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...

All data taken from https://fbref.com/

GitHub to my project: https://github.com/emreguvenilir/fifa23-ml-ratingsystem

There is another statistics dataset here on Kaggle where the data is totally incomplete. So I took the time, mainly because of a final school project, to download the raw data from R. I then cleaned the data to the specifics of my project. The data contains only players from the big 5 leagues (prem, la liga, bundesliga, ligue 1, serie a.)

Column Description

squad: The team of a given player

comp: The league of the team, only includes the “big 5”

player: player name

nation: nationality of the player

pos: position of the player

age: age of the player

born: year born

MP: matches played

Minutes_Played: minutes played in the season

Mn_per_MP: minutes per match played

Mins_Per_90: minutes per 90 minutes (length of a soccer match)

Starts: matches started

PPM_Team.Success: avg # of point earned by the team from matches in which the player appeared with a minimum of 30 minutes

OnG_Team.Success: goals scored by team while on pitch

onGA_Team.Success: Goals allowed by team while on pitch plus_per_minus_Team.Success: goals scored minus allowed while on pitch

Goals: goals scored

Assists: assists that led to goal

GoalsAssists: goals + assists

NonPKG: non penalty kick goals

PK: penalty kicks made

PKatt: penalties attempted

CrdY: yellow cards

CrdR: red cards

xG: expected goals based on all shots taken

xAG: expected assisted goals

npxG+xAG: non penalty expected goals + assisted goals

PrgC: progressive carries in the attacking half of the pitch and went at least 10 yards

PrgP: progressive carries in the attacking half of the pitch and went at least 10 yards

Gls_Per90: goals per 90 minutes

Ast_Per90: assists per 90 minutes

G+A_Per90: goals + assists per 90

G_minus_PK_Per: goals excluding penalties per 90

G+A_minus_PK_Per: goals and assists excluding penalties per 90

xG_Per: xG per 90

xAG_Per: xAG per 90

xG+xAG_Per: xG+xAG per 90

Shots: shots taken

Shots_On_Target: shots on goal frame

SoT_percent: sh/SoT * 100

G_per_Sh: goals per shot taken

G_per_SoT: goal per shot on target

Avg_Shot_Dist: avg shot dist

FK_Standard: shots from free kicks

G_minus_xG_expected: goals minus expected goals

np:G_minus_xG_Expected: non penalty goals minus expected goals

Passes_Completed: passes completed

Passes_attempted: passes attempted

Passes_Cmp_percent: pass completion percentage

PrgDist_Total: progressive pass total distance

Passes_Cmp_Short: short passes completed (5 to 15 yds)

Passes_Att_Short: short passes Attempted (5 to 15 yds)

Passes_Cmp_Percent_Short: short passes completed percentage (5 to 15 yds)

Passes_Cmp_Medium: medium passes completed (15 to 30 yds)

Passes_Att_medium: medium passes Attempted (15 to 30 yds)

Passes_Cmp_Percent_Medium: medium passes completed percentage (15 to 30 yds)

Passes_Cmp_long: long passes completed (30+ yds)

Passes_Att_long : long passes Attempted (30+ yds)

Passes_Cmp_Percent_long : long passes completed percentage (30+ yds)

A_minus_xAG_expected: assists minus expected assists

Key_Passes: passes that lead directly to a shot

Final_third: passes that enter the final third of the field

PPA: passes into the penalty area

CrsPA: crosses into penalty area

TB_pass: through ball passes

Crs_Pass: number of crosses

Offside_passes: passes that resulted in an offside

Blocked_passes: passes blocked by an opponent

Shot_Creating_Actions: shot creating actions

SCA_90: shot creating actions per 90

TakeOnTo_Shot: take ons that led to shot

FoulTo_Shot: fouls draw that led to shot

DefAction_Shot: defensive actions that led to a shot (pressing)

GoalCreatingAction: goal creating actions

GCA90: goal creating actions per 90

TakeOn_Goal: take ons that led to a goal

Fld_goal: fouls drawn that led to a goal

DefAction_Goal: defensive actions that led to a goal (pressing)

Tackles: number of tackles made

Tackles_won: tackles won

Def_3rd_Tackles: tackles in the defensive 1/3 of the pitch

Mid_3rd_Tackles: tackles in the middle 1/3 of the pitch

Att_3rd_Tackles: tackles in the attacking 1/3 of the pitch

Tkl_percent_won: % of dribblers tackled

Lost_challenges: lost challenges, unsuccessful attempts to win the ball

Blocks: # of times blocking the ball by standing in path

Sh_blocked: shots blocked

Passes_blocked: number of passes blocked

Interceptions: interceptions

Clearances; clearances

ErrorsLead_ToShot: errors made leading to a shot

Att_Take: attacking take ons attempted

Succ:Take: attacking take ons successful

Succ_percent_take: percentage of attacking take ons successfully

Tkld_Take: times tackled during a take on

Tkld_percent_Take: percentage of times tackled during a take on

TotDist_Carries: total distance carrying the ball in any direction

PrgDist_carries: progressive carry distance total

Miscontrolls: # of times a player...

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

This dataset contains 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.

Download Free Sample
This fantasy sports market insights report comprises information on key vendors and their competitive landscape, segmentations by Type (Fantasy soccer, Fantasy baseball, Fantasy basketball, Fantasy football, and Other sports) and Geography (North America, Europe, APAC, South America, and MEA), key drivers and challenges, and the parent market. This report also discusses vendor strategies that are playing a key role in the business growth.

One of the key vendor strategies is technological innovation, which has been discussed along with other business planning approaches in this report. To gain more insights on vendor strategies request for a sample of the report.

[Updated 10/2022]

FIFA concerned: From FIFA17 to FIFA23

Dataset

The dataset contains +17k unique players and more than 60 columns, general information and all KPIs the famous videogame offers. As the esport scene keeps rising espacially on FIFA, I thought it can be useful for the community (kagglers and/or gamers)

https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F754781%2F9a2ffdb0d55279bd06db95322821ae16%2F5.jpg?generation=1597934332241413&alt=media" alt="">

Context

The data was retrieved thanks to a crawler that I implemented to retrieve: - Aggregated data such as name of the players, age, country - Detailed data such as offensive potential, defense, acceleration I like football a lot and this dataset is for me the opportunity to bring my contribution for the realization of projects that can go from simple analysis to elaboration of strategies on optimal composition under constraints...

Acknowledgements

We wouldn't be here without the help of others. I would like to thanks @karangadiya who I got inspiration from, check his repo here !

FIFA19 dataset: https://www.kaggle.com/karangadiya/fifa19 FIFA18 dataset: https://www.kaggle.com/thec03u5/fifa-18-demo-player-dataset

More details on the crawler

I used beautifulsoup to scrap https://sofifa.com/. First, I scrap the main page to get all general information and then, I scraped each player's webpage that is associated. I defined a batch size so I can parallelize the retrieving of the data. Then I merge all dataframes and cleaned the merged one. I have only 4 CPU and defined 5 batches: - Without batch: 5h12 - With batch: 1h39

If you have any question or suggestion, feel free to comment !

Last update

I added concatenation of all dataframes. !!! Disclaimer !!! Id column is no longer primary key. the primary key would be Id + source together

This dataset contains detailed soccer match data in 2024-2025 season, compiled from ESPN soccer data API.
This dataset contains multiple csv files. The csv files include the following data:
- 30,000+ Match fixtures information, including - Match lineups - Play-by-play information - Key events - Commentary - Team statistics - Player statistics - 400+ unique leagues worldwide - 3,000+ Teams/clubs information - 45,000+ Player information - 1,200+ Teams with team roster

Data is updated daily and covers major soccer leagues world wide  

Files are organized in 5 zip archives by category, plus one archive for base files.

The ultimate Football database for data analysis and machine learning

What you get:

+96,000 matches with detailed minute-by-minute history of the single game + players name (goals, yellow/red cards, penalty, var, penalty missed ect.) - factor INC Season 2021-2022 included

18 European Leagues from 10 Countries with their lead championship: - premier-league - 7600 matches (seasons 2002-2022) - laliga - 7220 matches (seasons 2003-2022) - serie-a - 7150 matches (seasons 2003-2022) - ligue-1 - 6757 matches (seasons 2004-2022) - championship - 6684 matches (seasons 2010-2022) - league-one - 6440 matches (seasons 2010-2022) - bundesliga - 5838 matches (seasons 2003-2022) - league-two - 6015 matches (seasons 2011-2022) - eredivisie - 5776 matches (seasons 2004-2022) - laliga2 - 5519 matches (seasons 2010-2022) - serie-b - 5286 matches (seasons 2010-2022) - ligue-2 - 4470 matches (seasons 2010-2022) - super-lig - 3504 matches (seasons 2010-2022) - jupiler-league - 3756 matches (seasons 2010-2022) - fortuna-1-liga - 3687 matches (seasons 2010-2022) - 2-bundesliga - 3503 matches (seasons 2010-2022) - liga-portugal - 3414 matches (seasons 2010-2022) - pko-bp-ekstraklasa - 3338 matches (seasons 2010-2022)

Betting odds +winning betting odds Statistics Detailed match events (goal types, possession, corner, cross, fouls, cards etc…) for +96,000 matches

Why this data?

You can easily find data about football matches but they are usually scattered across different websites and those data in my opinion are missing with good shaped game's events. Therefore the most usefull part of this DataSet is factor INC which is in fact the register of game events minute-by-minute (goals, cards, vars, missed penalties ect.) collected in python list. Example Swansea-Reading:

"INC": [
      "08' Yellow_Away - Griffin A.",
      "12' Yellow_Away - Khizanishvili Z.",
      "12' Yellow_Home - Borini F.",
      "21' Goal_Home - Penalty Sinclair S.(Penalty )",
      "22' Goal_Home - Sinclair S.(Dobbie S.)",
      "39' Yellow_Away - McAnuff J.",
      "40' Goal_Home - Dobbie S.",
      "46' Red_Card_Away - Tabb J.",
      "49' Own_Away - Allen J.()",
      "54' Yellow_Home - Allen J.",
      "57' Goal_Away - Mills M.(McAnuff J.)",
      "80' Goal_Home - Sinclair S. (Penalty)",
      "82' Yellow_Home - Gower M."
    ],

Those data are scraped form one of the livesscores web page provider. I own program written in python which can scrape data from any league all around the world (but anyway it takes time and the program itself needs constant updating as the providers changing source code).

Locally my Dataset is larger because it contains +100 factors, i.e. it contains infos about previous game with all infos about that games and more additional infos. I shortend the DataSet uploaded on kaggle to make it simpler and more understandable.

License

I must insist that you do not make any commercial use of the data. I give this DataSet to your none-commercial use.

Cooperation

sebastian.gebala@gmail.com

Every player available in FM 20,21,22 and 23

Player positions

Player attributes with statistics as Attacking, Skills, Defense, Mentality, GK Skills, etc.

Player personal data like Nationality, Club, DateOfBirth, Wage, Salary, etc.

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

Related Datasets

• EPL ⚽ 2021-22 ⭐ English Premier League
• ⚽ Cristiano Ronaldo ⭐ All Club Goals 📈📊
• ⚽ Lionel Messi ⭐ All Club Goals 📈📊

Related Notebooks

• CR7 - Extensive EDA & Analytics-Cristiano Ronaldo
• "https://www.kaggle.com/code/azminetoushikwasi/lionel-messi-extended-eda-goals?scriptVersionId=97083243">▶️ Lionel Messi 📊 Extended EDA ⚽ Goals

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

The Kaggle Premier League dataset is a comprehensive collection of data that covers the performance of Premier League football teams from the seasons 2019/2020 to 2022/2023. The dataset contains detailed information about each team's matches, including match scores, dates, venue, and other important statistics. The dataset is an invaluable resource for analysing the performance trends of individual teams and players over the years, identifying patterns in team and player behaviour, and making data-driven decisions based on the insights gained from the data. Whether you are a football fan, analyst, or researcher, this dataset provides an excellent opportunity to gain deep insights into the world's most popular sport.

Context

LaLiga is the trademark behind the Spanish Football Competitions and their data can be viewed on laliga.com but there is no way to download a csv file with the whole information.

To that end, I've built a Python scraper to retrieve and public these data. Data is updated weekly.

Content

So far, here you have the available contents:

• Player Data: You'll find all available player data from LaLiga with a huge amount of columns for these competitions: female first division, male first division and male second division. Each file is identified by SXX-YY at the beginning meaning the season XX-YY.

Acknowledgements

Thanks Python!

Argentina national football team

The Argentina national football team represents Argentina in men's international football and is administered by the Argentine Football Association, the governing body for football in Argentina. Nicknamed La Albiceleste, they are the reigning world champions, having won the most recent World Cup in 2022.

https://a.espncdn.com/photo/2022/1218/r1108260_1296x518_5-2.jpg" alt=""> - Head coach: Lionel Scaloni - Captain: Lionel Messi - Nickname(s): La Albiceleste; ('The White and Sky Blue') - Current: 2 1 (22 December 2022) - Association: Argentine Football Association (AFA) - Arenas/Stadiums: Estadio Mâs Monumental, Estadio Mario Alberto Kempes

Featured Notebook

• 📊 Ext. EDA ⚽ Goals ▶️ Lionel Messi
• CR7 - Extensive EDA & Analytics-Cristiano Ronaldo

Related Datasets

• Brazil | All Football Matches | 1914-2023
• Lionel Messi vs Cristiano Ronaldo | Club Goals
• UCL 2021-22 | Players Data
• EPL 2021-22 | Stats | Matches and Players
• ⚽ Cristiano Ronaldo ⭐ All Club Goals 📈📊
• ⚽ Lionel Messi ⭐ All Club Goals 📈📊

Download

• kaggle API Command !kaggle datasets download -d azminetoushikwasi/argentina-all-football-matches-19142023

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

The Fjelstul World Cup Database is a comprehensive database about the FIFA World Cup created by Joshua C. Fjelstul, Ph.D. that covers all 21 World Cup tournaments (1930-2018). The database includes 27 datasets (approximately 1.1 million data points) that cover all aspects of the World Cup. The data has been extensively cleaned and cross-validated.

Users can use data from the Fjelstul World Cup Database to calculate statistics about teams, players, managers, and referees. Users can also use the data to predict match results. With many units of analysis and opportunities for merging and reshaping data, the database is also an excellent resource for teaching data science skills, especially in R.

Overview of the database

The 27 datasets in the Fjelstul World Cup Database are organized into 5 groups:

1. A first group of datasets (containing 9 datasets) includes information about each of the 9 basic units of observation in the database: tournaments (tournaments), including the host country, the winner, the dates of the tournament, and information about the format of each tournament; the FIFA confederations (confederations); teams (teams); players (players); managers (managers), including their team and home country; referees (referees), including their home country and confederation; stadiums that have hosted World Cup matches (stadiums); matches (matches), including the stage of the tournament, the location of the match (country, city, stadium), the teams involved, and the result; and the individual awards that are handed out to players at each tournament (awards). Each of these units of observation has a unique ID number.

2. A second group of datasets (containing 4 datasets) maps teams, players, managers, and referees to tournaments. There is a dataset about which teams qualified (qualified teams), which indicates how each team performed in the tournament; a dataset about squads (squads), which indicates the name, position, and shirt number of each player; a dataset about manager appointments (manager_appointments), which indicates the team and home country of each manager; and a dataset about referee appointments (referee_appointments), which indicates the home country and confederation of each referee.

3. A third group of datasets (containing 4 datasets) maps teams, players, managers, and referees to individual matches. There are datasets about team appearances (team_appearances), player appearances (player_apperances), manager appearances (manager appearances), and referee appearances (referee appearances). Players who start a game on the bench but who are not substituted in appear in the squads dataset but not the player_appearances dataset.

4. A fourth group of datasets (containing 4 datasets) cover in-match events, including: all goals (goals); all attempted penalty kicks in penalty shootouts and their outcomes (penalty_kicks); all bookings (bookings), including yellow cards and red cards; and all substitutions (substitutions). Each dataset includes the minute of the event and the player(s) and team involved. Each of these 4 types of in-match events has a unique ID number.

5. A fifth group of datasets (containing 6 datasets) cover tournament-level attributes. There a dataset about host countries (host_countries), including the performance of each host country; a dataset about the stages in each tournament (tournament_stages), which records each stage of the tournament, the dates of the stage, and key features of the stage; a dataset about the groups in each group stage (groups), which indicates the name of each group and the number of teams in each group; a dataset about the final standings in each group (group_standings); a dataset about the final standings for each tournament (tournament_standings); and a dataset about all individual player awards handed out at each tournament (award_winners).

Accessing the data

The database is also available via an R package, which is available on GitHub. You can also download the database from GitHub in 4 formats: an .RData version of the database is available in the data/ folder, a .csv version is available in the data-csv/ folder, a .json version is available in the data-json/ folder, and a relational database version (SQLite) is available in the data-sqlite/ folder.

Accessing the codebook

The full codebook for the Fjelstul World Cup Database is available on GitHub. The codebook is available in .pdf format in the codebook/pdf/ folder. It is also available in .csv format in the codebook/csv/ folder. There are 2 files: datasets.csv, which describes the contents of each dataset, and variables.csv, which describes ea...

Other datasets in this series

• FIFA World Cup 2022 Team Data
• FIFA World Cup 2022 Match Data
• FIFA World Cup 2022 Player Data

Context

The FIFA World Cup 2022 is here and it's bigger and better than ever! With 32 teams from around the world competing for the coveted trophy, the excitement and energy of this global event is palpable. This series of dataset captures all the action, from player statistics and team standings, to game scores and match performances.

Featuring some of the greatest players in the world, including Cristiano Ronaldo, Lionel Messi, and Neymar Jr., this series of datasets is a must-have for any die-hard soccer fan or aspiring data scientist. Use it to analyze game patterns, predict outcomes, and uncover insights into the strategies and performances of your favorite teams and players.

With this FIFA World Cup 2022 dataset, the possibilities are endless. Don't miss out on the opportunity to be a part of the action and join the ranks of the world's top data analysts. Grab your dataset today and let the games begin!

Acknowledgements

All data is downloaded from FBref and all credits for data collection and organization go to them.

Data was created using the fbref.com website and soccerdata library.

team_season_stats file contains aggregated season stats for all teams in the English Premier League. team_match_stats contains the match logs for all teams in the English Premier League.

Information about the European Soccer match statistics. Developed based on Euro Soccer database from https://www.kaggle.com/hugomathien/soccer

25.000+ match 41x Player feature 2008-2016, 8 Seasons statistics 10.000+ Player 500.000+ Rows Data 9.000+ Player picture

You can download the player's pictures from here. PID column+.jpg is the filename of the picture. https://1drv.ms/u/s!AoTudRti4cT8i4wsEAx3MLLEPPrbcw?e=mdWZTZ

Many thanks to Hugo Mathien https://www.kaggle.com/hugomathien

This dataset contains comprehensive historical information about the FIFA World Cup, the premier international football (soccer) tournament organized by FIFA (Fédération International de Football Association). The dataset spans multiple decades and covers various aspects of the tournament, including match results, player statistics, team details, and other relevant information related to the tournament.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13571604%2Fce4d4e613ed35074326ddc5537f50381%2FScreenshot%202023-07-30%20195415.png?generation=1690771057695850&alt=media" alt="">

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.