This dataset was obtained as part of my project to rate player performances in a game and use it to model game outcomes. I was looking for an open dataset which included important in-game stats for players but couldn't find one. Hence I ended up scraping data myself. Subsequently, it has been successfully used to predict player performances in future games and build an optimum fantasy league team. I would be updating the dataset monthly to include newer games of the current season.

The dataset includes 2 JSON files. One of the files describes in-game match stats for every match of the past 4 seasons (current season included) like player touches, passes, shots, yellow cards, saves etc. Some of the stats are available as aggregate stats for the entire team and some of them are player specific. Second, file describes general match outcomes like the full time and half-time score etc.

Data snapshot --

{
  "1190174":{
    "13":{
      "team_details":{
        "team_id":"13",
        "team_name":"Arsenal",
        "team_rating":"7.30714285714286",
        "date":"11/08/2017"
      },
      "aggregate_stats":{
        "fk_foul_lost":"9",
        "won_contest":"16",
        "possession_percentage":"70",
        "total_throws":"21",
         .............
       },
      "Player_stats":{
        "Petr Cech":{
          "player_details":{
            "player_id":"6775",
            "player_name":"Petr Cech",
            "player_position_value":"1",
            "player_position_info":"GK",
            "player_rating":"5.78"
          },
          "Match_stats":{
            "good_high_claim":"1",
            "touches":"27",
            "total_tackle":"1",
            "total_pass":"20",
            "formation_place":"1",
            "accurate_pass":"16"
          },

This dataset could be used to predict player performances and how a particular player/team plays against another. Can a game outcome be modeled on the player composition of the participating teams? Are goals the most important factor that determines season outcomes or something other than historical goals be used to predict the future team performance in the league?

Analysis of ‘Barclays Premiere League for last 12 seasons’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lumierebatalong/english-premiere-league-team-datasets on 28 January 2022.

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

Context

Barclay premier league is the best league in the world 💯 . It has 20 teams that qualified for the title. Among these 20 teams there are 5 teams which have already won the title in the last 12 seasons namely Man City, Liverpool, Man United, Chelsea, Leicester with two outsiders Arsenal and Tottenham. Who is your favorite team and how can you predict their title victory for the current or next season? The ball is in your camp 👀 .

Content

Notes for Football Data

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use and refer to data collected in earlier seasons. Each data contains last 12 seasons of English Premier League.

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards

I remove some features.

Acknowledgements

This dataset contains data for last 12 seasons of English Premier League. The dataset is sourced from http://www.football-data.co.uk/ website and contains various statistical data such as final and half time result, corners, yellow and red cards etc

Inspiration

Can you explain why Man United has not won the title for last 12 seasons?. Can you predict the victory of your favorite team in every championship game?.

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

Results for group 0 v group 1 balanced data set (Best Average Test Performance = 67.9% and Best Average Test Error = 10.8% with a combination of nine variables) and group 0 v group 1 model variables as means and standard deviations for player groupings.

This statistic shows a ranking of advertising categories based on their television screen time during FIFA World Cup games in the United Kingdom (UK) in June and July 2018. Throughout 30 World Cup games shown in ITV, betting ads were the most prominent with a total of 88 minutes of screen time, followed by motoring ads with 68 minutes and grooming ads with 39 minutes.

The aim of this study was to establish normative data and objective benchmarks for key CMJ, CMRJ, and IMTP metrics in professional and youth soccer players in the English Football League (EFL) League 2 and investigate age-specific statistical differences between groups. To the authors’ knowledge, such a study has not yet been performed within any professional football league worldwide.

This dataset provides detailed match events and summaries for over 300 English Premier League matches from the 2023/24 season. It captures the drama, tactics, and player performances, enabling users to gain new insights into sports analytics. The file includes transcripts of both the match summary and events, along with key details for each fixture.

Columns

• id: Unique identifier for each match.
• Home: The name of the home team.
• Away: The name of the away team.
• Date: The date the match was played.
• Stadium: The stadium where the match took place.
• Attendance: The recorded attendance for the match.
• Referee: The name of the match referee.
• events: Detailed minute-by-minute match events.
• summary: A summary of the match, often including key moments and player actions.

Distribution

The data file is typically provided in CSV format. It covers over 300 matches from the 2023/24 Premier League season. Specific numbers for rows or records are not available in the provided details, but the dataset offers event details for every minute played.

Usage

This dataset is ideal for various applications, including: * Summarisation tasks for football matches. * Text generation of match reports or commentary. * Sports analytics to uncover patterns in team or player performances. * Natural Language Processing (NLP) research related to sports commentary. * Studying tactical analyses and game flow.

Coverage

The dataset primarily focuses on the English Premier League. The time range for the matches is the 2023/24 season, specifically from 12th August 2023 to 15th April 2024. The data covers matches played across various stadiums in England, featuring all teams participating in the Premier League during this period.

License

CC-BY-NC

Who Can Use It

This dataset is intended for: * Sports analysts seeking to analyse team strategies and player statistics. * Researchers in NLP and text mining interested in sports commentary. * Developers building applications that require rich football match data. * Football enthusiasts looking for detailed insights into Premier League games. * Anyone interested in text-based sports data for academic or personal projects.

Dataset Name Suggestions

• English Premier League - Match Commentary
• Premier League Match Summaries & Events 2023/24
• EPL Football Commentary Dataset
• 2023/24 Premier League Match Transcripts
• English Football Match Data

Attributes

Original Data Source: English Premier League - Match Commentary

Notes for Football Data from football-data.co.uk.

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use (in particular odds from specific bookmakers no longer used) and refer to data collected in earlier seasons. For a current list of what bookmakers are included in the dataset please visit http://www.football-data.co.uk/matches.php

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards HBP = Home Team Bookings Points (10 = yellow, 25 = red) ABP = Away Team Bookings Points (10 = yellow, 25 = red)

Note that Free Kicks Conceeded includes fouls, offsides and any other offense commmitted and will always be equal to or higher than the number of fouls. Fouls make up the vast majority of Free Kicks Conceded. Free Kicks Conceded are shown when specific data on Fouls are not available (France 2nd, Belgium 1st and Greece 1st divisions).

Note also that English and Scottish yellow cards do not include the initial yellow card when a second is shown to a player converting it into a red, but this is included as a yellow (plus red) for European games.

Key to 1X2 (match) betting odds data:

B365H = Bet365 home win odds B365D = Bet365 draw odds B365A = Bet365 away win odds BSH = Blue Square home win odds BSD = Blue Square draw odds BSA = Blue Square away win odds BWH = Bet&Win home win odds BWD = Bet&Win draw odds BWA = Bet&Win away win odds GBH = Gamebookers home win odds GBD = Gamebookers draw odds GBA = Gamebookers away win odds IWH = Interwetten home win odds IWD = Interwetten draw odds IWA = Interwetten away win odds LBH = Ladbrokes home win odds LBD = Ladbrokes draw odds LBA = Ladbrokes away win odds PSH and PH = Pinnacle home win odds PSD and PD = Pinnacle draw odds PSA and PA = Pinnacle away win odds SOH = Sporting Odds home win odds SOD = Sporting Odds draw odds SOA = Sporting Odds away win odds SBH = Sportingbet home win odds SBD = Sportingbet draw odds SBA = Sportingbet away win odds SJH = Stan James home win odds SJD = Stan James draw odds SJA = Stan James away win odds SYH = Stanleybet home win odds SYD = Stanleybet draw odds SYA = Stanleybet away win odds VCH = VC Bet home win odds VCD = VC Bet draw odds VCA = VC Bet away win odds WHH = William Hill home win odds WHD = William Hill draw odds WHA = William Hill away win odds

Bb1X2 = Number of BetBrain bookmakers used to calculate match odds averages and maximums BbMxH = Betbrain maximum home win odds BbAvH = Betbrain average home win odds BbMxD = Betbrain maximum draw odds BbAvD = Betbrain average draw win odds BbMxA = Betbrain maximum away win odds BbAvA = Betbrain average away win odds

MaxH = Market maximum home win odds MaxD = Market maximum draw win odds MaxA = Market maximum away win odds AvgH = Market average home win odds AvgD = Market average draw win odds AvgA = Market average away win odds

Key to total goals betting odds:

BbOU = Number of BetBrain bookmakers used to calculate over/under 2.5 goals (total goals) averages and maximums BbMx>2.5 = Betbrain maximum over 2.5 goals BbAv>2.5 = Betbrain average over 2.5 goals BbMx<2.5 = Betbrain maximum under 2.5 goals BbAv<2.5 = Betbrain average under 2.5 goals

GB>2.5 = Gamebookers over 2.5 goals GB<2.5 = Gamebookers under 2.5 goals B365>2.5 = Bet365 over 2.5 goals B365<2.5 = Bet365 under 2.5 goals P>2.5 = Pinnacle over 2.5 goals P<2.5 = Pinnacle under 2.5 goals Max>2.5 = Market maximum over 2.5 goals Max<2.5 = Market maximum under 2.5 goals Avg>2.5 = Market average over 2.5 goals Avg<2.5 = Market average under 2.5 goals

Key to Asian handicap betting odds:

BbAH = Number of BetBrain bookmakers used to Asian handicap averages and maximums BbAHh = Betbrain size of handicap (home team) AHh = Market size of handicap (home team) (since 2019/2020) BbMxAHH = Betbrain maximum Asian han...

Detailed analysis of the business of the English Premier League, focusing on sponsorship and the media landscape Read More

This statistic presents the average number of stadium attendees of professional football matches of Ladbrokes League 1 in Scotland from 2010 to 2017. In 2017, the average number of people who attended matches of Ladbrokes League 1 amounted to 565 people.

Biographical data represented as means and standard deviations for player groupings.

Football is not only the most popular sport to watch and spectate in the United Kingdom (UK) and England, but also the most popular team sport to participate in. Between November 2023 and November 2024, roughly 2.2 million people in England played the sport. Football nation Being home to not only the biggest football league but the biggest and most successful sports league in the world, the Premier League, England has many football fans who support the sport with famous clubs such as Manchester United, Liverpool FC, Arsenal FC or Manchester City. Champions League Some of these top tier clubs compete in the UEFA Champions League with other high division teams, primarily from the other ’Big Five’ football leagues in Europe, Germany, Spain, Italy and France. In 2023/24, Real Madrid came out as the victor, winning their 15th Champions League title that season.

Notes for Football Data

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use (in particular odds from specific bookmakers no longer used) and refer to data collected in earlier seasons. For a current list of what bookmakers are included in the dataset please visit http://www.football-data.co.uk/matches.php

Key to results data:

• Div = League Division
• Date = Match Date (dd/mm/yy)
• Time = Time of match kick off
• HomeTeam = Home Team
• AwayTeam = Away Team
• FTHG and HG = Full Time Home Team Goals
• FTAG and AG = Full Time Away Team Goals
• FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win)
• HTHG = Half Time Home Team Goals
• HTAG = Half Time Away Team Goals
• HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available)

• Attendance = Crowd Attendance
• Referee = Match Referee
• HS = Home Team Shots
• AS = Away Team Shots
• HST = Home Team Shots on Target
• AST = Away Team Shots on Target
• HHW = Home Team Hit Woodwork
• AHW = Away Team Hit Woodwork
• HC = Home Team Corners
• AC = Away Team Corners
• HF = Home Team Fouls Committed
• AF = Away Team Fouls Committed
• HFKC = Home Team Free Kicks Conceded
• AFKC = Away Team Free Kicks Conceded
• HO = Home Team Offsides
• AO = Away Team Offsides
• HY = Home Team Yellow Cards
• AY = Away Team Yellow Cards
• HR = Home Team Red Cards
• AR = Away Team Red Cards
• HBP = Home Team Bookings Points (10 = yellow, 25 = red)
• ABP = Away Team Bookings Points (10 = yellow, 25 = red)

Note that Free Kicks Conceeded includes fouls, offsides and any other offense commmitted and will always be equal to or higher than the number of fouls. Fouls make up the vast majority of Free Kicks Conceded. Free Kicks Conceded are shown when specific data on Fouls are not available (France 2nd, Belgium 1st and Greece 1st divisions).

Note also that English and Scottish yellow cards do not include the initial yellow card when a second is shown to a player converting it into a red, but this is included as a yellow (plus red) for European games.

Key to 1X2 (match) betting odds data:

• B365H = Bet365 home win odds
• B365D = Bet365 draw odds
• B365A = Bet365 away win odds
• BSH = Blue Square home win odds
• BSD = Blue Square draw odds
• BSA = Blue Square away win odds
• BWH = Bet&Win home win odds
• BWD = Bet&Win draw odds
• BWA = Bet&Win away win odds
• GBH = Gamebookers home win odds
• GBD = Gamebookers draw odds
• GBA = Gamebookers away win odds
• IWH = Interwetten home win odds
• IWD = Interwetten draw odds
• IWA = Interwetten away win odds
• LBH = Ladbrokes home win odds
• LBD = Ladbrokes draw odds
• LBA = Ladbrokes away win odds
• PSH and PH = Pinnacle home win odds
• PSD and PD = Pinnacle draw odds
• PSA and PA = Pinnacle away win odds
• SOH = Sporting Odds home win odds
• SOD = Sporting Odds draw odds
• SOA = Sporting Odds away win odds
• SBH = Sportingbet home win odds
• SBD = Sportingbet draw odds
• SBA = Sportingbet away win odds
• SJH = Stan James home win odds
• SJD = Stan James draw odds
• SJA = Stan James away win odds
• SYH = Stanleybet home win odds
• SYD = Stanleybet draw odds
• SYA = Stanleybet away win odds
• VCH = VC Bet home win odds
• VCD = VC Bet draw odds
• VCA = VC Bet away win odds
• WHH = William Hill home win odds
• WHD = William Hill draw odds

• WHA = William Hill away win odds

• Bb1X2 = Number of BetBrain bookmakers used to calculate match odds averages and maximums

• BbMxH = Betbrain maximum home win odds

• BbAvH = Betbrain average home win odds

• BbMxD = Betbrain maximum draw odds

• BbAvD = Betbrain average draw win odds

• BbMxA = Betbrain maximum away win odds

• BbAvA = Betbrain average away win odds

• MaxH = Market maximum home win odds

• MaxD = Market maximum draw win odds

• MaxA = Market maximum away win odds

• AvgH = Market average home win odds

• AvgD = Market average draw win odds

• AvgA = Market average away win odds

Key to total goals betting odds:

• BbOU = Number of BetBrain bookmakers used to calculate over/under 2.5 goals (total goals) averages and maximums
• BbMx>2.5 = Betbrain maximum over 2.5 goals
• BbAv>2.5 = Betbrain average over 2.5 goals
• BbMx<2.5 = Betbrain maximum under 2.5 goals

• BbAv<2.5 = Betbrain average under 2.5 goals

• GB>2.5 = Gamebookers over 2.5 goals

• GB<2.5 = Gamebookers under 2.5 goals

• B365>2.5 = Bet365 over 2.5 goals

• B365<2.5 = Bet365 under 2.5 goals

• P>2.5 = Pinnacle over 2.5 goals

• P<2.5 = Pinnacle under 2.5 goals

• Max>2.5 = Market maximum over 2.5 goals

• Max<2.5 = Market maximum under 2.5 goals

• Avg>2.5 = Market average over 2.5 goals

• Avg<2.5 = Market average under 2.5 goals

Key to Asian handicap betting odds:

• BbAH = Number of BetBrain bookmakers used to Asian handicap averages and maximums ...

Manchester United is the most followed Premier League club in the UK. According to our survey, about 29 percent of internet respondents follow the Red Devils. Next in the ranking is Liverpool, followed by Arsenal, and Manchester City.

Which is the most popular Premier League club on Twitter?

Manchester United might have gone through a dry patch in terms of trophies for the past few campaigns, but this hasn’t done much damage to their popularity off the pitch. As of May 2022, the Red Devils had a total of 31 million followers on Twitter, more than any other club in the Premier League.

This dataset contains data for last 10 seasons of English Premier League including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.uk/ website and contains various statistical data such as final and half time result.

• Entries for English-Spanish: Scientific research & mathematical sciences (906 entries), Geosciences (10,215), Computer science, electronics & telecommunications (70,580), Industry (47,578), Transport & Maintenance (12,291), Economy (145,572), Biological sciences (38,989), Communication & media (8,143), Chemical & physical sciences (27,467). * Entries for English-French-German-Spanish: Environment (36,658), Health (66,727), Agriculture & food (25,975), Construction & public works (8,429), Law & policy (56,578), Sports & Leisure (17,312) * Two specialized lexicons: Spanish-English and English-French-German without domain codes: electronics, telematics, law, taxes, customs, etc. (550,000 entries). * Two general lexicons: Spanish-English-French-German and Spanish-English-French-German-Portuguese-Italian (83,000 entries).This terminological database contains, for each domain, a sub-domain indication is given (from 2 sub-domains for Scientific research to 39 for Sports & leisure). Each entry consists of a definition, phraseological unit, abbreviation, usage information, grammatical labels. Format: ASCII

The project has two main research questions: RQ1 - what is the financial impact of Covid-19 on English professional football clubs so far? RQ2 - what is the wider impact to the local community focusing on four professional football clubs and football community trusts? The data collected for the project is broken down below across the two research questions highlighted above and is split between quantitative data (research question 1) and qualitative data (research question 2). Data collection for RQ1 Quantitative data was extracted from the financial statements of football clubs and the relevant financial data was used to create a bespoke financial database in Microsoft Excel. The data covers all 92 professional football clubs in the EPL and EFL in any given season from 1992/1993 to 2019/2020. At present there are 20 clubs that compete in the EPL and 24 in each of the Championship, League 1, and League 2. Owing to promotion and relegation during the time period analysed, our database covers a total of 112 unique professional football clubs. The financial database contains 28 independent variables in respect of financial and sporting performance that we have defined as Key Performance Indicators (KPIs) for a football club. Data collection for RQ2 Qualitative data was sourced from four professional football clubs that are currently competing in League 1 at the time of writing. Semi-structured interviews were conducted with key individuals at the clubs. A total of 18 interviews were undertaken across the four clubs. Owing to the Covid-19 situation and various lockdowns and restrictions throughout the project, the majority of interviews (apart from one face-to-face visit) were conducted online using Microsoft Teams. Interviews were recorded and transcribed in Teams and then exported to Quirkos (a specialist qualitative analysis programme) for further thematic analysis. Interview schedules were designed based on job role of the interviewee. For example, interviews with CEOs covered all aspects of the business including finance and strategy whereas interviews with Community Managers focused more on the fans of clubs and wider social impact.

Explore match day statistics of every game and every team during the 2021-2022 season of the English Premier League!

Data includes data, teams, referee, and stats by home and away side such as fouls, shots, cards, and more! Also included is a dataset of the weekly rankings for the season.

The 2021–22 Premier League was the 30th season of the Premier League, the top English professional league for association football clubs since its establishment in 1992, and the 123rd season of top-flight English football overall. The start and end dates for the season were released on 25 March 2021, and the fixtures were released on 16 June 2021.

Manchester City successfully defended their title, securing a sixth Premier League title and eighth English league title overall on the final day of the season; it was also the club's fourth title in the last five seasons.

The data was collected from the official website of the Premier League. I then cleaned the data using google sheets

Analysis of ‘English Premier League stats 2019-2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/idoyo92/epl-stats-20192020 on 12 November 2021.

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

This Dataset is a merge of two EPL datasets I found online.

First, make sure to look up https://github.com/vaastav/Fantasy-Premier-League who has done an amazing job of collecting stats from the FPL app. There are further players' stats that I might share in the future. The second source is https://datahub.io/sports-data/english-premier-league, where some additional stats are available souch as referee name and betting odds (I kept 365 in the data, you might want to compare odds, etc)

Each row is a summary of a EPL game from one team's perspective. Among the stats you can find shots on target, xG Index, PPDA (measures pressing play) and more.

Notice: I added induvidual players stats. see the attached csv.

Acknowledgements:

As mentioned above, the collecting was done by others. Make sure you take a look and upvote the Github repo that is trully great.

So the EPL is currently shut down, we don't know when it'll be back. By that time, could you predict results? find trends?

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

Context

Barclay premier league is the best league in the world 💯 . It has 20 teams that qualified for the title. Among these 20 teams there are 5 teams which have already won the title in the last 12 seasons namely Man City, Liverpool, Man United, Chelsea, Leicester with two outsiders Arsenal and Tottenham. Who is your favorite team and how can you predict their title victory for the current or next season? The ball is in your camp 👀 .

Content

Notes for Football Data

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use and refer to data collected in earlier seasons. Each data contains last 12 seasons of English Premier League.

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards

I remove some features.

Acknowledgements

This dataset contains data for last 12 seasons of English Premier League. The dataset is sourced from http://www.football-data.co.uk/ website and contains various statistical data such as final and half time result, corners, yellow and red cards etc

Inspiration

Can you explain why Man United has not won the title for last 12 seasons?. Can you predict the victory of your favorite team in every championship game?.