This dataset contains detailed data on all footballers in the 23/24 top 5 leagues.

• 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 (matches played): Total matches played number
• 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.

Context

This dataset contains 2021-2022 football player stats per 90 minutes. Only players of Premier League, Ligue 1, Bundesliga, Serie A and La Liga are listed.

• 2022-2023 Football Player Stats
• 2021-2022 Football Team Stats
• 2022-2023 Football Team Stats

Content

+2500 rows and 143 columns. Columns' description are listed below.

• Rk : Rank
• Player : Player's name
• Nation : Player's nation
• Pos : Position
• Squad : Squad’s name
• Comp : League that squat occupies
• Age : Player's age
• Born : Year of birth
• MP : Matches played
• Starts : Matches started
• Min : Minutes played
• 90s : Minutes played divided by 90
• Goals : Goals scored or allowed
• Shots : Shots total (Does not include penalty kicks)
• SoT : Shots on target (Does not include penalty kicks)
• SoT% : Shots on target percentage (Does not include penalty kicks)
• G/Sh : Goals per shot
• G/SoT : Goals per shot on target (Does not include penalty kicks)
• ShoDist : Average distance, in yards, from goal of all shots taken (Does not include penalty kicks)
• ShoFK : Shots from free kicks
• ShoPK : Penalty kicks made
• PKatt : Penalty kicks attempted
• PasTotCmp : Passes completed
• PasTotAtt : Passes attempted
• PasTotCmp% : Pass completion percentage
• PasTotDist : Total distance, in yards, that completed passes have traveled in any direction
• PasTotPrgDist : Total distance, in yards, that completed passes have traveled towards the opponent's goal
• PasShoCmp : Passes completed (Passes between 5 and 15 yards)
• PasShoAtt : Passes attempted (Passes between 5 and 15 yards)
• PasShoCmp% : Pass completion percentage (Passes between 5 and 15 yards)
• PasMedCmp : Passes completed (Passes between 15 and 30 yards)
• PasMedAtt : Passes attempted (Passes between 15 and 30 yards)
• PasMedCmp% : Pass completion percentage (Passes between 15 and 30 yards)
• PasLonCmp : Passes completed (Passes longer than 30 yards)
• PasLonAtt : Passes attempted (Passes longer than 30 yards)
• PasLonCmp% : Pass completion percentage (Passes longer than 30 yards)
• Assists : Assists
• PasAss : Passes that directly lead to a shot (assisted shots)
• Pas3rd : Completed passes that enter the 1/3 of the pitch closest to the goal
• PPA : Completed passes into the 18-yard box
• CrsPA : Completed crosses into the 18-yard box
• PasProg : Completed passes that move the ball towards the opponent's goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area
• PasAtt : Passes attempted
• PasLive : Live-ball passes
• PasDead : Dead-ball passes
• PasFK : Passes attempted from free kicks
• TB : Completed pass sent between back defenders into open space
• PasPress : Passes made while under pressure from opponent
• Sw : Passes that travel more than 40 yards of the width of the pitch
• PasCrs : Crosses
• CK : Corner kicks
• CkIn : Inswinging corner kicks
• CkOut : Outswinging corner kicks
• CkStr : Straight corner kicks
• PasGround : Ground passes
• PasLow : Passes that leave the ground, but stay below shoulder-level
• PasHigh : Passes that are above shoulder-level at the peak height
• PaswLeft : Passes attempted using left foot
• PaswRight : Passes attempted using right foot
• PaswHead : Passes attempted using head
• TI : Throw-Ins taken
• PaswOther : Passes attempted using body parts other than the player's head or feet
• PasCmp : Passes completed
• PasOff : Offsides
• PasOut : Out of bounds
• PasInt : Intercepted
• PasBlocks : Blocked by the opponent who was standing it the path
• SCA : Shot-creating actions
• ScaPassLive : Completed live-ball passes that lead to a shot attempt
• ScaPassDead : Completed dead-ball passes that lead to a shot attempt
• ScaDrib : Successful dribbles that lead to a shot attempt
• ScaSh : Shots that lead to another shot attempt
• ScaFld : Fouls drawn that lead to a shot attempt
• ScaDef : Defensive actions that lead to a shot attempt
• GCA : Goal-creating actions
• GcaPassLive : Completed live-ball passes that lead to a goal
• GcaPassDead : Completed dead-ball passes that lead to a goal
• GcaDrib : Successful dribbles that lead to a goal
• GcaSh : Shots that lead to another goal-scoring shot
• GcaFld : Fouls drawn that lead to a goal
• GcaDef : Defensive actions that lead to a goal
• Tkl : Number of players tackled
• TklWon : Tackles in which the tackler's team won possession of the ball
• TklDef3rd : Tackles in defensive 1/3
• TklMid3rd : Tackles in middle 1/3
• TklAtt3rd : Tackles in attacking 1/3
• TklDri : Number of dribblers tackled
• TklDriAtt : Number of times dribbled past plus number of tackles
• TklDri% : Percentage of dribblers tackled
• TklDriPast : Number of t...

Comprehensive football statistics on the home team to win, including win percentages, goals, and betting insights. Updated daily.

Football Teams PPG Table is a very popular market for football betting, and this page aims to serve as a list of highly-qualified fixtures. It will show you upcoming fixtures (and the teams playing in those fixtures) ranked by Football Teams PPG Table occurance in their current season.

Context

This dataset contains European football team stats. Only teams of Premier League, Ligue 1, Bundesliga, Serie A and La Liga are listed.

Content

Auxiliary datasets: * 2021-2022 Football Player Stats * 2021-2022 Football Team Stats

Acknowledgements

Data from Football Reference. Image from Wyscout.

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Comprehensive football statistics on away team wins, including win percentages, goals, and betting insights. Updated daily.

Comprehensive football statistics including win percentages, goals, corners, and betting insights and tips. Updated daily.

Comprehensive football statistics on matches where both teams score, including goals and betting insights. Updated daily.

Premier League, Serie A, La Liga, Bundesliga, Ligue 1 from 2017-2018 to 2020-2021. 1 collection for each league of a certain season. 1 document for each player. Within each document:
- name, age, nationality, height, weight, team, position. - general stats: games, time, yellow cards, red cards. - offensive stats: goals, assists, xG, xA, shots, key passes, npg, npxG, xGChain, xGBuildup. - defensive stats: Tkl, TklW, Past, Press, Succ, Block, Int. - passing stats: Cmp, Cmp%, 1/3, PPA, CrsPA, Prog.

Three data resources were used: Understat, api-football and Fbref. For more information on the data acquisition phase, I recommend reading the Football players notebook in the Code section.

This dataset is built with the aim of supporting an analysis to try to identify the most probable top performance age range of a player knowing the league in which he plays, his physical characteristics, his role and his nationality.

Tracking 7 upcoming fixtures. Data updated every 6 hours from official league sources.

There are 2 datasets here.

football_matches_dataset.csv

This is the main dataset with complete information.

Data description. - 54 features, see their description below. - 16332 matches - totally every match from Top-5 European football leagues (EPL, La liga, Serie A, Bundesliga, Ligue 1) 9 seasons (2014/2015 - 2022/2023), except those several matches (in Ligue 1 2019/2020) which were canceled because of coronavirus and the match between SC Bastia and Lyon (0 - 3) in Ligue 1 (April 16, 2017) which ended in a technical defeat to SC Bastia after halftime. - Complete information from understat.com and whoscored.com websites, no missing values except some NaNs where some averaged stats (for 2 or 4 previous matches) could not be calculated beacause of too few games since the start of the season. - Team names correspond to understat.com website names. - Each line is each match.

How was the data collected? - Parsing understat.com website. Collecting team names, date of the match, league, season, score, player names (lineups and substitutions) and xG (expected goals metric). Using python. - Parsing whoscored.com website. Adding players match ratings and positions. Since whoscored.com website seems impossible to parse with just code, this was done by octoparse.com tool. Their website describes the method. - Adding more information like teams table standings and points at the time of the match, average values of such stats as xG and team players ratings for 2 or 4 previous matches, etc. Using python.

Columns description. - 0 - id - 1-2 - home team and away team names - 3 - date of the match (e.g. "August 08 2014") - 4-5 - league and season, which the match belongs to - 6-7 - home team and away team scores (number of goals scored) - 8-9 - home team and away team xG (expected goals) - 10 - datetime of the match (e.g 2014-08-08) - 11-12 - home team and away team current standings (table positions) just before the start of the match - depending on team names alphabetical order in case of the same number of points or the first matches of the season - 13-14 - home team and away team current number of points just before the start of the match - 15-18 - total number of points home/away team have gained in their previous 4 matches or previous 2 home matches (for home team) or previous 2 away matches (for away team) - can be NaN in case of the first matches of the season when teams have not played enough matches this season yet - 19-22 - total number of goals home/away team have scored in their previous 4 matches or previous 2 home matches (for home team) or previous 2 away matches (for away team) - can be NaN in case of the first matches of the season when teams have not played enough matches this season yet - 23-26 - total number of goals home/away team have conceded (goals against) in their previous 4 matches or previous 2 home matches (for home team) or previous 2 away matches (for away team) - can be NaN in case of the first matches of the season when teams have not played enough matches this season yet - 27-30 - average value of xG home/away team have gained in their previous 4 matches or previous 2 home matches (for home team) or previous 2 away matches (for away team) - can be NaN in case of the first matches of the season when teams have not played enough matches this season yet - 31-34 - average value of xG-against (xG of the team opponent) home/away team have gained in their previous 4 matches or previous 2 home matches (for home team) or previous 2 away matches (for away team) - can be NaN in case of the first matches of the season when teams have not played enough matches this season yet - 35-36 - home team and away team starting formation (values in [0, 1, 2, 3, 4]: 0 for 2 defenders and 1 forward, 1 for 3 defenders and 1 forward, 2 for 2 defenders and 2 forwards, 3 for 3 defenders and 2 forwards, 4 if otherwise) - 37-42 - average home/away team players match rating separately among defense, midfield, attack players (defense, midfield or attack player is determined by his position in the match) - 43-54 - average value of defense, midfield, attack players match ratings for home/away team in their previous 4 matches or previous 2 home matches (for home team) or previous 2 away matches (for away team) - can be NaN in case of the first matches of the season when teams have not played enough matches this season yet

ratings.csv

This is the additional dataset.

Brief dataset description. This dataset contains the information about position and rating for every player in every match. The data was collected from whoscored.com website.

Columns description. - 0 - id (trash, may be repeated) - 1 - URL of whoscored.com website match page - 2 - player name (corresponds to whoscored.com website) - 3 - player position in the match (standard abbreviations + "Sub" when the player has entered the pitch...

Comprehensive football statistics on matches with over 2.5 goals, including win percentages and betting insights. Updated daily.

Tracking 7 upcoming fixtures. Data updated every 6 hours from official league sources.

Half with Most Goals / Highest Scoring Half (2nd) is a very popular market for football betting, and this page aims to serve as a list of highly-qualified fixtures. It will show you upcoming fixtures (and the teams playing in those fixtures) ranked by Half with Most Goals / Highest Scoring Half (2nd) occurance in their current season.

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...

BTTS First Half (Top-Performing Teams) is a very popular market for football betting, and this page aims to serve as a list of highly-qualified fixtures. It will show you upcoming fixtures (and the teams playing in those fixtures) ranked by BTTS First Half (Top-Performing Teams) occurance in their current season.

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Comprehensive statistics on football matches ending in a draw, including team performance, odds, and betting insights. Updated daily.

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