In 2024/25, the average match attendance in the Premier League was 40,498. This represented an increase of around five percent on the previous season.

Context

This dataset is a collection of basic but crucial stats of the English Premier League 2020-22 season. The dataset has all the Team Stats that played in the EPL and their standard stats such as Team, Refree, xG, xA, Opponent, Captain and more!

Content

• Date : Date listed is local to the match
• Time : Time listed is local to match Venue
• Comp : Type of competition the team is playing
• Round : Round or phase of competition
• Day : Day of Week
• Venue : Home Ground or Away
• Result : Win or Loss of the Team
• gf : Goals For
• ga : Goals Against
• Opponent : The opponent Team
• xg : Expected Goals
• xga : Expected Goals Allowed
• poss : Posession(Calculated as percentage of passed attempted)
• Attendance : Audience for the match
• Captain : Captain of the team
• Formation : Number of players in each row from defender to forwards
• Refree : Refree for the match
• Match report : Report of the match
• Notes : Notes for the match
• sh : Shots total
• sot : Shots on Target
• dist : Average distance (in yards)
• fk : Shots from free kicks
• pk : Penalty kicks made
• pkatt : Penalty kicks Attempted
• Season : Season year

Inspiration:

You can do many things with this dataset 1. Machine Learning Algorithms can be used to predict the Winner of the match 2. Which Team got the most penalty kicks 3. Analysing the Team formations, And Many More......

The possibilities are endless, create a notebook and explore them!

Do upvote if you like this Dataset!

Context

I am an avid soccer fan and I thought it would be cool to observe various trends in statistics over the history of the English Premier League.

Content

The statistics of past Premier League Seasons is recorded in an easy to use JSON format. At the moment, I have only uploaded the past 5 seasons, but I will upload more data in the coming days.

The data is organized under different folders, each for a different season. For Premier League Seasons 2017-18 and onward, a lot more detailed data has been collected. The data represents different aspects of the game.

(Note that the italicized data is not available for seasons prior to the 2017-18 season.

• squadStd : Brief overview of statistics throughout the season. Contains data regarding goals scored/conceded, number of players used throughout the season, yellow cards and red cards accumulated through the course of the season, and so on.

• keeperStd : Statistics regarding goalkeepers. Shots faced, shots saved, clean sheets(a game where a keeper does not concede any goals) are all recorded here.

• keeperAdv : A modern goalkeeper does more than just stop shots on goal. They often act like an 11th outfield player, often getting involved in building up attacks by distributing the ball, or coming out of the penalty box to perform a defensive action (such as a tackle, interception, block, or even a tactical foul. The "#OPA" stat describes the number of defensive actions taken outside the penalty area, and the "AvgDist" metric measures the goalkeepers average position (in metres) away from the goal line.

• squadShoot : Actions relating to shooting and goal scoring. No of shots taken, shots on target, shot distance, freekicks and penalties attempted and so on

• squadPass : Stats related to passes. Passes attempted, completed, distance covered by passes etc. Progressive passes are passes which progress the ball towards the opponents goal. A pass towards a team's own goal covers 0 distance in this metric. Also contains assist statistics.

• squadPassTypes : Covers the nitty-gritty details of passes - what part of the body was used to make the pass (head, left foot, right foot). Whether it was a throw in or a dead ball situation (corner, free kick). The height of the pass (ground level, below shoulder height, or above shoulder height). Passes made under pressure, through balls (pass through two defenders), crosses, cross field balls (passes that switch the play across the width of the field). Also covers the outcome of the pass - completed, resulted in offside, went out of bounds, or was blocked or intercepted.

• squadGCA: Goal/Shot Creating Actions. The direct actions that resulted in a shot or goal. These actions include dribbles, passes or fouls drawn.

• squadDef : Defensive actions. Shows tackles attempted, successful tackles and what third of the field the tackle was made. Also shows number of dribblers tackled, times dribbled past, number of pressures, shots/passes blocked and intercepted, errors (events by own team leading to opponents shot).

• squadPossession: Possession play. Dribbles, carries, distance dribbled, nutmegs ( 😆 )

• squadPlayTime : Stats regarding subsitutions.

• squadMisc : Miscellaneous stats. Fouls drawn/committed. Aerial duels won/lost. These might not make much difference in the overall analysis, but are still worth noting.

Note about Expected Stats

Some statistics have an "x" before them: xG, npxG, xA among others. These are advanced metrics which have emerged by advances in match analysis and machine learning. These stats show how a team is expected to perform. xG indicates how many goals a team is expected to score, from the chances they had. xG, for instance, depends on the distance of the shot, the type of shot(free kick, penalty, header, etc). How a team is expected to perform can be vastly different from its actual performance. A team with high xG doesn't necessarily score more goals, it just takes shots from positions where it is highly likely to score from. The actual outcome may be very different, and depends on various external factors such as the position of goalkeeper, whether it took a deflection or not, the quality of the player and goalkeeper (a shot taken from the exact same position by an attacker and a defender could result in different outcomes. Also, better players can convert chances better than others), and luck. Note that these statistics are only available in the recent seasons.

For more info, please refer to Opta's Advanced Metrics.

Acknowledgements

All data was scraped through Football Reference

This statistic presents the average stadium utilization of professional football matches of The Premier League in England from 2010 to 2017. In 2017, the average stadium of The Premier League was utilized at 96.5 percent.

Manchester City won the Premier League in the 2021-2022 Season

In this dataset, I present to you all the general data on the passing, shooting, and defensive actions of the Premier League teams.

Let's explain the dataset briefly.

"englandpremierleaguetable22.csv" is the name of the data that describes the general situation of the league table.

The descriptions of the columns in this data are as follows: Rk - League Rank Squad: Team MP: Match Played W: Team Wins D: Team Draws L : Team: Team Losts GF: Team Goals GA: Team Goal Conceded GD: Team Goal Difference(Average) Pts: Team Season Points xG : Team Expected Goals xGA: Team Expected Goals Allowed xGD : Team Expected Goals Differences xGD : Team Expected Goals Differences per 90 minutes Atttendance : Team Tota Attendances Top Team Scorer: Team Scorer

"englandpremierleaguestats22.csv" is the name of data that describes goals, assists, and expected goal numbers for all teams.

The descriptions of the columns in this data are as follows: Squad: Team NPl : Number of Players Age: Average Age Poss: Team Average Possession GlsTot: Team Goals AstTot : Team Assists GmPKTot : Goal minus Penalty Kick PKTot : Penalty Kicks made PKattTot : Attempted penalty kick CrdYel : Yellow cards CrdRed : Red cards GlsPer90 : Goals per 90 minutes AstPer90 : Assists per 90 min GpAPer90 : Goal + Asists GmPKPer90 : Goals -Penalty Kick GpAmPKPer90 : (Goals + Assists)- Penalty kick xGTot : Expected goals npxGTot : Non-penalty expected goals xATot : Expected assists npxGpxATot : Non-penalty expected goals + expected assists xGPer90 : Expected goals per 90 min xAPer90 : Expected assists per 90 min xGpxAPer90 : (Expected goals + Expected assists) per 90 min npxGPer90 : Non-penalty expected goals per 90 min npxGxAPer90 : (Non-penalty expected goals+ expected assists) per 90 min

"englandpremierleaguesquadshooting22.csv" is the name of data that describes the conversion rate of shots into goals for all teams.

The descriptions of the columns in this data are as follows: Squad: Team NPl : Number of Players 90s: MatchedPlayed in 90min GlsTot : Team Goals TotSh : Team Total Shots TotShTg : Shots on Target SoTgRt : Shots on Target Conversion Rate Sh/90 : Shots per 90 minutes SoT/90: Shots on target per 90 min G/Sh :Goals per shot G/SoT : Goals per shot on target Dist: average distances, from goal of shot FK : free-kick PK : penalty kick made PKatt attempted penalty kick xG : expected goals npxG : non-penalty expected goals npxG/Sh : non-penalty expected goals per shot GmxG : Goal minus expected goals npGmxG : Non-penalty goals minus expected goals

"englandpremierleaguesquadpossession22.csv" is the name of data that describes the touches, dribblers, and carries for all teams.

The descriptions of the columns in this data are as follows: Squad: Team NPl : Number of Players Poss : Team Possesion TotTouch : Total Touches DefPenTouc : Touches in Defensive penalty area Def3rdTouc : Touches in Defensive 3rd Mid3rdTouc : Touches in Middle 3rd Att3rdTouc : Touches in Attacking 3rd AttPenTouc : Touches in Attacking penalty area LiveTouc : Live-ball touches SuccDrib : Successful dribbles AttDrib : Dribbles attempted SuccDribRt : Minimum 5 dribbles per squad game Megs : Number of times a player dribbled the ball through opposition player's leg TotCarr : Number of times player controlled the ball with their feet TotDistCarr : Total distances, in yards, carries PrgDistCarr : Progressive distances carries ProgCarr : Progressive carries 1/3Carr : Carries that enter the 1/3 of the pitch closest the goal CPA : Carries into the 18 yard box MisCarr : Number of times a player failed when attempting to gain to control a ball DisCarr : Number of times a player loses control of the ball TargRec : Number of times a player target of an attempting a pass SucRec : Number of times a player successfully received a pass RecRt : Percentage time of a player successfully received a pass ProgRec : Progressive Pass received

"englandpremierleaguesquadpassingstats22.csv" is the name of data that describes short, medium, and long pass numbers for all teams.

The descriptions of the columns in this data are as follows: Squad: Team NPl : Number of Players 90s: MatchedPlayed in 90min CmpTot : Total completed passes AttTot : Attempted passes TotCmpRt : Passes Completion Percentage TotDist : Total distance passes PrgDist : Progressive distance passes CmpSh : Short completed passes AttSh : Attempted short passes ...

This statistic presents the average stadium capacity of professional football matches of The Premier League in England from 2010 to 2017. In 2017, the average stadium of The Premier League had the capacity of 37 thousand spectators.

The English Premier League is the most popular and highest level football league in the world. This dataset contains all games from new era of English Professional Football Soccer called Premier League (before 1993, it was a different competition system called English Football League). Data set gets updated weekly according to new results after weekend games.

• All matches from season 1993-1994 up to date.
• Matches include statistics when possible
• Matches includes basic Bet356 odds when possible, including expected average goal per match.

Inspiration Develop a data analysis System with up to date match results that answers statistical questions based on historical results.

Manchester United had the highest average attendance in the Premier League in the 2024/25 season, attracting crowds of around 73,815 to Old Trafford. Meanwhile, Goodison Park typically saw crowds of over 39,000 in its final season as the home of Everton's men's team.

The aggregate attendance of the English Premier League, commonly referred to as the Premier League, has exhibited a gradual increase from 13 million attendees in 2009/2010 to approximately 15 million attendees in 2021/22. In 2020/21, the total aggregate attendance at the games fell to its lowest value throughout this period as a result of the coronavirus (COVID-19) pandemic . The Premier League The Premier League, currently consisting of 20 professional football clubs, constitutes the highest level of professional club football within the United Kingdom (UK). Alongside the so-called “Big-Five” leagues of Europe, which include the top-tier football leagues of England, Spain, Italy, Germany, and France; the Premier League is considered to be one of the most widely followed and well-known football leagues in the world. Revenue The Premier League was established in 1992 following the decision of the then first division of the English Football league to capitalize on a lucrative television rights deal. A decision that has led to the clubs within the Premier League consistently yielding the largest combined revenue of the ‘Big-Five’ leagues of Europe with the combined revenue of all 20 Premier League clubs projected to be around 6.2 billion euros in the 2021/22 season; over two billion euros more than the expected combined revenue of their counterparts in Spain and Germany, who rank second and third respectively. The 2020/21 season The 2020/2021 season, following a delay until the 12 September due to the postponement of the previous season's conclusion as a result of the impact of the COVID-19 containment measures culminated in Manchester City FC reclaiming their title from the defending champions, Liverpool FC. Leading to the club's third championship title within the last four seasons. In the 2021/22 season the Premier League bounced back from COVID-19 as the strongest attendance wise in the Big-Five with an average of close to 40 thousand spectators per match.

Results for group 1 v group 2 balanced data set (Best Average Test Performance = 61.5% and Best Average Test Error = 11.6% with a combination of seven variables) and group 1 v group 2 model variables as means and standard deviations for player groupings.

Context For most football fans, May - July represents a lull period due to the lack of club football. What makes up for it, is the intense transfer speculation that surrounds all major player transfers today. Their market valuations also lead to a few raised eyebrows, lately more than ever. I was curious to see how good a proxy popularity could be for ability, and the predictive power it would have in a model estimating a player's market value.

Content name: Name of the player

club: Club of the player

age : Age of the player

position : The usual position on the pitch

position_cat :

1 for attackers

2 for midfielders

3 for defenders

4 for goalkeepers

market_value : As on transfermrkt.com on July 20th, 2017

page_views : Average daily Wikipedia page views from September 1, 2016 to May 1, 2017

fpl_value : Value in Fantasy Premier League as on July 20th, 2017

fpl_sel : % of FPL players who have selected that player in their team

fpl_points : FPL points accumulated over the previous season

region:

1 for England

2 for EU

3 for Americas

4 for Rest of World

nationality

new_foreign : Whether a new signing from a different league, for 2017/18 (till 20th July)

age_cat

club_id

big_club: Whether one of the Top 6 clubs

new_signing: Whether a new signing for 2017/18 (till 20th July)

Inspiration To statistically analyse the beautiful game.

In the 2022/23 season, players in the English Premier League (EPL) tended to be sent off at a higher frequency than Women's Super League (WSL) players, with a red card being shown nearly every 13 games on average. Meanwhile, in the same season, the WSL saw a red card once in nearly every 16 matches.

📌 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

In the 2012/13 season, the average in-play time of English Premier League matches was **** percent. By 2022/23, this had dropped to **** percent, meaning that fans may have seen a slight increase in stoppages and off-the-ball time wasting.

This dataset consists of the Premier League team stats for seasons 2022/2023, 2021/2022 and 2022/2021. The data was scraped from fbref.com and formatted into a csv file.

Columns:

date = Date of the match time = Kick-off time of the match comp = Competition of the match (i.e English Premier League) round = The match week the match took place on day = The day the match took place on (i.e Monday, Tuesday etc) venue = Whether team was Home, Away or Neutral venue result = Whether the team Won, Lost or Drew (W, L, D) gf = How many goals the team scored ga = How many goals the team conceded opponent = Who the team faced that day xg = Expected goals xa = Expected goals allowed poss = Possession attendance = How many people attended the match captain = Captain of the team for match formation = Formation the team used for match referee = The referee for the match match report = Please ignore notes = Please ignore sh = Shots total sot = Shots on target dist = average distance by shot fk = shots from free kicks pk = Penalty kicks made pkatt= Penalty kicks attempted season = The year the season took place (i.e for 2022/2023 season year would be 2023) team = The team the stats belong to (i.e Manchester City)

English Premier League matches from 2023/2024 season, will be updated weekly. Data is scraped from https://fbref.com/en/

Unnamed: 0: An index or identifier column.

Date: The date when the match took place.

Time: The kickoff time of the match.

Comp: The competition name, which is the Premier League for the rows displayed.

Round: The matchweek or round of the competition.

Day: The day of the week the match was played.

Venue: Indicates whether the team was playing at home or away.

Result: The outcome of the match from the perspective of the team mentioned at the end (W = Win, D = Draw, L = Loss).

GF (Goals For): The number of goals scored by the team.

GA (Goals Against): The number of goals conceded by the team.

Opponent: The name of the opposing team.

xG: Expected goals for the team.

xGA: Expected goals against the team.

Poss: Possession percentage during the match.

Attendance: The number of spectators present at the venue.

Captain: The name of the team captain.

Formation: The team's formation.

Referee: The name of the match referee.

Match Report: A link or reference to a detailed match report.

Notes: Any additional notes about the match.

Sh (Shots): Total number of shots taken by the team.

SoT (Shots on Target): Number of shots on target.

Dist: Average distance (likely in meters) from which shots were taken.

FK: Number of free kicks taken.

PK (Penalty Kicks): Number of penalty kicks scored.

PKatt (Penalty Kicks Attempted): Number of penalty kicks attempted.

Season: The season year.

Team: The team the data row is about.

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

This statistic shows the number of television viewers of the English Premier League from the 2010/11 season to the 2016/17* season, by broadcaster. After starting this period with ***** times the TV audience of rivals BT, Sky's share of the Premier League's viewership has fallen in the past years, to end the period with *** million viewers compared to BT's *** million.

The Premier League distributed over *** billion British pounds to its members in 2024/25, with champions Liverpool receiving a payout of ***** million British pounds. The league's distribution model sees the bulk of broadcasting revenue being shared evenly, meaning that even relegated clubs can receive a substantial payout.

Context

As the EPL season is put on hold due to COVID-19, let us look into the data from the past 10 seasons. We have data from 3800 matches which could give interesting insights about the English Premier League and the teams.

Content

Every match in the 10 seasons from 2009 till 2019 is listed with the following details. Div - Division (E0) Date - Date of the match HomeTeam AwayTeam FTHG - Full Time Goal of Home Team FTAG - Full Time Goal of Away Team FTR - Full Time Result (H - Home side win, A - Away side win, D - Draw) HTHG - Half Time Goal of Home Team HTAG - Half Time Goal of Away Team HTR - Half Time Result (H - Home side win, A - Away side win, D - Draw) Referee - Name of the referee who officiated the match HS - Total shots made by home team AS - Total shots made by away team HST - Total shots on target made by home team AST - Total shots on target made by away team HF - Total fouls committed by home team AF - Total fouls committed by away team HC - Total corners received for home team (conceded by away team) AC - Total corners received for away team (conceded by home team) HY - Total yellow cards received for home team AY - Total yellow cards received for away team HR - Total red cards received for home team AR - Total red cards received for away team

Acknowledgements

This data was created from the files downloaded from the link below. https://www.football-data.co.uk/englandm.php

Inspiration

Try to answer the following questions.

1. How many matches were played in EPL between 2009 - 2019? How many teams played in the EPL during these 10 seasons? Which are they?
2. How many games were played by each team in these 10 seasons?
3. Which teams played all the seasons in EPL from 2009 - 2019? Which teams played only 1 season in EPL between 2009 - 2019?
4. How much percent of the total matches were won by the home team, away team or draw?
5. How many referees officiated the EPL matches between 2009 and 2019? List them with the total number of matches officiated.
6. In how many games did the teams loosing at half time come back to win the game at full time (comeback wins)? List all of them.
7. Sort the comeback wins based on year.
8. Sort the comeback wins based on season.
9. Generate a data frame with the following info, a. Number of games per team. b. Home wins per team. c. Home defeats per team. d. Away wins per team. e. Away defeats per team. f. Number of draw per teams (home and away combined). g. Total points in EPL across 10 seasons.
10. Extract the total points gained by teams who played in all 10 seasons.
11. Which game/games produced most number of goals? List them.
12. Find out the total goals scored by each team at home, away and in total. Find the average goals scored per game at home and away (not combined).
13. Find the total number of dominant performances by each team. (Winning by a goal margin of 3 or more goals at Full Time).
14. Find the total points collected by each team per season. Extract the points table of the teams who played all 10 seasons.
15. Find the total shots on goal (shots and shots on target separately) made by each team.
16. Extract the shots and goals stats of the teams who played all 10 seasons.
17. Find the conversion rates (goals/shots) for the teams which played all 10 seasons.
18. Find the average goals conceded @home and @away for all the teams.
19. Find the total yellow and red cards given to home and away teams.
20. Find the total yellow and red cards given to each team home and away.
21. Find the total number of yellow and red cards awarded by each referee.
22. Find the total number of fouls committed by each team @ home and away.
23. Find the yellow card per foul in % and red card per foul in % for every team.
24. Find the corners gained/conceded during home and away games for each team. Also find the total corners gained and conceded during 2009 - 2019.
25. Find the EPL champions of each year with their points. If 2 teams have equal points in a season, find the champion by goal difference. (Total Goals Scored - Total Goals Conceded) Find how many teams became EPL champions during 2009 - 2019. List their names. Find the team which won the EPL more times between 2009 - 2019.