams from across the continent competing for the famous trophy. As of 2025, Real Madrid was the club with the most Champions League titles, having over twice the titles of second-placed AC Milan. Meanwhile, Olympique Lyonnais was the club with the most UEFA Women's Champions League titles. Europe’s Big Five leagues The Champions League features a number of elite clubs, including those from the Big Five European soccer leagues. The Big Five consists of the top-tier leagues of England, France, Italy, Spain, and Germany. Altogether, the market size of the Big Five European soccer leagues amounted to more than 19 billion euros in the 2022/23 season. Individually, the English Premier League generated the most revenue out of the Big Five leagues in 2022/23, at nearly seven billion euros.

Complete overview of UEFA Europa League. Current standings, top scorers, recent matches, and league statistics.

Football Player Database – Top 5 European Leagues (Added Season 2025-2026)

A database of players from the top 5 leagues from the 1992-1993 season (Ligue 1 from 1995-1996), excluding goalkeeper statistics, with added columns for UEFA Champions League (UCL) appearances and individual awards. For seasons up to 2017-2018, with limited/reduced statistics. Source: https://fbref.com/en/

Player Info

• PlayerID – Unique identifier for the player
• Player – Player's full name
• Squad – Team/club the player belongs to
• League – League in which the player competes
• Nation – Player's nationality
• Pos – Playing position (e.g., FW, MF, DF)
• Age – Age during the season
• Born – Year of birth
• Season – Season of the data (e.g., 2022-2023)

Playing Time

• MP – Matches played
• Min – Minutes played
• Mn/MP – Minutes per match (average)
• Min% – Percentage of team minutes played
• Starts – Matches started
• Mn/Start – Minutes per start
• Subs – Appearances as a substitute
• Mn/Sub – Minutes per substitute appearance
• unSub – Unsubstituted appearances (played full match)
• 90s – Minutes played expressed in 90-minute units

Shooting / Scoring

• Sh – Total shots
• Sh/90 – Shots per 90 minutes
• SoT – Shots on target
• SoT% – Percentage of shots on target
• SoT/90 – Shots on target per 90 minutes
• G/Sh – Goals per shot
• G/SoT – Goals per shot on target
• Gls – Goals scored
• Ast – Assists
• G+A – Goals plus assists
• PK – Penalties scored
• PKatt – Penalty attempts
• PKcon – Penalties conceded
• OG – Own goals
• xG – Expected goals
• npxG – Non-penalty expected goals
• npxG/Sh – Non-penalty xG per shot
• G-xG – Goals minus expected goals (over- or underperformance)
• np:G-xG – Non-penalty goals minus non-penalty xG

Passing

• Pass – Total passes attempted
• Cmp – Passes completed
• Cmp% – Pass completion percentage
• PassLive – Completed live-ball passes that lead to a shot attempt
• PassDead – Completed dead-ball passes that lead to a shot attempt
• KP – Key passes
• Att – Passes Attempted
• Crs – Crosses attempted
• CrsPA – Crosses that lead to a shot
• A-xAG – Assists minus expected assists from key passes
• xAG – xAG: Exp. Assisted Goals Expected Assisted Goals xG which follows a pass that assists a shot
• xA – Expected assists
• PPA – Passes Penalty Arena
• Live – Live-ball Passes
• Dead – Set-piece passes leading to shots
• FK – Free kicks attempted
• TB – Through balls
• Sw – Switches Passes that travel more than 40 yards of the width of the pitch
• TI – Throw-ins Taken
• CK – Corners
• In – Inswinging Corner Kicks
• Out – Outswinging Corner Kicks
• Str – Straight Corner Kicks
• Compl – Completed progressive passes
• Mis – Misplaced passes

Defensive Actions

• Tkl – Tackles
• TklW – Tackles won
• Tkl% – Tackle success percentage
• Tkld – Tackles attempted in defensive third
• Tkld% – Tackle success in defensive third
• Tkl+Int – Tackles plus interceptions
• Int – Interceptions
• Blocks – Shots blocked
• Clr – Clearances
• Fls – Fouls committed
• Recov – Ball recoveries

Defensive / Positional Coverage

• Def – Defensive actions in total
• Def 3rd – Defensive actions in defensive third
• Mid 3rd – Defensive actions in middle third
• Att 3rd – Defensive actions in attacking third
• Att Pen – Actions in penalty area
• Off – Passes Offside
• Dis – Dispossessions

Duels / Possession

• Won – Duels won
• Won% – Duels win percentage
• Lost – Duels lost

On/Off Metrics

• +/- – Team goal difference when player is on pitch
• +/-90 – Goal difference per 90 minutes
• On-Off – Impact on team goal difference
• onG – Goals scored by team while player is on pitch
• onGA – Goals conceded while player is on pitch
• onxG – Expected goals while on pitch
• onxGA – Expected goals against while on pitch
• xG+/- – xG difference while player is on pitch
• xG+/-90 – xG difference per 90 minutes

Chance Creation / Progressive Play

• SCA – Shot-creating actions
• SCA90 – Shot-creating actions per 90 minutes
• PrgC – Progressive carries
• PrgDist – Progressive distance carried
• PrgP – Progressive passes
• PrgR – Progressive runs
• Rec – Recoveries
• Carries – Ball carries
• CPA – Carries into penalty area
• Touches – Number of touches
• Dist – Total distance covered with the ball
• TotDist – Total distance covered overall
• PPM – Points per Match

Individual Awards

• Ballon d’or – Ballon d’Or wins
• European Golden Shoe – European Golden Shoe wins
• League Won – Domestic league titles won
• UCL_Won – UEFA Champions League titles won
• The Best FIFA Mens Player – FIFA Best Men’s Pla...

As of 2025, Alfredo Di Stéfano and Ferenc Puskás held the record for the most goals scored in UEFA Champions League (or European Cup) finals, with a total of seven. Meanwhile, Cristiano Ronaldo scored four Champions League final goals.

This dataset contains match-level data from the Top 5 European football leagues:

• Premier League (England)
• La Liga (Spain)
• Serie A (Italy)
• Bundesliga (Germany)
• Ligue 1 (France)

📅 Seasons covered: 2023/24 and 2024/25

Each row represents a single match, including key details such as date, venue, teams, goals scored, result, and goal difference.

📊 Columns Included:

match_date → Date of the match

day → Day of the week

match_hour → Kick-off time (local)

week → Matchday

country → Country of the league

season → Football season (e.g., 2023/24)

league → League name

venue → Home/Away indicator

team → Team name

gf → Goals scored (for the team)

ga → Goals conceded (against)

opponent → Opponent team name

result → Match outcome (Win 1 / Draw 2 / Loss 0 for the team)

goal_diff → Goal difference (gf − ga)

🔧 Possible Uses

1. Team performance analysis across leagues and seasons
2. Predictive modeling (e.g., match outcomes, expected goals, standings simulation)
3. Data visualization of team strengths and weaknesses
4. Comparative studies across Europe’s top leagues

⚡ This dataset is clean, structured, and ready-to-use for:

⚽️ Sports analytics

🤖 Machine learning projects

📊 Visualization dashboards

Disclaimer Data originally sourced from football-data.org. Transformed and structured by me for analysis and machine learning purposes.

This dataset compiles the top scorers from the five major European football leagues (Premier League, La Liga, Bundesliga, Serie A, Ligue 1) and the UEFA Champions League for the 2023–2024 and 2024–2025 seasons.

Each CSV file includes rich player data such as:

⚽ Goals, assists, and penalty goals

📅 Age, date of birth, and nationality

🏟️ Club name, home stadium, official website

📸 Wikipedia player image and national flag

This dataset is perfect for:

Comparing player performance across seasons and leagues

Nationality trends among top scorers

Predictive modeling and visual insights in sports analytics

Complete overview of UEFA Europa Conference League. Current standings, top scorers, recent matches, and league statistics.

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

As of 2025, Gerd Müller was the Bundesliga's all-time top scorer, with a total of 365 goals. The three-time European Cup winner spent most of his career at Bayern Munich, winning the Ballon d'Or in 1970.

About this Dataset

The provided dataset contains a comprehensive set of data about football players from the top 42 European first leagues. The dataset encompasses various statistics and information related to these players, providing valuable insights into their performance, skills, and backgrounds. The data covers a wide range of categories, including player details, club information, performance metrics, awards and achievements, transfer history, youth career, social media presence, and much more.

The dataset includes the following key information for each player:

Player Information: Full name, age, date of birth, place of birth, position, sub-position, nationality, height, and outfitter.

Club Details: Club name, league country, league, market value, contract expiry date, and transfermarkt URL.

Performance Metrics: Appearances, goals, assists, yellow cards, red cards, starting eleven appearances, minutes played, and goal participation.

Player Performance Ratings: Seasonal performance rating and overall performance rating.

Awards and Achievements: Accolades, team achievements, youth trophies, and continental trophies.

Transfer History: Transfer fees, transfer dates, left clubs, and joined clubs.

Social Media Presence: Facebook, Twitter, and Instagram links along with followers, following, likes, and other related metrics.

Domestic and Continental Competitions: Appearances, goals, assists, yellow cards, red cards, minutes played, goal participation, clean sheets, and conceded goals in domestic league competitions, UEFA Champions League, UEFA League, and UEFA Conference League.

Domestic Cup Performances: Appearances, goals, assists, yellow cards, red cards, starting eleven appearances, minutes played, and goal participation in domestic cup competitions.

Player Attributes and Skills: Scoring frequency, accurate passes, successful dribbles, tackles, interceptions, shots on target, ground and aerial duels won, accurate long balls, clearances, dispossessed, possession lost and won, touches, fouls, saves, punches, high claims, crosses not claimed, and much more.

The dataset also provides injury-related information such as missed matches and days injured, allowing analysis of a player's injury history.

This comprehensive dataset serves as a valuable resource for football analysts, clubs, researchers, and enthusiasts to gain in-depth insights into the performance and profiles of football players from the top 42 European first leagues.

Category

Sports

Keywords

soccer,football,sport,transfer

Row Count

15633

Price

$2000.00

نظرة عامة شاملة على UEFA Europa League. الترتيب الحالي، هدافين، المباريات الأخيرة، وإحصائيات الدوري.

Version-info

This data includes statistics up to the final of the 2022/23 season.

Context

The UEFA Champions League (abbreviated as UCL) is an annual club football competition organized by the Union of European Football Associations (UEFA) and contested by top-division European clubs, deciding the competition winners through a group and knockout format. It is one of the most prestigious football tournaments in the world and the most prestigious club competition in European football, played by the national league champions (and, for some nations, one or more runners-up) of their national associations. (*** From wikipidea)

Note: This doesn't have any information about the European cup competition (1950-1992). It starts with the beginning of the Champions league (1992/93) season.

Content

So far this data has the following: 1- Each club's participation record in the competition 2- Each country's clubs participation records in the competition (summary of #1) 3- Top Player Appearances by club (i.e. number of times played for a club in the competition) 4- Top Player Appearances Total games (summary of #3) 5- Top Goal scorer by club (i.e. number of goals scored by a player for a club in the competition) 6- Top Goal scorer Totals (summary of #5) 7- Top Coach Appearances by club (i.e. number of times coached for a club in the competition) 8- Top Coach Appearances Total games (summary of #7) 9- Top Goal Scorer for each season in the competition with # of appearances 10- Number of goals scored per round per group in each season

Acknowledgements

All this data was provided by UEFA.com. All I did was download the PDF and then scrape the data and put it in csv format.

INFO -

This is Meta and Shot Data from all the top 5 European Football Leagues from seasons 2014/15 - 2019/20 1. English Premier League 2. La Liga 3. Bundesliga 4. Serie A 5. Ligue 1

Description -

META :

1. player_id → Understat’s unique player ID which helps identify each player
2. player_name → The player’s name
3. games → Number of games player has appeared in that season
4. time → Number of minutes player has played in that season
5. goals → Goals scored in that season
6. xG → Expected goals accumulated in that season
7. xA → Expected assists accumulated in that season
8. shots → shots taken in that season
9. key_passes → key passes made in that season
10. yellow_cards → yellow cards received in that season
11. red_cards → red cards received in that season
12. position → Position played throughout the season. F → Forward, M→ Midfield, D→ Defender, GK→ Goalkeeper
13. team_name → The team the player played for in that season, if the player switched clubs mid-season, that player will have two entries for that specific season
14. npg → Non Penalty Goals scored in that season
15. npxG → Non Penalty Expected goals accumulated in that season
16. xGChain → Total xG of every possession the player was involved in
17. xGBuildup → Total xG of every possession the player was involved in (without key passes and shots)
18. year → Season in which the preceding data was collected. 2014 is 2014/15 and so on

SHOT :

1. h_team → Home Team during that specific match
2. id → MatchID of that specific match
3. minute → Minute at which the shot was taken (remember that one of the downfalls of understat data is that it doesn’t differentiate between shots taken in the extra time after the first half and the restart after the second half)
4. result → Outcome of that shot. Examples →Goal, MissedShots, SavedShot, MissedShots, Goal, BlockedShot
5. X → The understat pitches classifies the pitch as 100 units by 100 units (both length and width). X represents the length. 0 to 100 is from left to right (goal-line to the other goal line)
6. Y → Represents the width. 0 to 100 is from bottom to top
7. xG → Expected Goal value of that specific shot
8. player → Player who took the shot
9. h_a → Whether the shot was taken at home or away
10. player_id → Unique ID of the player who took the shot
11. situation → Situation from which shot was taken. Examples → OpenPlay, SetPiece, DirectFreekick, FromCorner
12. year → Season in which the shot was taken
13. shotType → Which body part the shot was taken with → RightFoot, Head, LeftFoot or Other
14. match_id → Unique ID of the match in which the shot was taken
15. a_team → Away Team during that specific match
16. h_goals → Goals scored by the home team in that specific match
17. a_goals → Goals scored by the away team in that specific match
18. date → Date of the match
19. player_assisted → Player who assisted the shot if any
20. lastAction → The last action before the shot was taken. Examples → Chipped, Cross, Pass, TakeOn, Rebound

All-time top North Macedonia national football team goal scorers complete list

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

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• ⚽ Lionel Messi ⭐ All Club Goals 📈📊

Related Notebooks

• CR7 - Extensive EDA & Analytics-Cristiano Ronaldo
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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

Description

This dataset contains detailed player performance statistics for the 2023-2024 season from the Big 5 European soccer leagues: Premier League, La Liga, Serie A, Bundesliga, and Ligue 1. The data has been meticulously scraped from FBref.com, a comprehensive source for soccer statistics.

Columns and Metrics:

• Rank: The rank of the player based on performance metrics.
• Player: Name of the player.
• Nation: Nationality of the player.
• Position: Playing position of the player.
• Squad: Club the player belongs to.
• Competition: League the player is competing in.
• Age: Age of the player.
• Year_Born: Year the player was born.
• Playing Time_MP: Matches played.
• Playing Time_Starts: Matches started.
• Playing Time_Min: Minutes played.
• Playing Time_90s: Equivalent of 90-minute matches played.
• Performance_Gls: Goals scored.
• Performance_Ast: Assists.
• Performance_G+A: Goals plus assists.
• Performance_G-PK: Goals excluding penalties.
• Performance_PK: Penalty kicks made.
• Performance_PKatt: Penalty kicks attempted.
• Performance_CrdY: Yellow cards.
• Performance_CrdR: Red cards.
• Expected_xG: Expected goals.
• Expected_npxG: Non-penalty expected goals.
• Expected_xAG: Expected assists.
• Expected_npxG+xAG: Non-penalty expected goals plus expected assists.
• Progression_PrgC: Progressive carries.
• Progression_PrgP: Progressive passes.
• Progression_PrgR: Progressive dribbles.
• Per 90 Minutes_Gls: Goals per 90 minutes.
• Per 90 Minutes_Ast: Assists per 90 minutes.
• Per 90 Minutes_G+A: Goals plus assists per 90 minutes.
• Per 90 Minutes_G-PK: Goals excluding penalties per 90 minutes.
• Per 90 Minutes_G+A-PK: Goals plus assists excluding penalties per 90 minutes.
• Per 90 Minutes_xG: Expected goals per 90 minutes.
• Per 90 Minutes_xAG: Expected assists per 90 minutes.
• Per 90 Minutes_xG+xAG: Expected goals plus expected assists per 90 minutes.
• Per 90 Minutes_npxG: Non-penalty expected goals per 90 minutes.
• Per 90 Minutes_npxG+xAG: Non-penalty expected goals plus expected assists per 90 minutes.

I am passionate about soccer and have created this dataset in the hope that it can be useful for others who share my love for the game. Whether you're conducting analysis, building models, or just exploring player stats, I hope this dataset provides valuable insights and serves as a helpful resource.

All-time top Bulgaria national football team goal scorers complete list

Context

I am a huge football fan and football statistics fascinate me. This data helps you to understand who is really who in the world of football. After discovering the understat.com platform with extended analytics about every single game in top European leagues I started to understand football even more. So I decided to scrape available summary data to take a look at some numbers of all teams of all leagues that not many people go for.

Content

The dataset contains statistical summary data by the end of each season from 2014 for 6 UEFA Leagues:

• La Liga

• EPL

• BundesLiga

• Serie A

• Ligue 1

• RFPL

Standard parameters: position, team, amount of matches played, wins, draws, loses, goals scored, goals missed, points.

Additional metrics:

• xG - expected goals metric, it is a statistical measure of the quality of chances created and conceded. More at understat.com

• xG_diff - difference between actual goals scored and expected goals.

• npxG - expected goals without penalties and own goals.

• xGA - expected goals against.

• xGA_diff - difference between actual goals missed and expected goals against.

• npxGA - expected goals against without penalties and own goals.

• npxGD - difference between "for" and "against" expected goals without penalties and own goals.

• ppda_coef - passes allowed per defensive action in the opposition half (power of pressure)

• oppda_coef - opponent passes allowed per defensive action in the opposition half (power of opponent's pressure)

• deep - passes completed within an estimated 20 yards of goal (crosses excluded)

• deep_allowed - opponent passes completed within an estimated 20 yards of goal (crosses excluded)

• xpts - expected points

• xpts_diff - difference between actual and expected points

Acknowledgements

Huge thanks for the team of understat.com for collecting this data and make it open to the world.

Inspiration

With this dataset we can have more arguments in describing every league and can find answers to questions like:

Which teams create more chances to score a goal?

Which teams use pressure a lot and what results does this give?

Which teams play more defensive/offensive football?

Which teams have luck on their side, which do not?

Is there any particular characteristic of each league?

With this high overview dataset we can play a lot and understand more European football.

Women's Football (European Leagues)

Team and Player Performance Statistics

By [source]

About this dataset

This dataset includes comprehensive female football-related performance data and player statistics from the top 5 European leagues: Serie A in Italy, Liga Femenina in Spain, Women's Super League in England, Bundesliga Frauen in Germany, and Division 1 Feminin in France. Gathered throughout each season of the respective leagues, the dataset tracks teams, players, matches and a range of important performance metrics. The recently released data provides intriguing insight into team success and player form - covering parameters such as goals scored per game (xGHome), clean sheets (CS), number of opponents' passes allowed (Sweeper_#OPA) as well as individual performance stats such as tackles made per goal kick (Crosses_Stp). Analyze this insightful data to gain further insight on how female football is developing across Europe's major leagues!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to analyze and compare the performance of teams and players across the top five European leagues: Serie A in Italy, Liga Femenina in Spain, Women's Super League in England, Bundesliga Frauen in Germany, and Division 1 Feminin in France. The dataset provides records of each individual match that occurred within these leagues during the tracked season(s), as well as a range of performance metrics for both teams and players.

To use this dataset effectively it is important to understand which columns are available, as described above. By exploring different combinations of team-level versus player-level data you will be able to identify correlations between certain performance metrics for teams or players that provide insights about female football success across Europe.

Once you’re ready to start exploring the data there are several approaches you may take from visualizing your data via bar or line graphs with Python Matplotlib or Seaborn packages; correlating team-level versus player-level statistics such as number of wins (W) compared against goalkeeper saves (Saves); or performing more complicated regression analyses on your data that explore how different features like time played (Min) can predict goals scored (Goals_FK). Each approach provides unique insights into trends within female football success.

No matter how you choose to analyze this dataset it is important to note that trendlines may shift from year-to year -- so make sure you use consistent periods when comparing changes between seasons! It is also helpful to break down aggregate results by country when analyzing different trends across Europe so consider running separate analyses for each country instead aggregating them all together at once. Using this stepwise approach we hope that through careful exploration of the female football success will begin ‘uncovering’!

Research Ideas

• Analyzing the effect of player performance metrics on team success and vice versa: Using this dataset, it is possible to analyze how changes in different player performance metrics might affect overall team performance (e.g. goals scored or allowed, clean sheets). With further analysis, correlations can be drawn between teams’ and players’ performances under different match-day conditions such as travel distance or surface type.

• Examining trends in the development of female football: This data set spans multiple seasons, making it possible to evaluate any general trends in aspects such as the average age of the players across countries and how that affects their performances; or identifying any underused opportunities available for young talented footballers in specific countries which could be benefitted from improvisations by these countries' governing bodies;

• Benchmark positions used among teams versus outside experts’ opinions: One clever use for this dataset can be to compare positional performances between expert opinions from scouts with actual field results from teams using those positions within each country's top leagues and analyzing areas where consensus is reached upon versus discrepancies found throughout the analyzed data samples . For example, one may cross-examine national team call up rosters with squad selections for clubs’ top female divisions - finding anomalies not spotted prior by those making roster decisions - thereby potentially deriving more informed decisions with regards to selecting position holders based on tangible facts rather than focusing merely on biased subjective eye tests over which player should officially take ...