Comprehensive Football Player Statistics: 2023-2024 Season This dataset contains detailed player statistics from top football leagues for the 2023-2024 season. Sourced from FBref, the dataset includes a wide range of metrics covering various aspects of player performance, such as defense, goalkeeping, passing, and shooting.

Key Features Detailed Player Metrics: Statistics for individual players across multiple performance areas. Structured Data: Organized into tables focusing on different aspects of the game for easy analysis. Top Leagues: Includes data from prominent leagues that provide comprehensive detailed stats.

Github Repository link of the project : https://github.com/GuechtouliAnis/Football-Data-Scraping

By: Guechtouli Anis

Upload from https://github.com/fantasydatapros/data

Context

National Football League historic game and betting info

Content

National Football League (NFL) game results since 1966 with betting odds information since 1979. Dataset was created from a variety of sources including games and scores from a variety of public websites such as ESPN, NFL.com, and Pro Football Reference. Weather information is from NOAA data with NFLweather.com a good cross reference. Betting data was used from http://www.repole.com/sun4cast/data.html for 1978-2013 seasons. Pro-football-reference.com data was then cross referenced for betting lines and odds as well as weather data. From 2013 on betting data reflects lines available at sportsline.com.

Acknowledgements

Helpful sites with interest in football and sports betting include:

https://github.com/fivethirtyeight/nfl-elo-game

http://www.repole.com/sun4cast/data.html

https://www.pro-football-reference.com/

http://www.espn.com/nfl/

http://www.nflweather.com/

http://www.noaa.gov/weather

https://www.sportsline.com/

https://github.com/jp-wright/nfl_betting_market_analysis

http://www.aussportsbetting.com/data/historical-nfl-results-and-odds-data/

Inspiration

Can you build a predictive model to better predict NFL game outcomes and identify successful betting strategies?

Analysis of ‘NFL scores and betting data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tobycrabtree/nfl-scores-and-betting-data on 12 November 2021.

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

Context

National Football League historic game and betting info

Content

National Football League (NFL) game results since 1966 with betting odds information since 1979. Dataset was created from a variety of sources including games and scores from a variety of public websites such as ESPN, NFL.com, and Pro Football Reference. Weather information is from NOAA data with NFLweather.com a good cross reference. Betting data was used from http://www.repole.com/sun4cast/data.html for 1978-2013 seasons. Pro-football-reference.com data was then cross referenced for betting lines and odds as well as weather data. From 2013 on betting data reflects lines available at sportsline.com.

Acknowledgements

Helpful sites with interest in football and sports betting include:

https://github.com/fivethirtyeight/nfl-elo-game

http://www.repole.com/sun4cast/data.html

https://www.pro-football-reference.com/

http://www.espn.com/nfl/

http://www.nflweather.com/

http://www.noaa.gov/weather

https://www.sportsline.com/

https://github.com/jp-wright/nfl_betting_market_analysis

http://www.aussportsbetting.com/data/historical-nfl-results-and-odds-data/

Inspiration

Can you build a predictive model to better predict NFL game outcomes and identify successful betting strategies?

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

This dataset includes CSV files about players of games. We gathered the list of games from https://en.wikipedia.org/wiki/2020_Summer_Olympics. The data in the CSV files are gathered from the below resources:

http://www.adventurestats.com/tables/8000ergeo.shtmlhttp://www.bbc.com/news/magazine-28363129https://www.statcrunch.com/app/index.php?dataid=614855http://www.stadiumguide.com/figures-and-statistics/lists/europes-largest-football-stadiums/https://data.world/sports/olympics/file/outturn%20sports-related%20costs%20of%20the%20Olympic%20Games%20.csvhttp://www.bbc.com/news/magazine-28363129https://owlcation.com/stem/TopTenMountainRangeshttps://github.com/jokecamp/FootballData/blob/master/MLS/MLS%20-%202015/mls_players_2015-01-12.csvhttp://www.topendsports.com/events/summer/sports/number.htmhttp://newsok.sportsdirectinc.com/golf/pga-players.aspx?page=/data/pga/players/A_players.html

Data of football ovals in the City of Greater Geelong.\r \r Although all due care has been taken to ensure that these data are correct, no warranty is expressed or implied by the City of Greater Geelong in their use. Geographic Coordinate System: GCS_WGS_1984.

This dataset contains the location of Football (AFL) Ovals in the Surf Coast Shire. This material may be of assistance to you but the Surf Coast Shire does not guarantee that the publication is without flaw of any kind or is wholly appropriate for your particular purposes and therefore disclaims all liability for any error, loss or consequences which may arise from your relying on any information contained in this material.

Point data for location of sports grounds within Ballarat. This includes Athletic tracks, Baseball/Softball, Cricket Pitch, Cricket/Football, Equestrian, Football, Greyhound, Hockey, Soccer.\r \r Included attributes ID, Site, Location, Feature Type, Centroid, Measurement \r \r Although all due care has been taken to ensure that these data are correct, no warranty is expressed or implied by the City of Ballarat in their use.

Context

I had the need to collect Europe's top 5 leagues' dataset for my own undergraduate project. The idea was to eliminate human bias from the player scouting process.

More Details: https://github.com/Suwadith/Winning-Eleven-Scout-Evaluation-and-Analysis-to-Enhance-Football-Player-Recommendations-ML-Flask

Content

This dataset contains individual player statistics from Europe's top 5 leagues 2009 - 2018. Leagues included: La Liga, Bundesliga, Serie A, Ligue 1, Premier League Types of stats: Offensive, Defensive, Passing, Overall Summary

Acknowledgements

This dataset was compiled from the https://www.whoscored.com website

The goal of this data collection is to help assess how the Russian sport mega-events of the 2010s, the Sochi Olympics and the 2018 World Cup, are publicly remembered and what does this suggest about the outcomes of Russia's efforts to influence domestic and international audiences through such projects several years after their conclusion. The collection is comprised of three sets of research:(i) follow-up Interviews with Russian fans and media specialists; (ii) Twitter data related to memory of Sochi 2014 following the outbreak of the Russo-Ukraine war; and (iii) Twitter data related to memory of Sochi 2014 and Russia 2018 Football World Cup.My research investigates the nation projection activities of neo-authoritarian states, with a focus on contemporary Russia. The concept of 'nation projection' subsumes classic cultural diplomacy efforts and soft power activities such as the hosting of sport and entertainment events, including the Olympic Games. The term also refers to state-sponsored campaigns of external influence such as Russia's alleged media-driven meddling in the affairs of foreign states. As such, the nation projection activities of neo-authoritarian states are often depicted as 'propaganda' designed to conceal domestic political and economic problems on the one hand and exacerbate social and political tensions in democratic states on the other. The interface between nation projection and high-profile media events is fertile ground for the exploration of how meaning in the contemporary world is co-created by politicians, the media, and the publics. Yet neither the strategies nor the outcomes of the nation projection of illiberal states through media events, from sports tournaments to political campaigns and elections, have been understood adequately. Using contemporary Russia as a case study, my research better informs our understanding of the ways illiberal states communicate with international publics through media events. My work confronts the oversimplistic and outdated perceptions of such efforts as well-coordinated, malicious, and effective 'propaganda' that persist among political and media specialists and members of the public. My PhD thesis on Russia's nation projection through the Sochi 2014 Olympic Games and subsequent work on the research project "Reframing Russia" make an important contribution to addressing these misconceptions. The Fellowship will allow me to consolidate, disseminate, and ensure the impact of my investigation of the field to propose adequate solutions to this research problem. The PDF will also enable me to work with sets of interview and social media data regarding recent media events involving Russia, gathered during my doctoral and postdoctoral work, which I have yet to fully exploit. The Fellowship will enable me to communicate my research findings to a variety of stakeholders, and influence research, decision-making, and perceptions among relevant academics, political advisors and government departments, media specialists and other professional research users, as well as the general public. The outcomes of the Fellowship will prompt a re-evaluation and better understanding of the strategies of attraction employed by contemporary neo-authoritarian states through media events. I will challenge the notion that such events provide a universal template that neo-authoritarian political systems can easily utilise as soft power and regime legitimation tools. I will achieve this goal by writing a book based on my thesis and producing an academic journal article. My work will also include a policy-focused brief targeted for key policy-makers and advisors, and professional development and knowledge exchange visits to the International Olympic Committee's Olympic Studies Centre and the thinktank Play the Game. The Fellowship will also help me connect with researchers working on related topics and share my findings more widely among them. I will grow professionally under the mentorship of world-leading scholars Joseph McGonagle and Stephen Hutchings and have a chance to utilise their networks. The PDF will additionally allow me to develop a proposal for a new research programme on nation projection and its outcomes during media events co-hosted by democratic and neo-authoritarian European states, such as the Pan-European UEFA 2021 Football Championship. Overall, the Fellowship will be invaluable in my transition from doctoral student to emerging leader in the field of nation projection and media events. It will ensure the results of my work to date achieve maximum impact. The purpose of this data collection was to conduct follow-up interviews with the research participants I recruited and interviewed in 2018, and to conduct focused new social media research on the public memory of the Sochi 2014 and Russia 2018 several years after the tournaments’ conclusion. 1) The follow up interviews were conducted with research participants who have been previously recruited during my fieldwork in 2018 informing my PhD discussion. Four of the original interviewees took part in new interviews, transcription of which is collected here. Interviews were conducted as online audiocalls via encrypted platforms such as such as Signal and Telegram. Participants were either Russian sports fans, or Russian media professionals (see README for more details). A standard questionnaire was followed to conduct, record and transcribe the interviews. 2) Twitter data related to memory of Sochi 2014 following the outbreak of the Russo-Ukraine war: Using Academic Twitter API method, Twitter was search for messages containing the following terms and hashtags: “Sochi2014” “2014 Olympic” “Sochi Olympics” “Олимпиада 2014” “Сочи 2014” “Сочи2014”. The period of collection included February 20, 2022 to June 21, 2022. 3) Twitter data related to memory of Sochi 2014 and Russia 2018 Football World Cup. This sample of tweets was generated from a previous collection of Twitter messages related, conducted in 2018. The original collection was conducted by the University of Manchester Research IT team using the Twitter Streaming API method. Between June 15 and August 3, 2018, tweets matching keywords and hashtags below were collected, generating a collection of over 29 million tweets. More information on the original collection and methods can be obtained via UoM ResearchIT GitHub repository https://github.com/UoMResearchIT/twidata-code In 2023, the collection was searched to generate a new sample of messages relating to the public memory of Sochi 2014, and Russia 2018 tournaments. Tweets matching the following keywords in English and Russian were collected: “Memory” “memorable” “unforgettable” “vivid” “unforgettable” “indelible” “momentous” “historic” “immortal” “undying” “everlasting” “eternal” “unfading” “perpetual” “imperishable” “timeless” “remembering” “remember” “forgettable” “Память” “памятн” “незабвенн” “неизгладим” “знаменательн” “историческ” “бессмертн” “неумирающ” “вечн” “неувядаем” “нетленн” “вневременн” “запоминающ” “забываем” “припомин” “фиксиров” “помни”.

Centroïdes des stades de l'édition 2014 de la coupe du monde de football qui se déroule au Brésil du 12 juin au 13 juillet.

Origine

Récupération des données auprès de sites internets comme Open Football, sur OpenStreetMap ou d'après le site officiel de la FIFA.

Organisations partenaires

Isogeo Demo, Isogeo

Liens annexes

• Géométrie et objets (Esri Feature)
• L'événement sur le blog Isogeo
• Couche tuilée des pays du monde (WMTS)
• La vidéo de création des métadonnées
• Donnée tuilée (Esri Tile)

➞ Consulter cette fiche sur geo.data.gouv.fr

Brazilian Soccer Data

This repository consists of collecting the history and current data of all the most important competitions that Brazilian teams compete, the principal competitions are:

• Brasileirão(Brazilian soccer league)
• Libertatodes(Principal South america Competition)
• Sudamericana(South American secondary competition)
• Copa do Brasil(Brazilian Cup)

Next Steps: - structure the collection of the games of the sudamericana and copa do brasil - Gather data from the main state championships(SP, RJ, MG, RS) - Gather more data from these championships, such as match statistics

Any questions or suggestions are welcome, feel free to collaborate on the github repository

Context

This dataset was created because I wanted to analyze shooting tendencies in penalty shooting. It was built by watching every single penalty kick in a World Cup Shootout from Spain 1982 to Russia 2018.

Content

For each penalty I registered the following: - Kicking Team - Where the shot was taken. To do this, I divided the goal in 9 different zones. - Whether the shooter was left or right footed. - Where did the keeper dive. - Whether the shot was on target or not. - Whether the shot went in or not. - The penalty number in each series. - Whether or not the penalty was for elimination. This where the cases when, if the shot went in, the Game was over or the cases where a miss would end the game.

Notes

In the Zone column, I divided the goal into 9 different sections looking at it from the front: 1. Upper left corner 2. Upper center 3. Upper right corner 4. Middle left area 5. Middle center area 6. Middle right area 7. Bottom left corner 8. Bottom center zone 9. Bottom right corner

https://github.com/pablolopez2733/9plus6/blob/master/AnalisisPenaltis/Images/Goal.png?raw=true" alt="goal division">

For the Keeper column, looking at it from the shooter's perspective: I = the keeper dived to the left. C = the keeper kept in the middle of the goal. R = the keeper dived to the right.

https://github.com/pablolopez2733/9plus6/blob/master/AnalisisPenaltis/Images/Keeper.png?raw=true" alt="keeper dives">

I collected data from fifaindex.com for a thesis project. The dataset includes players and teams data from the EA Sports FIFA video game series, from FIFA12 to FIFA22. Some columns were dropped only from the team data (both teamyearly.csv and teamweekly.csv) due to inconsistent formatting used through the years.

The dataset only has data from the top five European leagues (Premier League, Serie A, Ligue 1, the Bundesliga, and La Liga), except for teamweekly.csv, which also has data from second divisions (of the same countries).

Scraper code: https://github.com/enricocattaneo/FIFA_WebScrapers

list of football clubs, auskick program, touch football and associations.