In 2023/24, Erling Haaland won the race for the Premier League Golden Boot, with 27 goals. In the previous season, Haaland broke the record for the most goals scored in a single Premier League season, with 36.

In the English Premier League, the Golden Boot is awarded to the player(s) who scores the most league goals throughout the season. Since the 1888/89 season, the year of the first top flight season in English football, 109 different individuals have been named "top goal scorer" over 127 seasons. In the 2024/25 season, Liverpool's Mohamed Salah won the Golden Boot for the fourth time in eight seasons. Manchester City's Erling Haaland was the top goalscorer in the previous two seasons, including his record-breaking tally of 36 goals in his debut season in 2022/23. Premier League records Current records are generally given in the context of the past three decades, as the total number of games was reduced from 42 to 38 per season in 1995 (in the Premier League's fourth season). In the Premier League era, Thierry Henry and Mohamed Salah have won the Golden Boot more times than anybody else, winning this accolade four times each. Alan Shearer, who won three consecutive Golden Boots in the 90s, is the Premier League's all-time top goal scorer, with 260 goals. Interestingly, Wayne Rooney, who is the Premier League's third-highest goal scorer of all time, never won a Golden Boot. All-time records Outside the Premier League era, Jimmy Greaves has been the top scorer in England more times than any other player, appearing at the top of the list six times between 1958 and 1969, during his career with Chelsea and Tottenham Hotspurs. Derby County's Steve Bloomer finished five seasons as the league's top scorer between 1895 and 1904. The highest ever tally in a single season was sixty goals, which was scored by Everton's Dixie Dean in the 1927/28 season. Greaves, Bloomer, and Dean are also the three top goalscorers of all time in the English league, with 357, 314 and 310 goals respectively. Players from Tottenham have been named top scorer more than players from any other club, appearing 13 times on this list. 20 different nationalities are represented here, and although the vast majority of players are English, there were 16 times where the top scorer in the First Division was Scottish. A much wider variety of nationalities has been represented in recent years, including in the 2018/19 season where it was shared between three players from different African nations.

As of 2025, Alan Shearer was the Premier League's all-time top scorer, with a career total of 260 goals. The former Newcastle United striker lifted the league title with Blackburn Rovers in 1995. Meanwhile, Harry Kane scored his 200th Premier League goal in early 2023, becoming Tottenham Hotspur's all-time top scorer in his last season at the club.

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

Premier League 2023/24: Match, Player, and Team Performance Insights

Dataset Description

This dataset offers an in-depth analysis of the 2023/24 Premier League season, capturing comprehensive data on team and player performances across all matchdays. With over 50 individual CSV files, this collection includes stats on passing accuracy, goal-scoring, defensive actions, possession metrics, and player ratings. Whether you're looking to analyze top scorers, assess team strengths, or delve into individual player contributions, this dataset provides a rich foundation for football analytics enthusiasts and professionals alike.

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

Contents

The dataset contains the following types of data:

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

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

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

Suggested Analysis

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

1. Team Performance Analysis

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

2. Player Performance Analysis

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

3. Goalkeeping and Defensive Analysis

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

4. League Table Insights (New)

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

5. Advanced Metrics Exploration

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

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

License and Disclaimer

License

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

Disclaimer

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

Terms of Use

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

As of September 2024, Harry Kane was England's all time top goal scorer, with 68 goals in total. Kane is also the all time top scorer for Premier League side Tottenham Hotspur.

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

During the ten seasons of Indian Super League held between 2014 and 2024, Bartholomew Ogbeche was the all time highest scorer with ** goals by playing in ** matches. Sunil Chhetri ranked second with ** goals.

Ballon d'Or 2025 Nominees Dataset

This dataset provides comprehensive performance statistics for the 30 official nominees for the Men's Ballon d'Or 2025, covering the 2024-25 European football season (August 2024 to July 2025). The nominees were announced on August 7, 2025, by France Football and UEFA, with the ceremony held on September 22, 2025, at the Théâtre du Châtelet in Paris. Ousmane Dembélé (Paris Saint-Germain) won the Men's Ballon d'Or for his standout season, including PSG's Champions League triumph.

Why This Dataset?

• Timely & Complete: All nominees' data in one place, including mid-season transfers (e.g., Khvicha Kvaratskhelia from Napoli to PSG).
• Rich Metrics: Standard stats (goals, assists) + advanced analytics (xG, progressive carries) for in-depth analysis.
• Use Cases:
  • Predict future winners using ML (e.g., regression on xG + assists).
  • Visualize league dominance (e.g., La Liga's Yamal vs. Premier League's Salah).
  • Compare positions (e.g., GK Donnarumma's unique row vs. FW Haaland).
  • Track young talents like 17-year-old Lamine Yamal (Kopa Trophy winner).

Data sourced from FBref.com, the go-to for football stats. Nominees list verified against official UEFA/France Football announcement.

Nominees Overview

• Total Rows: 32 (30 unique players; duplicates for transfers like McTominay and Kvaratskhelia).
• Leagues Covered: Premier League (England), La Liga (Spain), Bundesliga (Germany), Serie A (Italy), Ligue 1 (France), Primeira Liga (Portugal).
• Key Highlights:
  • Top Scorer: Viktor Gyökeres (39 goals, Sporting CP).
  • Most Assists: Mohamed Salah (18, Liverpool).
  • Highest xG+xAG/90: Dembélé (1.29, winner's edge).
  • Full Nominees: Achraf Hakimi, Alexis Mac Allister, Cole Palmer, Declan Rice, Denzel Dumfries, Désiré Doué, Erling Haaland, Florian Wirtz, Gianluigi Donnarumma, Harry Kane, João Neves, Jude Bellingham, Khvicha Kvaratskhelia, Kylian Mbappé, Lamine Yamal, Lautaro Martínez, Michael Olise, Mohamed Salah, Nuno Mendes, Ousmane Dembélé, Pedri, Raphinha, Robert Lewandowski, Scott McTominay, Serhou Guirassy, Virgil van Dijk, Vitinha. (Missing from data: Fabián Ruiz, Vinícius Júnior—add via FBref if needed.)

Columns

Column	Description	Example
Rk	Player's rank in league stats table	228
Player	Full name	Achraf Hakimi
Nation	Country code + Full name	ma MAR
Pos	Position(s)	DF
Squad	Club name	Paris S-G
Age	Age during season	25
Born	Birth year	1998
MP	Matches Played	25
Starts	Matches Started	24
Min	Total Minutes	2066
90s	Full 90-minute equivalents	23.0
Gls	Total Goals	4
Ast	Assists	6
G+A	Goals + Assists	10
G-PK	Non-Penalty Goals	4
PK	Penalties Scored	0
PKatt	Penalties Attempted	0
CrdY	Yellow Cards	4
CrdR	Red Cards	0
xG	Expected Goals	2.9
npxG	Non-Penalty Expected Goals	2.9
xAG	Expected Assists	6.2
npxG+xAG	Non-Penalty xG + xAG	9.1
PrgC	Progressive Carries	93
PrgP	Progressive Passes	183
PrgR	Progressive Passes Received	273
GlsPer90	Goals per 90 Minutes	0.17
AstPer90	Assists per 90 Minutes	0.26
G+APer90	(G+A) per 90 Minutes	0.44
G-PKPer90	Non-PK Goals per 90	0.17
G+A-PKPer90	Non-PK (G+A) per 90	0.44
xGPer90	xG per 90	0.13
xAGPer90	xAG per 90	0.27
xG+xAGPer90	(xG + xAG) per 90	0.40
npxGPer90	npxG per 90	0.13
npxG+xAGPer90	npxG + xAG per 90	0.40
Matches	Link to match logs (FBref)	Matches
League	Competition	Ligue 1

Data Sources & Methodology

• Source: FBref.com (StatsBomb-powered data).
• Scraping: Python with BeautifulSoup, cloudscraper, and pandas (handles Cloudflare protection).
• Scope: Domestic league stats only (excludes internationals like EURO 2025 or Club World Cup). Season: 2024-25.
• Updates: Last refreshed September 26, 2025. Add international data for fuller analysis.
• Limitations: Goalkeepers (e.g., Donnarumma) have limited offensive stats. Some names use FBref formatting (e.g., "ma MAR" for Morocco).

Usage Ideas

• EDA: Plot goals vs. xG to spot over/underperformers.
• ML: Build a model to rank nominees (features: xG+xAGPer90, PrgC).
• Visuals: Heatmap of per-90 metrics by league.

Fork this dataset, create notebooks, and share your insights! Questions? Comment below.

Keywords: Ballon d'Or, football stats, soccer analytics, 2025 nominees, xG, player performance.

License: CC0 (Public Domain)—use freely!

Citations: UEFA.com , Olympics.com , Wikipedia .

As of September 18, 2024, Harry Kane had scored more goals in the UEFA Champions League than any other English player, with a total of 33. Meanwhile, Wayne Rooney scored 30 Champions League goals during his career.

Nepal Premier League (NPL) Dataset

This dataset contains detailed information about the Nepal Premier League, Nepal's premier cricket league featuring top domestic and international players. It is designed to support a wide range of cricket data analysis tasks, from exploratory analysis to predictive modeling. Features of the Dataset:

Match Details: Information about matches, including teams, dates, venues, and results.
Player Statistics: Individual player performance metrics such as runs scored, wickets taken, strike rates, and economy rates.
Team Performance: Insights into team strategies and win/loss records.

Potential Use Cases:

Performance Analysis: Analyze player and team performance trends.
Match Prediction: Use machine learning to predict match outcomes.
Strategy Insights: Gain insights into team or player strategies for better decision-making.
Visualization: Create dashboards or charts to visualize key metrics such as top scorers, partnerships, or winning streaks.
Fan Engagement: Build applications or platforms to engage cricket fans with interesting stats and trends.

Why Use This Dataset?

Perfect for beginners and experts in sports analytics.
A real-world dataset for applying machine learning and data visualization techniques.
Promotes insights into the rising popularity of cricket in Nepal.

File Information:

File Name: Nepal_Premier_League(1).csv
  Contents: This file contains detailed information about all matches in the Nepal Premier League, including team details, match outcomes, and key player performances.

Column Descriptions:

Team 1: Name of the first team playing the match.
Team 2: Name of the second team playing the match.
Toss: Team that won the toss and their decision (e.g., bat or field).
Series: The series or tournament name (e.g., Nepal Premier League 2024).
Season: The year or season of the match.
Player Of The Match: Name of the player awarded for outstanding performance in the match.
Match Days: Number of days the match was played.
Umpires: Names of the umpires officiating the match.
TV Umpire: Name of the umpire overseeing video replays.
Reserve Umpire: Name of the backup or reserve umpire.
Match Referee: Name of the official match referee.
Points: Points awarded to the teams based on the match outcome.
Match Info: Additional match details such as format (e.g., T20, ODI).
Target: The target score set for the chasing team (if applicable).
Winner_Team: Name of the team that won the match.
Won_By_Margin: Margin of victory (e.g., runs or wickets).

Feel free to download, explore, and share your analysis with the Kaggle community!

Complete overview of Super Cup. Current standings, top scorers, recent matches, and league statistics.

The provided dataset contains detailed league performance statistics for the nominees of the 2024 Ballon d'Or, covering the major European football leagues. These statistics are related to the 2023-2024 season, providing a comprehensive analysis of the top nominees and their performances. The dataset includes key metrics such as goals, assists, expected goals (xG), expected assists (xAG), progression metrics, and many others, allowing a deep understanding of the impact and contribution of the players to their respective teams.

The analysis of the 2024 Ballon d'Or nominees starts with Jude Bellingham, the English midfielder from Real Madrid. He had an impressive season, contributing significantly with goals and assists, in addition to being crucial in midfield control and playmaking. His xG and xAG metrics highlight his ability both to finish and to create clear opportunities for his teammates.

Hakan Çalhanoğlu, from Inter Milan, is also one of the nominees and stood out for his role as a playmaker for the team. Known for his excellent passing accuracy and set-piece skills, Çalhanoğlu presented high numbers in assists and play progression, indicating his direct influence on creating attacking situations.

Among the defenders, Dani Carvajal (Real Madrid) and Rúben Dias (Manchester City) had remarkable impacts during the season. Carvajal stood out for his offensive contributions from the flanks, while Rúben Dias was solid in defense, with metrics highlighting his interventions, clearances, and aerial duels won, which helped Manchester City maintain a cohesive defense.

One of the most surprising names among the nominees is Artem Dovbyk, who had a season that began at Dnipro, passed through Girona, and ended at Roma. He showed versatility and adaptability by scoring important goals in different leagues, which secured him a place among the best.

Phil Foden, from Manchester City, had a brilliant season, with dribbles and assists that helped his team dominate the Premier League. Foden stood out not only in traditional statistics like goals and assists but also in chance creation metrics, demonstrating his vital role in generating dangerous attacks.

Erling Haaland, the Norwegian goal scorer from Manchester City, is once again nominated after a devastating season. His goal numbers are impressive, and the xG confirms his ability to convert opportunities into real goals. Moreover, he also contributed to the team's progression, showing that his role goes beyond simply finishing plays.

Among veteran defenders, Mats Hummels, from Borussia Dortmund, showed that he is still capable of competing at a high level, using his experience to lead the defense. Meanwhile, Harry Kane, who transferred to Bayern Munich, demonstrated that his goal-scoring ability was not limited to the Premier League, adapting immediately to the Bundesliga and contributing not only with goals but also with assists, showcasing his role as a complete forward.

Toni Kroos, from Real Madrid, had a solid season, playing a key role as a playmaker. His statistics for completed passes and expected assists reflect his importance in creating and maintaining his team's possession. Ademola Lookman, from Atalanta, also had a productive season, being one of the main forwards for the Italian team and showing great dribbling and finishing skills.

Among the goalkeepers, Emiliano Martínez, from Aston Villa, had a memorable season. His saves at critical moments earned several points for his team, and his expected goals prevented statistics show his importance in keeping Aston Villa competitive.

Lautaro Martínez, from Inter, had an excellent season, scoring important goals both in Serie A and in European competitions. His attacking partnership was essential for Inter's success, while Kylian Mbappé, who started the season at Paris Saint-Germain before moving to Real Madrid, continued to show why he is considered one of the best in the world, leading in goals and chances created.

Martin Ødegaard, from Arsenal, and Declan Rice, also from Arsenal, were key in the Gunners' midfield. Ødegaard stood out with his creativity and vision, while Rice had a solid defensive performance, being crucial in protecting the defense.

Rodri, from Manchester City, is another expected presence among the nominees, as he had a fantastic season as City's main defensive midfielder, helping both in building plays and recovering the ball. Antonio Rüdiger, from Real Madrid, maintained a strong season as a defender, while Vinícius Júnior showed his electrifying skill on the flanks, being one of the most difficult players to mark.

Federico Valverde, another Real Madrid player, is known for his versatility and intensity. He played an essential role in the midfield for the merengues, contributing both defensively and offensively. Vitinha, from Paris Saint-Germain, and Nico Williams, from Athletic C...

During the 2024 season, Liam Marshall of the Wigan Warriors was the Super League's leading try scorer, with ** tries to his name. Marshall was not just successful on an individual level, but also helped the Warriors secure a seventh Grand Final victory.

EA Perry was the leading run scorer in the Women's Premier League (WPL) as of *********, scoring *** runs during the league. MM Lanning, a Delhi Capitals player, followed with *** runs during the WPL.

As of June 2024, the leading all-time try scorer in Great Britain's highest tier of rugby league - the Super League - was Ryan Hall. Still active in the league at the time of writing, Hall broke the record in 2024 while playing for Hull KR. However, Hall is most known for his time at Leeds Rhinos, where he spent over a decade playing on the wing.

Abgebildet wird ein Ranking der erfolgreichsten Torschützen in der englischen Premier League in der laufenden Saison 2025/2026. Erling Haaland von Manchester City führt mit neun Treffern die Torschützenliste an.

Virat Kohli was the leading all-time run scorer in the Indian Premier League (IPL) as of June 2025, scoring over ************ runs during the league. Rohit Sharma ranked second in the same time period.

Der Spieler Dereck Kutesa vom Servette FC hat nach dem letzten Spieltag der Saison 2024/2025 der Fußball-Super League in der Schweiz 15 Tore geschossen. Damit war er der zweitbeste Torschütze der Liga. Die Super League ist seit 2003/2004 die höchste Spielklasse im Schweizer Profifußball. Mit dem Start der Saison 2021/2022 wurde die Liga offiziell nach ihrem Sponsorennamen Credit Suisse Super League genannt.