So, I was trying to predict the rating of players in the FIFA21 game which is going to be released in the coming weeks by using their individual performance in the previous season and the rating in the previous edition of the game FIFA20. But, I couldn't find a dataset for this. So I had no option other than to scrape data from the PL website itself.

Each row in the datasets represents each player's performance in that particular season. It starts with Name, Position, Appearances, and the statistics of his performance throughout the season. Some entries are null because those attributes don't correspond to the position in which the player actually plays, for instance, a Forward will not have Number of saves; it doesn't make sense.

To all those football freaks like me, Feel free to use this dataset

Let me know if there's an error

English Premier League 2021-22 Match and Team Data

Fouls, Shots, Cards, and More!

By Evan Gower [source]

About this dataset

In the Premier League's 30th season, the top English professional league for association football clubs, Manchester City successfully defended their title on the final day of the season. This was also the club's fourth title in the last five seasons. The data for this season was collected from the official website of the Premier League and includes statistics for every game and every team. It is an interesting dataset for anyone looking to explore match day statistics, team data, and referee data for an entire season of play

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How to use the dataset

1. 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.

2. 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.

3. The data was collected from Match Reports https://www.premierleague.com/stats/top/players/goals?se=210

   Date:The date of

Research Ideas

• Predicting the outcome of future matches based on past performance
• Analyzing which teams are most successful at home or away games
• Identifying which teams are most likely to win the title based on their performance throughout the season

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: soccer21-22.csv | Column name | Description | |:--------------|:-----------------------------------------| | Date | The date of the match. (Date) | | HomeTeam | The home team. (String) | | AwayTeam | The away team. (String) | | FTHG | The full time home team goals. (Integer) | | FTAG | The full time away team goals. (Integer) | | FTR | The full time result. (String) | | HTHG | The half time home team goals. (Integer) | | HTAG | The half time away team goals. (Integer) | | HTR | The half time result. (String) | | Referee | The referee. (String) | | HS | The home team shots. (Integer) | | AS | The away team shots. (Integer) | | HST | The home team shots on target. (Integer) | | AST | The away team shots on target. (Integer) | | HF | The home team fouls. (Integer) | | AF | The away team fouls. (Integer) | | HC | The home team corners. (Integer) | | AC | The away team corners. (Integer) | | HY | The home team yellow cards. (Integer) | | AY | The away team yellow cards. (Integer) | | HR | Home team ranking. (Integer) | | AR | Away team ranking. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Evan Gower.

Liverpool Football Club's Matches (English League)

Manager Nationality, Games and Odds

By Ryan Estrellado [source]

About this dataset

This dataset reveals the history of Liverpool Football Club's success in the English League matches since 1892. Analyze this data to explore all aspects of their match performance, including their manager's experience, win percentage, and other statistics. Learn how each manager has contributed to the achievement of Liverpool FC in each season they have been at the helm while examining their nationality and tenure at Liverpool. See which managers helped Liverpool secure trophies such as FA Cup, League Cup, European Cup/Champions League and UEFA Super Cup triumphs over time. Study closely how changes in odds calculation were reflected in performances on field or understand how certain trends related to differences between away and home games' results also inform future decisions for any team aspiring to be a part of English football’s elite clubs without fail. Take advantage of this collection today and win more knowledge about the iconic successes that many legendary managers have brought forth from Anfield to delight fans from around world!

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How to use the dataset

This dataset contains the win ratios of Liverpool Football Club in English League matches for the period from 1892. It includes detailed information on each manager's role, tenure, win percentage and other metrics. This data can be used to provide key insights into the team’s performance and the strategies put in place by their managers throughout this time period.

Research Ideas

• Analyzing the tenure of a manager to identify the optimal amount of time for a successful reign. This can be done by plotting win percentages against the duration of their managerial term at Liverpool FC, with information about leagues and cups won or lost as further arguments for success or failure.
• Comparing win ratios from different eras in order to identify significant patterns and possible trends in the long-term history of LFC's performance on English League matches, taking into account factors such as changing legue rules and mentalities on football tactics over time
• Examining correlations between team performance, manager nationality and period in office, to assess whether there is any relationship between them that could help predict team outcomes in future matches based on these characteristics

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: wiki_lfc_mngrs.csv | Column name | Description | |:----------------|:------------------------------------------------------------------------------| | Name | Name of the manager. (String) | | Nationality | Nationality of the manager. (String) | | From | Start year of the manager's tenure. (Integer) | | To | End year of the manager's tenure. (Integer) | | P | Total number of matches played in the English League season. (Integer) | | W | Total number of wins in the English League season. (Integer) | | D | Total number of draws in the English League season. (Integer) | | Win % | Win percentage of the English League season. (Float) | | L1 | Total number of home wins in the English League season. (Integer) | | L2 | Total number of away wins in the English League season. (Integer) | | FA | Total number of FA Cup wins in the English League season. (Integer) | | **L...

Estimated models for free kick accuracy recorded as a percentage.

Context

I wanted to create a unique dataset which had not been made before. I was thinking about it for many days as to which data I should work upon because anything I was thinking was already there so I came up with this FIFA VAR dataset. I just wanted to keep it one hundred. VAR arrived in the Premier League in the season 19-20 and caused much controversy, with a total of 109 goals or incidents directly affected by the video ref.

What will the VAR review? - Goal/no goal - Penalty/no penalty - Direct red card (not second yellow card/caution) - Mistaken identity (when the referee cautions or sends off the wrong player)

What will it not review? - Any yellow card (including second yellow card leading to red) - Any free kick offence outside the box (other than red card offence)

Content

There are in all 2 files: 1. VAR_Incidents_Stats: This file contains the information about the various incidents that led to the inclusion of VAR in a football match. It contains 7 columns namely: - Team- Name of the team for which VAR was applied - Opponent Team - Name of the Opponent team for which VAR was applied - Date- The date when the match between team and opponent team was played - Site- Match was played Home(H) or Away(A) with respect to the Team - Incident- Description of the incident that led to the usage of VAR facility - Time- Time during the match when VAR was used or the incident happened - VAR used - VAR was used FOR or AGAINST with respect to the Team

1. VAR_Team_Stats: This file contains the statistics as to how VAR was used for a Team. It contains 10 columns namely:
2. Team - Name of the team for which VAR was applied
3. Overturns- How many times was the on field referees decision was overturned by the VAR
4. Leading to goals for - VAR Decisions that Lead to goal for the team
5. Disallowed goals for - VAR Decisions that Disallowed goals for the team
6. Leading to goals against - VAR Decisions that Leading to goals against the team
7. Disallowed goals against - VAR Decisions that Disallowed goals against the team
8. Net goal score - Total FOR and AGAINST VAR Decisions that led to a Goal
9. Subjective decisions for - VAR Subjective decisions for the team
10. Subjective decisions against - VAR Subjective decisions against the team
11. Net subjective score - Total FOR and AGAINST VAR Subjective Decisions

Acknowledgements

Data has been scraped from https://www.espn.in/football/english-premier-league/story/3929823/how-var-decisions-have-affected-every-premier-league-club.

Inspiration

• Has VAR been a good edition to the Premier League or Football in General?
• Has VAR been biased to any specific team?
• Is VAR improving with its decisions overtime?
• The name of the player on whom VAR has been used the most(FOR or AGAINST)?
• VAR has been used the most for which specific reason(Read cards, penalties, etc)?
• VAR has been used more on defenders, forwards, goalkeepers..?

Attitudes in the United Kingdom towards the EU. Topics: awareness of the 50th birthday of the EU; awareness of the charity exhibition football match; source of the information; assessment of the charity exhibition football match as part of the celebrations; assessment of benefits for British football from continental European football players and managers; assessment of the EU’s role in securing peace, prosperity and democracy for its member countries; assessment of the UK’s EU membership in terms of: travel, work, study, buy property, take holidays, transfer money; responsibility for benefits in the aforementioned fields: national government, local government, EU; assessment of the EU membership with regard to greater influence of the UK on the following issues: fight climate change, fight international terrorism, advance freer trade; estimated proportion of UK trade with the EU compared to the US (in percent); preferred political priorities for the EU; attitude towards indefinite extension of the UK’s use of imperial measures alongside their metric equivalents; preferred measurements to work with; awareness of the reduction of mobile phone roaming costs; attitude towards EU actions to tackle climate change; attitude towards more EU regulation with regard to the following issues: increase use of renewable energy, reduce traffic congestion, invest in other forms of transport, improve energy efficiency in domestic households; willingness to prevent climate change by supporting the following measures: wind turbines near residential area, reduce private car use, finance improved public transport, reduce personal carbon footprint, personal carbon-tax allowance. Demography: sex; age; age at end of education; occupation; professional position; region; type of community. Additionally coded was: respondent ID; interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; weighting factor.

Complete historical game data between Kentucky and South Carolina including scores, dates, locations, and game statistics.

Estimated models for corner accuracy recorded as a percentage.

Descriptive statistics for physical performance variables for substitutes from timing of pitch-entry to the end of match-play.