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

Clean data of the Premier League from Season 2010/2011 to Season 2019/2020

103 Data Points for every player for every season (2013 - 2020)

This data set contains 20 seasons of some English Premier League data points from 1997/1998 season up to 2017/2018 season. I intend to update this data with new seasons.

The data was gathered and cleaned using Microsoft Power Query ensuring the table is as slim as possible. So you will see there is only one single field for "Values" which is described by another field called "Attribute" such as goals scored, corners conceded, yellow cards etc. This makes it easy for analysis.

Analysis of ‘English Premier League Data for 10 Seasons’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/taranguyen/english-premier-league-data-for-10-seasons on 30 September 2021.

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

English Premier League Data

AUTHOR: TARA NGUYEN

Background

The English Premier League is top level of competition in English football (or soccer, as Americans like to call it). It is widely regarded as one of the most competitive and is one of the most watched sports competition in the world. Each season typically lasts from mid-August to mid-May (with the exception of the 2019/2020 season, which was postponed for three months due to COVID-19). Each season 20 teams compete for the Premier League trophy, as well as for the top four spots, because the top four teams are eligible for the next season of the Champions League (one of the most prestigious football tournaments not just in Europe but also in the world).

Datasets

The folder contains 46 datasets for ten seasons of the Premier League, from the 2010/2011 season to the 2019/2020 season. The 2020/2021 season was not included because it is an ongoing season.

Original Datasets

The original datasets came from https://www.football-data.co.uk/englandm.php. Ten datasets (one for each season) were imported, each containing match statistics and betting odds for each game in one season.

Data Cleaning and Wrangling

All steps of data cleaning and wrangling were done entirely in R. The original ten datasets were cleaned, transformed, and merged into one big dataset (epl-allseasons-matchstats.csv) containing the following information: - Season - Date - Referee - Home teams and away teams - Results at full time and at half-time - Number of goals scored by the home team at full time and that at half-time - Number of goals scored by the away team at full time and that at half-time - Number of: shots, shots on target, corner kicks, fouls committed, yellow cards received, and red cards received. Each of these pieces of information is available for both the home team and the away team.

Additional datasets

Forty-five other datasets were created based on the epl-allseasons-matchstats.csv dataset. They include: - 10 season-end league tables, one for each season covered by the data (filenames ending in leaguetable.csv); - 10 datasets (one for each season) containing the result and number of points each team got after each game/match (filenames ending in matchday-results-pts.csv); - 10 datasets (one for each season) containing the number of goals scored and number of goals conceded by each team after each game/match (filenames ending in matchday-goals-shots.csv); and - 15 datasets containing the head-to-head match statistics across all ten seasons (filenames beginning with epl-allseasons-head2head-).

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

Analysis of ‘All Time Premier League Player Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rishikeshkanabar/premier-league-player-statistics-updated-daily on 30 September 2021.

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

Context

I am a really huge football fan and the Premier League is one of my favourite football (or soccer, whatever you like to call it) leagues. So, as my very first dataset, I thought this would be a great opportunity for me to make a dataset of player statistics of all seasons from the Premier League.

The Premier League, often referred to as the English Premier League or the EPL outside England, is the top level of the English football league system. Contested by 20 clubs, it operates on a system of promotion and relegation with the English Football League (EFL). Contested by 20 clubs, it operates on a system of promotion and relegation with the English Football League.

Home to some of the most famous clubs, players, managers and stadiums in world football, the Premier League is the most-watched league on the planet with one billion homes watching the action in 188 countries.The league takes place between August and May and involves the teams playing each other home and away across the season, a total of 380 matches.

Three points are awarded for a win, one point for a draw and none for a defeat, with the team with the most points at the end of the season winning the Premier League title. The teams that finish in the bottom three of the league table at the end of the campaign are relegated to the Championship, the second tier of English football. Those teams are replaced by three clubs promoted from the Championship; the sides that finish in first and second place and the third via the end-of-season playoffs.

Details about the dataset

• Some players of certain position may not have certain statistics - For example, A goalkeeper may not have a statistic for "Shot Accuracy"
• The format for the filename is - dataset - {yyyy-mm-dd Date} (The date is date when the file was last updated on)

Content

The data was acquired from:

https://www.premierleague.com/

I made a BeautifulSoup4 Web Scrapper in Python3 which automatically outputs a csv file of all the player statistics. The runtime of the file is about 20 minutes but it varies with the bandwidth of the Internet connection. I made this program so that this dataset could be updated weekly. The reason for weekly update is that the statistics change after each match played by the player so I felt that for the most up-to-date results, such a program is needed. Planning this project took 2 days. Making the program in Python3 took 7 days and the testing and bug fixing took another 5 days. The project was completed in the span of 2 weeks.

Acknowledgements

Source credits : https://www.premierleague.com/ Image credits : https://rb.gy/wuiwth

Inspiration

How do variables like age, nationality and club affect the player performance?

Known issues in the dataset

• Goals per match displays an abnormally high value for a few players as the HTML displays incorrect value during first few milliseconds of loading the page. I am trying to fix it analytically rather than scrapping directly from the website.

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

Analysis of ‘Premier League Player Stats Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/themlphdstudent/premier-league-player-stats-data on 30 September 2021.

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

Context

Data set for people who love Football and Data Science. Scraping code at GitHub repo: https://github.com/themlphdstudent/kaggle/blob/master/datasets/Premier%20League%20Player%20Stats/Premier%20League%20Player%20Stats.ipynb

Content

• Rank : Rank of the player
• Player : Player name
• Team : Player team name
• GP : Games played
• GS : Games started
• MIN : Minutes played
• G : Goals
• ASST : Assists
• SHOTS : Total shots
• SOG : Shots on goal

Data Source

The data is scraped from the website https://www.msn.com/en-us/sports/soccer/premier-league/player-stats by extracting the player stats in premier league.

Acknowledgements

The data has been crawled from the https://www.msn.com/en-us/sports/soccer/premier-league/player-stats website. Cover photo credit : Photo by Fachry Zella Devandra on Unsplash.

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

This dataset contains start & end timestamps for League of Legends games played via the Duowan plugin. We randomly sampled 100k user_ids and gathered all games associated with those users.

The statistic presents the share of League of Legends fans in the United States in 2018, by age group. According to the estimates, teenagers aged 13 to 17 accounted for 12 percent of U.S. League of Legends fans.

Released in October 2020, League of Legends: Wild Rift is a mobile MOBA (multiplayer online battle arena) gaming title which was developed and published by Riot Games. The title is a mobile version of the MOBA genre classic League of Legends and generated approximately 970 thousand app downloads in December 2023.

Analysis of ‘Premier League Scoring Data 2020 2021 Season’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/crained/premier-league-scoring-data-2020-2021-season on 30 September 2021.

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

This is the Premier League dataset that is connected to a notebook which scrapes data and appends to a CSV file. https://www.kaggle.com/crained/premier-league-20-21-pandas-2-csv

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

Box Scores from the 2011-2012 season in Excel format. You can get these in properly normalized SQL format from the relational learning database repository: https://relational.fit.cvut.cz/dataset/PremiereLeague

Content type of data is soccer box scores/aggregate data.

This dataset was originally deposited in the Simon Fraser University institutional repository.

This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier. The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided. This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.

Premier League football Soccer for 2020 2021 top scorers

Indian Premier League Dataset Overview

Dataset includes 21 Tables as mentioned below.

1. Ball_by_Ball

2. Batsman_Scored

3. Batting_Style

4. Bowling_Style

5. City

6. Country

7. Extra_Runs

8. Extra_Type

9. Match

10. Out_Type

11. Outcome

12. Player

13.Player_Match

14. Rolee

15. Season

16. Team

17. Toss_Decision

18.Umpire

19.Venue

20.Wicket_Taken

21. Win_By

Analysis of ‘League of Legends - Champions & Items' Stats’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/barthetur/league-of-legends-champions-items-stats on 30 September 2021.

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

Context

To develop my skills in data analysis I work on eSports Data such as basic statistics of League of Legends. To be able to get information about the team I use the open data available on League of Legends Wiki and official website too.

Content

This dataset gives all the details of Champion Statistic at level 1 and 18 but also the growth factor of each characteristics. In the future, dataset on items and masteries will be added.

Acknowledgements

Those data will be updated with the upcoming patch.

Inspiration

Easy work we can do with this : Calculation and determination of spikes, damge mix for draft, etc. Enjoy !

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

League of Legends (LOL) is the most popular game on PC, drawing 8 million concurrent players. A common activity of gamers, besides playing games, is to watch other players presenting tips and tricks. Streaming platforms allow some players to show gameplays and live games. Twitch.tv is the world´s leading live streaming platform.

Considering that hate speech is a ubiquitous problem in online gaming, we collected 985,766 comments from five videos of the top 10 LOL streamers in Twitch.tv platform.

The dataset is freely available in a single file, ensembling all videos/players; and divided by players as well.

These comments are a rich data source for opinion mining, sentiment analysis, topic modeling, and hate speech detection (including sexism and racism).

This project contains various connected data for all the seasons of Hero Indian Super League till last year. The data has been scraped using R from the websites listed below: * All_Transfers.csv - transfermarkt * Other files - worldfootball

Summary of tables available/to be added

• Season_Teams: Teams in each season with team code and flag for whether they made the playoffs or not
• Teams_Profile: Details of each team like date founded, home ground, homepage etc.
• Teams_Manager_History: History of team managers per team
• All_Matches: Summary of individual Matches in each season
• Match_Info: Further details about each match like referee, stadium, attendance
• Match_Report: Detailed list of players & substitutes in each match along with substitution time, card time, goal time
• Player_Bio: Biographic details about each player like DOB, height, playing position
• All_Transfers: All transfers per season with available values
• Match_Attendance: To be added
• Referees_Assistants: To be added

* Player_career: To be added

Next Steps

• [ ] Enhance tables above by adding more information where available
• [ ] Add new tables as listed above (and others if any)

Analysis of ‘Premier League’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zaeemnalla/premier-league on 12 November 2021.

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

Context

Official football data organised and formatted in csv files ready for download is quite hard to come by. Stats providers are hesitant to release their data to anyone and everyone, even if it's for academic purposes. That was my exact dilemma which prompted me to scrape and extract it myself. Now that it's at your disposal, have fun with it.

Content

The data was acquired from the Premier League website and is representative of seasons 2006/2007 to 2017/2018. Visit both sets to get a detailed description of what each entails.

Inspiration

Use it to the best of your ability to predict match outcomes or for a thorough data analysis to uncover some intriguing insights. Be safe and only use this dataset for personal projects. If you'd like to use this type of data for a commercial project, contact Opta to access it through their API instead.

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

This statistic illustrates the number of League of Legends (LoL) monthly active users worldwide from 2011 to 2016. In 2016, LoL had 100 million MAU, up from 90 million in 2015. Being one of the most prominent eSports games, in 2015 LoL championship finals attracted 36 million viewers worldwide. What is more, with the rise of gaming video content popularity, video games enthusiasts can now watch other players try their chances in the game, from the comfort of their own home. And so, League of Legends was one of the top games on Twitch, based on the number of hours viewed in 2016. It comes a no surprise that LoL held the highest share of the MOBA games revenues that year.