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.

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.

📂 Dataset Information

This dataset contains statistics for the top goal scorers in the Premier League. It includes key metrics such as goals, assists, minutes per goal, and shot accuracy — making it a great resource for football (soccer) analysis and data visualization.

• Rows: 41 players
• Columns: 9 stats fields
• Size: ~3 KB

📊 Column Descriptions

🏅 Sample Records

🔍 Insights You Can Explore

• Who are the most efficient scorers (lowest minutes per goal)?
• Which players convert their chances best?
• Comparison of goal vs. assist contributions
• Visualizing goal-scoring trends across players

📌 Notes

• Percent fields (Goal Conversion, Shot Accuracy) are stored as strings and may need conversion for numerical analysis.
• Dataset is perfect for dashboards, leaderboards, and sports analytics projects.

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.

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...

Statistical analysis into the top 50 goal scorers of the past 3 decades since the inception of the Premier League in 1992. The main idea is to explore those goal-scoring frequencies with statistical analysis.

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.

As of January 2025, Leicester City's Jamie Vardy held the record for the longest goal scoring run in Premier League history, scoring in 11 consecutive games in the 2015/16 season. Meanwhile, Ruud van Nistelrooy scored in 10 consecutive games for Manchester United in 2003.

This dataset contains detailed data on all footballers from the 2023/24 premier league season

• Player: The name of the player.
• Nation: The player's nationality.
• Pos: The player's position (e.g., forward, midfielder, defender).
• Age: The player's age.
• MP (Minutes Played): Total minutes played by the player.
• Starts: Number of matches the player started.
• Min (Minutes): Total minutes played by the player (this might be the same as MP).
• 90s (90s Played): The equivalent of 90-minute matches played by the player (e.g., 1.5 = 135 minutes).
• Gls (Goals): Total number of goals scored by the player.
• Ast (Assists): Total number of assists made by the player.
• G+A (Goals + Assists): Total number of goals and assists combined.
• G-PK (Goals - Penalty Kicks): Total number of goals scored excluding penalty kicks.
• PK (Penalty Kicks): Number of penalty goals scored by the player.
• PKatt (Penalty Kicks Attempted): Number of penalty kicks attempted by the player.
• CrdY (Yellow Cards): Number of yellow cards received by the player.
• CrdR (Red Cards): Number of red cards received by the player.
• xG (Expected Goals): The expected number of goals from the player's shots.
• npxG (Non-Penalty Expected Goals): Expected goals excluding penalties.
• xAG (Expected Assists): The expected number of assists from the player's passes.
• npxG+xAG (Non-Penalty xG + xAG): Total of non-penalty expected goals and expected assists.
• PrgC (Progressive Carries): Number of times the player carried the ball forward.
• PrgP (Progressive Passes): Number of passes made by the player that moved the ball forward.
• PrgR (Progressive Runs): Number of times the player made runs forward with the ball.
• Gls (Goals): (Repeated, already defined) Total number of goals scored.
• Ast (Assists): (Repeated, already defined) Total number of assists made.
• G+A (Goals + Assists): (Repeated, already defined) Total number of goals and assists combined.
• G-PK (Goals - Penalty Kicks): (Repeated, already defined) Goals scored excluding penalty kicks.
• G+A-PK (Goals + Assists - Penalty Kicks): Total goals and assists minus penalty goals.
• xG (Expected Goals): (Repeated, already defined) Expected number of goals from the player's shots.
• xAG (Expected Assists): (Repeated, already defined) Expected number of assists from the player's passes.
• xG+xAG (Expected Goals + Expected Assists): Total expected goals and assists.
• npxG (Non-Penalty Expected Goals): (Repeated, already defined) Expected goals excluding penalties.
• npxG+xAG (Non-Penalty xG + Expected Assists): Total of non-penalty expected goals and expected assists.

Explore the epic showdown between Messi and Cristiano: goals, assists, averages, and more. A deep dive into the stats that define their legacy.

Premier League Players Performance Dataset

This dataset provides a comprehensive overview of player performance in the Premier League capturing a wide array of metrics related to gameplay, scoring, passing, and defensive actions. With records detailing individual player statistics across different teams, this dataset is a valuable resource for analysts, data scientists, and fans who are interested in diving into player performance data from one of the world’s top soccer leagues.

Each entry represents a single player's profile, featuring data on expected goals (xG), expected assists (xAG), touches, dribbles, tackles, and more. This dataset is ideal for analyzing various aspects of player contribution, both offensively and defensively, and understanding their impact on team performance.

Dataset Columns

Player: Name of the player Team: Team the player belongs to '#' : Player's jersey number Nation: Nationality of the player Position: Primary playing position on the field Age: Age of the player Minutes: Total minutes played Goals: Number of goals scored Assists: Number of assists Penalty Shoot on Goal: Penalty shots taken on goal Penalty Shoot: Total penalty shots attempted Total Shoot: Total shots attempted Shoot on Target: Shots successfully on target Yellow Cards: Number of yellow cards received Red Cards: Number of red cards received Touches: Total ball touches Dribbles: Total dribbles attempted Tackles: Total tackles made Blocks: Total blocks Expected Goals (xG): Expected goals, calculated based on shooting positions and likelihood of scoring Non-Penalty xG (npxG): Expected goals excluding penalties Expected Assists (xAG): Expected assists, based on actions leading to an expected goal (xG) Shot-Creating Actions: Actions leading to a shot attempt Goal-Creating Actions: Actions leading to a goal Passes Completed: Successful passes completed Passes Attempted: Total passes attempted Pass Completion %: Pass completion rate, expressed as a percentage (some entries have missing values here) Progressive Passes: Passes advancing the ball significantly toward the opponent’s goal Carries: Total ball carries Progressive Carries: Carries advancing the ball significantly toward the opponent’s goal Dribble Attempts: Total dribbles attempted Successful Dribbles: Total successful dribbles Date: Date of record collection or game date

Potential Use Cases

Data Visualization: Explore relationships between various performance metrics to identify patterns.

Player Comparisons: Compare individual players based on goals, assists, xG, xAG, and other metrics.

Team Analysis: Evaluate contributions of players within the same team to gain insights into team dynamics.

Predictive Modeling: Use the dataset to build models for predicting game outcomes, goals, or assists based on player performance metrics.

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

📌 This dataset offers season-by-season statistics for all teams that participated in the English Premier League from 1992–93 through 1995–96. It includes detailed information on each club’s performance across multiple dimensions — including match outcomes, goals scored/conceded, top scorers, attendance, and promotion/relegation status.

The dataset serves as a rich resource for sports analytics, trend analysis, data visualization, and predictive modeling.

Dataset Structure Each row in the dataset represents a team’s seasonal performance and includes the following columns:

Rk: Final league rank

Squad: Club name

MP: Matches played

W, D, L: Wins, draws, and losses

GF, GA, GD: Goals for, against, and goal difference

Pts: Total points

Pts/MP: Points per match

Attendance: Average home attendance

Goalkeeper: Primary goalkeeper for the season

Notes: Info such as European qualification or relegation status

Season: The league season (e.g., 1993–94)

Team_Top_Scorer: Name of the top scorer

Top_scorer_Goals: Number of goals scored by top scorer

Promoted: Whether the team was newly promoted

Years_in_League_after_promotion: Years since promotion (if applicable)

Same_Rank_As_Last_Season: Flag indicating if rank was the same as previous season

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.

⚽ Top Footbal Scorers data from the BBC Sports section on 16/03/2025.

Extracts: - Player Name - Goals Scored - Team Name - Matches - Assists - Shots

Ideal for football enthusiasts and sports analysts and outputs data in CSV/JSON/XLSX format for analysis.

🐍**Python Web Scraping Script is also available**🐍

As of 2024, Teddy Sheringham was the oldest goal scorer in Premier League history, aged 40 years and 268 days when he scored for West Ham against Portsmouth in December 2006. Meanwhile, Dean Windass was aged 39 years and 235 days when he last scored for Hull City in the Premier League.

This is a Dataset of All Stats of All Players (Sorted by Goal Scored) in English Premier league in Season 2021-22.

Apps - Appearance in Games Min - Total Minute Played G - Goals Scored A - Assists xG - Expected goals (or xG) measures the quality of a chance by calculating the likelihood that it will be scored from a particular position on the pitch during a particular phase of play. xA - Expected assists gives us an indication of how many assists a player of team should have had based on their build up and attacking play. Play Expected Assists (xA) xG90 - Expected goals per 90 (xG/90) – Expected goals per 90 minutes played by a specific player.

In the season 2014/15, Frank Lampard scored *** goals. His record-number of goals was reached in the season 2009/10, when he scored ** goals. Find further Premier League statistics regarding the number of goals scored for players like Adama Traoré, Zat Knight, and Emmanuel Adebayor.

Dataset Overview:
This dataset contains comprehensive player statistics for the 2024/25 Premier League season, scraped from FBref. It includes detailed performance metrics for approximately 500 players across all teams, making it a valuable resource for football analytics, machine learning models, and performance analysis.

Data Source:
Data is derived from FBref, a leading football statistics platform, collected on September 02, 2025. Credit to FBref for the original data. Compiled by [Your Name].

Columns Included:
- Player: Player's full name
- Nation: Player's nationality
- Position: Player's primary position (e.g., FW, MF, DF)
- Squad: Team name
- Age: Player's age
- Born: Year of birth
- Matches Played: Number of matches played
- Starts: Number of games started
- Minutes: Total minutes played
- 90s Played: Minutes divided by 90
- Goals: Total goals scored
- Assists: Total assists
- Goal+Assist: Combined goals and assists
- Penalty Goals: Goals from penalties
- Penalties Attempted: Total penalty attempts
- Yellow Cards: Number of yellow cards
- Red Cards: Number of red cards
- Expected Goals (xG): Expected goals based on shot quality
- Non-Penalty xG: xG excluding penalties
- Expected Assists (xAG): Expected assists
- Non-Penalty xG+xAG: Combined non-penalty xG and xAG
- Progressive Carries: Number of progressive carries
- Progressive Passes: Number of progressive passes
- Goals per 90: Goals per 90 minutes
- Assists per 90: Assists per 90 minutes
- Goal+Assist per 90: Combined G+A per 90 minutes
- Non-Penalty Goals per 90: Non-penalty goals per 90
- Non-Penalty G+A per 90: Non-penalty G+A per 90
- xG per 90: Expected goals per 90 minutes
- xAG per 90: Expected assists per 90 minutes
- xG+xAG per 90: Combined xG+xAG per 90
- Non-Penalty xG per 90: Non-penalty xG per 90
- Non-Penalty xG+xAG per 90: Non-penalty xG+xAG per 90

Limitations:
- Data reflects stats up to the scrape date (September 02, 2025) and may not include late-season updates.
- Scraped using automated tools; minor inconsistencies or missing data may exist.
- Subject to FBref's terms of use; please use responsibly and avoid commercial exploitation without permission.

Usage:
Ideal for statistical analysis, predictive modeling, or visualizing player performance. Users are encouraged to cite this dataset and FBref in any derived work. License: CC BY-4.0 (attribution required).

Contact:
For questions or updates, reach out to siddhraj761@gmail.com.

Explore the assists competition (Opta criteria) between Messi and Cristiano. Who leads in creating goal-scoring opportunities?