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 2024, Alan Shearer was the Premier League's all-time top scorer, with a career total of 260 goals. The former Newcastle United forward 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.

9 MORE COMPLETE DATASETS FOR SALE ON ETSY (LINKTREE IN BIO)!!!!!

The 2 datasets in this post are composed of 25 different variables, seen below which hold historical data ranging from 1993/94 - 2024/25 (Mid Season). Perfect for understanding the history of the highest level of English football.

Credits to Joseph Buchdahl, X: https://x.com/12Xpert, Web, http://12xpert.co.uk/

1) Date, The date when the match was played

2) Season, The football season in which the match took place (usually spans across two years, e.g., 2023-24)

3) HomeTeam, The team playing at their home stadium

4) AwayTeam, The visiting team

5) FTH Goals, Full Time Home Goals (total goals scored by home team at the end of the match)

6) FTA Goals, Full Time Away Goals (total goals scored by away team at the end of the match)

7) FT Result, Full Time Result (typically shown as H for home win, A for away win, D for draw)

8) HTH Goals, Half Time Home Goals (goals scored by home team at half-time)

9) HTA Goals, Half Time Away Goals (goals scored by away team at half-time)

10) HT Result, Half Time Result (H for home team leading, A for away team leading, D for draw at half-time)

11) Referee, Name of the match official/referee

12) H Shots, Total shots attempted by the home team

13) A Shots, Total shots attempted by the away team

14) H SOT, Home Shots on Target (shots by home team that were on goal)

15) A SOT, Away Shots on Target (shots by away team that were on goal)

16) H Fouls, Number of fouls committed by the home team

17) A Fouls, Number of fouls committed by the away team

18) H Corners, Corner kicks awarded to the home team

19) A Corners, Corner kicks awarded to the away team

20) H Yellow, Yellow cards shown to home team players

21) A Yellow, Yellow cards shown to away team players

22) H Red, Red cards shown to home team players

23) A Red, Red cards shown to away team players

24) Display_Order, A numerical ordering system for displaying the matches (likely used for sorting or presentation purposes)

25) League, The competition or league in which the match was played

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.

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

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

Egyptian Premier League Matches (2015–2025)

Dataset Overview

Contains 10 seasons of match data from 2015-2015 to 2024-2025 (including matches up to 29-April-2025). Data is organized in 3 tables across multiple CSV files (one per season).

Please take a look to combine and transform notebook for more details about enhanced version

Tables

1. Results

2. Goals

3. Referee_Stadium

Data Source

All data is sourced from a leading football statistics website.

Key Notes

• Each season is in a separate CSV file
• Data spans 29-April-2025
• Includes own goals and penalty indicators
• Tracks assist types and providers

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.

As of 2023, the youngest goal scorer in Premier League history James Vaughan, who scored against Crystal Palace while playing for Everton in 2005. At the time, the English forward was 16 years and 270 days old.

Data Guide: Premier League Match Statistics

Welcome to the Premier League Match Statistics dataset! ⚽ This guide will help you understand the structure of the dataset, key variables, and how to make the most of the data for analysis and predictions.

This dataset contains detailed match statistics from the English Premier League, including final scores, player statistics, team performance, goals, yellow cards, red cards, and more. It is ideal for analyzing team performance, predicting match outcomes, and exploring trends in football. This dataset is valuable for football enthusiasts, data analysts, and predictive model developer.

1. Dataset Overview

This dataset provides comprehensive match statistics from the English Premier League, including team performance, player stats, goals, assists, yellow/red cards, and more. It is ideal for football enthusiasts, analysts, and machine learning projects.

2. Data Structure

The dataset consists of multiple columns, each representing different aspects of a match:

Additional columns may provide more in-depth insights.

3. How to Use This Dataset?

Here are some ideas to explore using this dataset:
✅ Analyze team performance trends over different seasons.
✅ Predict match outcomes using machine learning models.
✅ Identify key players based on goals, assists, and ratings.
✅ Explore disciplinary records (yellow/red cards) for fair play analysis.

4. Data Limitations

• This dataset focuses only on the English Premier League.
• Some matches may have missing or incomplete data.
• Real-time updates may not be available immediately after matches.

As of February 2025, the quickest goal ever scored in the Premier League was Shane Long's opener against Watford in April 2019, coming in just 7.69 seconds. The second-fastest goal was scored by Philip Billing, who scored 9.11 seconds into AFC Bournemouth's match against Arsenal in March 2023.

Analysis of ‘English Premier League(2020-21)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rajatrc1705/english-premier-league202021 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset is a collection of basic but crucial stats of the English Premier League 2020-21 season. The dataset has all the players that played in the EPL and their standard stats such as Goals, Assists, xG, xA, Passes Attempted, Pass Accuracy and more! Do upvote if you like it!

Content

Inspiration

There are several directions you can take with this dataset: 1) Find out which team has the most aggressive defenders (or players for that matter) 2) Which team had more players in the top 10 most assists chart 3) Who were the players with most attempted passes 4) Which players had the most accurate passes excluding the goal keeper and the defenders 5) Defenders with most goals!! 6) Which nation had the most aggressive players? the possibilities are endless, create a notebook and explore them!

--- Original source retains full ownership of the source dataset ---

Context

The Premier League, often referred to as the English Premier League or the EPL (legal name: The Football Association Premier League Limited), is the top level of the English football league system. Contested by 20 clubs, it operates on a system of promotion and relegation with the English Football League (EFL). Seasons run from August to May with each team playing 38 matches (playing all 19 other teams both home and away).[1] Most games are played on Saturday and Sunday afternoons. This data included the premier league standings of the last decade.

Content

Acknowledgements

I would like to acknowledge the work of all my fellow contributors to give me as gist of what kind of data can be uploaded on this platform

Inspiration

Which teams have the best attacking or defensive record in the last decade ? How many points does it usually take to qualify for Europe ? Who are the underdogs of the last decade ?

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

Analysis of ‘Premier League Matches 2014-2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sanjeetsinghnaik/premier-league-matches-20142020 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

The Premier League is by far one of the world’s most entertaining leagues. They have some of the best managers, players and fans! But, what makes it truly entertaining is the sheer unpredictability. There are 6 equally amazing teams with a different team lifting the trophy every season. Not only that, the league has also witnessed victories from teams outside of the top 6. So, let us analyze some of these instances.

So far, the implementation of statistics into soccer has been positive. Teams can easily gather information about their opponents and their tactics. This convenience allows managers to create well-thought out game plans that suit their team, maximize opponents' weaknesses, and increase their chances of winning.

A goal is scored when the whole of the ball passes over the goal line, between the goalposts and under the crossbar, provided that no offence has been committed by the team scoring the goal. If the goalkeeper throws the ball directly into the opponents' goal, a goal kick is awarded.

THE TIME OF SEASON/MOTIVATION: While a club battling for a league title is going to be hungry for a win, as is a side that is fighting to stay up, a club that has already won the title or has already been relegated is unlikely to work as hard, and often rest players as well. THE REFEREE: Of course, when referee's send players off it make a massive impact on a match, but even if he is just awarding a yellow card then it can affect the outcome of the game as the player booked is less likely to go in as hard for the rest of the match.

SUBSTITUTES: The whole point of substitutes is for them to be able to come on and impact a match. Subs not only bring on a fresh pair of legs that are less tired than starters and more likely to track back and push forward, but can also play crucial roles in the formation of a team.

MIND GAMES/MANAGERS: Playing mind games has almost become a regular routine for top level managers, and rightly so. Just a simple mind game can do so much to impact a match, a good example coming from Sir Alex Ferguson.

Per his autobiography, when Manchester United were losing late on in a match at a certain point he would tap his watch and make sure to let the opposition know he is signalling this to his players. United's opposition already know that United have a tendency to come back from behind, and upon seeing this gesture they will think that United are going to come back. And because scientific studies prove that living creatures are more likely to accept things that have happened before than not - horses are more likely to lose to a horse they have already lost to in a race even if they are on an even playing field - they often succumb to a loss.

FORM/INJURIES/FIXTURES: A team on better form is more likely to win a match than if they have been on a poor run of form, while a team in the middle of a condensed run of fixtures is less likely to win than a well rested team. These are just some of the things that affect matches - if you have any other just mention them in the comment section below and I'll try to add them in!

--- Original source retains full ownership of the source dataset ---

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.

Context

I wanted to create a unique dataset which had not been made before. I was thinking about it for many days as to which data I should work upon because anything I was thinking was already there so I came up with this FIFA VAR dataset. I just wanted to keep it one hundred. VAR arrived in the Premier League in the season 19-20 and caused much controversy, with a total of 109 goals or incidents directly affected by the video ref.

What will the VAR review? - Goal/no goal - Penalty/no penalty - Direct red card (not second yellow card/caution) - Mistaken identity (when the referee cautions or sends off the wrong player)

What will it not review? - Any yellow card (including second yellow card leading to red) - Any free kick offence outside the box (other than red card offence)

Content

There are in all 2 files: 1. VAR_Incidents_Stats: This file contains the information about the various incidents that led to the inclusion of VAR in a football match. It contains 7 columns namely: - Team- Name of the team for which VAR was applied - Opponent Team - Name of the Opponent team for which VAR was applied - Date- The date when the match between team and opponent team was played - Site- Match was played Home(H) or Away(A) with respect to the Team - Incident- Description of the incident that led to the usage of VAR facility - Time- Time during the match when VAR was used or the incident happened - VAR used - VAR was used FOR or AGAINST with respect to the Team

1. VAR_Team_Stats: This file contains the statistics as to how VAR was used for a Team. It contains 10 columns namely:
2. Team - Name of the team for which VAR was applied
3. Overturns- How many times was the on field referees decision was overturned by the VAR
4. Leading to goals for - VAR Decisions that Lead to goal for the team
5. Disallowed goals for - VAR Decisions that Disallowed goals for the team
6. Leading to goals against - VAR Decisions that Leading to goals against the team
7. Disallowed goals against - VAR Decisions that Disallowed goals against the team
8. Net goal score - Total FOR and AGAINST VAR Decisions that led to a Goal
9. Subjective decisions for - VAR Subjective decisions for the team
10. Subjective decisions against - VAR Subjective decisions against the team
11. Net subjective score - Total FOR and AGAINST VAR Subjective Decisions

Acknowledgements

Data has been scraped from https://www.espn.in/football/english-premier-league/story/3929823/how-var-decisions-have-affected-every-premier-league-club.

Inspiration

• Has VAR been a good edition to the Premier League or Football in General?
• Has VAR been biased to any specific team?
• Is VAR improving with its decisions overtime?
• The name of the player on whom VAR has been used the most(FOR or AGAINST)?
• VAR has been used the most for which specific reason(Read cards, penalties, etc)?
• VAR has been used more on defenders, forwards, goalkeepers..?

Wayne Rooney tops the list of all-time goal-scorers for Manchester United, having scored 253 times in 559 appearances for the club. In second place is legendary player, Sir Bobby Charlton, who scored 249 times in 758 appearances. These appearances put him second in the list of all-time games played for the Red Devils. At the top of this list is Welshman Ryan Giggs, who played 963 times for United between 1991 and 2016.

Premier League giants Manchester United are currently the record title winners in the English top flight, having won the division a total of 20 times since 1888. Most of these wins came after the inception of the Premier League at the start of the 1992/93 season under the leadership of manager Sir Alex Ferguson. Indeed, United’s most recent title came in the 2012/13 season, which was Ferguson’s final season of management before his retirement. Since United's last Premier League title, local rivals Manchester City have lifted the trophy on multiple occasions, most recently in the 2022/23 season.

Cup successes Based on their success on the pitch, Manchester United have cemented themselves as one of the biggest club teams in the world. Alongside their success in the league, they have won the FA Cup 12 times, only two behind record winners, Arsenal. Their titles also reach beyond British shores as they have won the European Cup on three occasions, most recently in 2008. However, the undisputed kings of the Champions League are Real Madrid, who have taken home the trophy on 14 occasions, including four out of five times between 2013 and 2018.

Introduction

Club football data is an collection of data on match, player, club, referees and atomic level event in competitions ranging from english, french, spanish, german, italian league for season 2017/2018.

The project involves the collation of vital club football data, this is different from the general statistics that exist on the internet such as total shots, total cards etc. This data project captures granular football match and event data including stats surrounding the events and the personnels (player, referees, ...) involved.

This data project aims to provide valuable insight on a club, person, match at an atomic-event level. It means analysis can be done on a given day and time, a football match is played in a given club league championship between two teams with eleven starting players with a match referee in the middle.

Data dictionary

match_event

• id - (int) - Unique Id for match event
• club_id - (int) - Foreign key from club table
• match_id - (int) - Foreign key from match table
• player_id - (int) - Foreign key from player table
• referee_id - (int) - Foreign key from referee table
• matchPeriod- (string) - period of play the event tool place
• eventName - (varchar) - The label of the event (pass, foul, duel)
• action - (varchar) - Specfic action of the event (simple pass,yellow card, ground attack )
• modifier (varchar)- Aftermath of the action (missed ball, opportunity, high, gb, lo, pl )
  • description of the abrev modifier label gb - Position: Goal low center gbr - Position: Goal low right gc - Position: Goal center gl - Position: Goal center left glb - Position: Goal low left gr - Position: Goal center right gt - Position: Goal high center gtl - Position: Goal high left gtr - Position: Goal high right obr - Position: Out low right ol - Position: Out center left olb - Position: Out low left or - Position: Out center right ot - Position: Out high center otl -Position: Out high left otr - Position: Out high right pbr - Position: Post low right pl - Position: Post center left plb - Position: Post low left pr - Position: Post center right pt - Position: Post high center ptl - Position: Post high left ptr - Position: Post high right
• eventSec - (float) - length of time the event took place
• x_start - (int) - start postion of the event on the x-axis
• y_start - (int) - start position of the event on the y-axis
• x_end - (int) - end position of the event on the x-axis
• y_end - (int) - end position of the event on the y-axis
• is_success - (boolean) - Was the event successful? True or False

match

• id - (int) - Unique Id for match
• dateutc - (varchar) - Date match was played
• competition - (varchar) - Type of competition
• season - (varchar) - season match was played
• venue - (varchar) - Venue the match was played
• home_club - (varchar) - Club playing at home
• away_club - (varchar) - Club playing away
• winner - (varchar) - winner of the match
• goal_by_home_club - (varchar) - goals scored by home club
• goal_by_away_club - (varchar) - goals scored by away club
• referee_id - (int) - Foreign key from referee table

club

• id - (int) - Unique Id for club
• name - (varchar) - name of club
• OfficeName - (varchar) - full name description of club
• country - (varchar) - name of country

player

• id - (int) - Unique Id for player
• first_name - (varchar) - player first name
• last_name - (varchar) - player last name
• birth_date - (datetime) -player data of birth
• country - (varchar) - player country
• position - (varchar) - player position
• foot - (varchar) - player preferred foot
• height - (float) - player height

referee

• id - (int) - Unique Id for Referee
• first_name - (varchar) - first name of referee
• last_name - (varchar) - last name of referee
• birthDate - (datetime) - Date of birth of referee
• country - (varchar) - Referee home country

Acknowledgements

Much appreciation to Luca Pappalardo Figshare Soccer match data for the data source.

Inspiration

An analytics dashboard can be created using the data on the following

• successful/missed shot on goal from a player
• successful/missed passes from a player
• most common position of shot on goal from a player
• most successful tackles/duels from a player
• successful/missed headers from a player
• expected goal from a player or team using predictive modelling etc..

The list goes on, feel free to generate your own insights