Major US Sports Venues Usage and Affiliations

Team, League, Conference and Population Usage Records

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset provides detailed information on major sport venues, along with their usage and affiliations. It includes data related to the National Association for Stock Car Auto Racing, Indy Racing League, Major League Soccer, Major League Baseball, National Basketball Association, Women's National Basketball Association, National Hockey League, National Football League, PGA Tour, NCAA Division 1 FBS Football, NCAA Division 1 Basketball and thoroughbred horse racing.* This dataset contains columns such as USE (which describes the type of use for the venue), TEAM (the team associated with the venue), LEAGUE (the league associated with the venue) , CONFERENCE (the conference associated with the venue), DIVISION (the division associated with the venue), INST_AFFIL(the institution affiliation associatedwith the venue), TRACK_TYPE(type of track at a specific point in time or over its complete life-cycle) as well as LENGTH_MILEGE ('length of track in milege') ROOF_TYPE(The type of roof covering used at a specific point in time or over its complete life-cycle) and plenty other variables. With this astounding range and quantity of data points -- spanning countries across different continents and leagues -- explore patterns in sports games you never even thought were possible!

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How to use the dataset

The MajorUS Sports Venues Usage and Affiliations dataset includes data on major sports venues from leagues including National Association for Stock Car Auto Racing (NASCAR), Indy Racing League (IRL), Major League Soccer (MLS), Major League Baseball (MLB), National Basketball Association (NBA), Women's National Basketball Association (WNBA), National Hockey League (NHL), National Football League(NFL), PGA Tour, NCAA Division 1 FBS Football, NCAA Division 1 Basketball, and thoroughbred horse racing. The columns provided include USE_, USE_POP, TEAM, LEAGUE,CONFERENCE,DIVISION ,INST_AFFIL,TRACK_TYPE. LENGTH_MI,ROOF_TYPESTADIUM_SH,`ADDDATAE , USEWEBSITE',and'COMMENTS'.

The `USE~ column specifies the type of usage of each venue at which point can be college athletics or professional athletics. The corresponding column to this is the ‘USE~POP’ which informs you about how many people are using each venue for a particular sport at a given time. For example if there were 6 NHL games being played that day then USE~ would say “professional Athletics” while USE~POP would state “NNN” reflecting there were NNN people spectating those events collectively: The next column is TEAM which represents what team sponsors or manages each venue or what teams will be playing in them.

 Following on from TEAM is LEAGUE; here you can find out what league each team represents such as MLB, NBA etc… The next three columns CONFERENCE/DIVISION/INST ~ AFFIL provide more specific details as they blur into collegiate level as well where CONFERENCE indicates which conference they belong within their respective division: while INST ~ AFFIL states its affiliated school body e.g.: Southeastern Conference > University of Arkansas Razorbacks . Rounding up our overview these last three columns TRACK ~ TYPE/LENGTH

Research Ideas

• Analyzing the affiliations and usage of different sports venues to determine which teams or leagues have the most presence across a certain geographic area.
• Comparing different stadiums within a given conference in terms of their roof type, track length, and stadium shape for optimal design features for new construction projects.
• Placing sponsorships or advertisements within each sporting arena based on audience size, league popularity, and team affiliation within a given conference or division

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 contribut...

This dataset gives a game-by-game attendance to every NCAA FBS game from 2001 to today. Big thanks to the SportsDataVerse whose cfbfastR package was used to get a majority of this data. NCAA Statistics was used to get current year attendance data.

Here are a few use cases for this project:

1. Player Performance Analysis: The "basketball" model can be utilized in sports analysis to evaluate player performance by identifying player movements, ball handling, shooting angles, shot success rate considering the rim, etc.

2. Augmented Reality Games: It could be used in the development of augmented reality (AR) sports games where real-world gestures and actions are mimicked in the game setting. The model can identify the person, ball, and rim to integrate these elements in the gameplay.

3. Sports Broadcasting Enhancement: The model can enhance the viewing experience by providing advanced tracking statistics in live broadcasts or highlights, such as identifying key moments where the person, ball, and rim interacted in significant ways.

4. Training and Coaching: It can be used to analyze training exercises and provide feedback. It can identify incorrect techniques or recommend improvements based on the data it gathers about the person's interaction with the ball and the rim.

5. Surveillance and Security in Sports Facilities: When installed in sports facilities, the model can help in identifying if the property is being used for its intended purpose. For example, if only people and the basketball are present but no interaction with the rim, it could suggest irregular activities.

The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

Due to the closure of school sites during the coronavirus pandemic, the Active Lives Children and Young People survey was adapted to allow at-home completion. This approach was retained into the academic year 2022-23 to help maximise response numbers. The at-home completion approach was actively offered for secondary school pupils, and allowed but not encouraged for primary pupils.

The adaptions involved minor questionnaire changes (e.g., to ensure the wording was appropriate for those not attending school and enabling completion at home) and communication changes. For further details on the survey changes, please see the accompanying User Guide document. Academic years 2020-21, 2021-22 and 2022-23 saw a more even split of responses by term across the year, compared to 2019-20, which had a reduced proportion of summer term responses due to the disruption caused by Covid-19.

The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets have been provided:

1) Main dataset: this file includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels; they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross - Csplan files are available for SPSS users who can utilise them).

2) Year 1-2 dataset: This file includes responses directly from children in school years 1-2, providing their attitudinal responses (e.g., whether they like playing sport and find it easy). Analysis can also be carried out into feelings towards swimming, enjoyment of being active, happiness, etc. Weighting is required when using this dataset (wt_gross / wt_gross - Csplan files are available for SPSS users who can utilise them).

3) Teacher dataset: This file includes responses from the teachers at schools selected for the survey. Analysis can be carried out to determine school facilities available, the length of PE lessons, whether swimming lessons are offered, etc. Since December 2023, Sport England has provided weighting for the teacher data (‘wt_teacher’ weighting variable).

For further information, please read the supporting documentation before using the datasets.

Abstract copyright UK Data Service and data collection copyright owner.

The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

The Active Lives Children and Young People Survey, 2017-2018 commenced during school academic year 2017 / 2018. It ran from autumn term 2017 to summer term 2018 and excludes school holidays. The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets are available:

1) Main dataset includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child's activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_set1.csplan).

2) Year 1-2 pupil dataset includes responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_set1.csplan).

3) Teacher dataset includes responses from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable.

For further information about the variables available for analysis, and the relevant school years asked survey questions, please see the supporting documentation. Please read the documentation before using the datasets.

Latest edition information

For the second edition (January 2024), the Teacher dataset now includes a weighting variable (‘wt_teacher’). Previously, weighting was not available for these data.

Main Topics:

Topics covered in the Active Lives Children and Young People Survey include:

• Sport and physical activity participation
• Well-being
• Health

History

Chess is very popular nowadays, there are more than 600 million people in the world who play it. Everyone has heard of them at least once. But how difficult is it to become a professional player? This dataset features amateur and professional players who have ever played at the international level. Yes, yes, Magnus Carlsen and Garry Kasparov are also here.

Try

I think you are also interested to know how the best players differ from amateurs, which country has the most professional players and who is the youngest player now? Analyze and visualize the data, maybe you will find the secret of this wonderful game.

Here are a few use cases for this project:

1. Sports Analytics: The "ball_handler" model could be implemented by basketball teams or broadcasters to accurately analyze the gameplay. It can help in determining crucial statistics such as ball possession time, player positioning on the court, and shooting accuracy based on rim detection.

2. Player Training and Performance Improvement: Coaches could use this model to analyze individual player's skills, such as ball-handling ability, court presence, and shooting style. This can then facilitate personalized training regimes to enhance their performance.

3. Video Game Development: This model could be useful in creating realistic basketball video games. It can help game developers create AI-powered characters that mimic real-world player movements, ball-handling dynamics and can even detect the rim for accurate shooting simulations.

4. Augmented Reality (AR) Sports Apps: "ball_handler" model could be used to build AR applications that provide interactive basketball training sessions. Users can practice ball-handling against virtual players or improve their shooting accuracy with rim detection feature.

5. Surveillance and Security: Beyond sports, this model could be used for surveillance purposes, particularly in public sports facilities. It can identify people, detect unusual movements (such as someone lying on the floor when they shouldn't be), and potentially provide alerts in real-time.

Have you ever found yourself with a football dataset that almost had it all, but left you short of happiness? Time after time, promising datasets failed to deliver the statistics that truly matter – match events, player performances, team results, and season standings.

That time is over!

This in-depth football dataset, curated straight from a RapidAPI endpoint, brings you the data points we've all been waiting for. From fixtures and injuries to goals, assists, and tactical breakdowns, this dataset unlocks the full picture of the beautiful game.

What You Get 🏆 - Fixture Stats & Events: Goals, assists, fouls, and match-defining moments across leagues up to 2024. - Player Performances: From tackles to dribbles, passes, and shots – every stat that makes a difference. - Season Stats & League Standings: Discover how teams dominate, stumble, or rise to glory each season. - Team Insights: Analyze home/away performance, goal-scoring patterns, and defensive strengths. - Match Highlights: Real-time events like own goals, red cards, and critical substitutions. - Injuries & Suspensions: Missing players and their impact on team dynamics. - Iconic Stadiums: Explore venues, capacities, and surfaces that set the stage for football's greatest moments.

Why It’s Exciting 🌟

This isn’t just another football dataset – it’s the ultimate resource for fans, analysts, and strategists who want to dig deeper. Whether you're predicting outcomes, analyzing player form, or crafting the next big football insights project, you now have all the tools you need.

Get ready to unlock stories, trends, and insights like never before – because this time, the stats you actually care about are all here. Let’s kick it off! ⚽✨

In terms of fixture stats for players, the endpoint provides data from 2015 up through the 2024 season and I plan to make one more update at the end of all league/cup seasons in June of 2025.

Disclaimer: This dataset is intended for non-commercial, academic purposes and does not infringe upon any intellectual property rights of the original data providers, including RapidAPI or associated sources. For full details, please refer to the respective terms of use provided by the data sources.

If you have questions about the data or simply want to connect, reach out on LinkedIn and if you plan on using this data for any type of analysis, can you please share that with me!

PS: I am a Ronaldo fan... Suiiiii !!!

Leagues/Cups in datasets: - La Liga - Ligue 1 - Serie A - World Cup - Bundesliga - NWSL Women - Pro League - Championship League - Copa America - Premier League - CONCACAF Gold Cup - Euro Championship - UEFA Europa League - MLS - Africa Cup Of Nations - CONCACAF Champions League

Other Datasets: - Spotify - Zillow

The dataset for people who double on Fifa and Data Science

Content

• 17,000+ players
• 50+ attributes per player ranging from ball skills aggression etc.
• Player's attributes sourced from EA Sports' FIFA video game series, including the weekly updates
• Players from all around the globe
• URLs to their homepage
• Club logos
• Player images male and female
• National and club team data

Weekly Updates would include :

• Real life data (Match events etc.)
• The fifa generated player dataset
• Betting odds
• Growth

https://data2.origin.com/live/content/dam/originx/web/app/games/fifa/fifa-17/screenshots/fifa-17/PogbaDab_pdp_screenhi_3840x2160_en_ww.jpg" alt="">

Data Source

Data was scraped from https://www.fifaindex.com/ first by getting player profile url set (as stored in PlayerNames.csv) and then scraping the individual pages for their attributes

Improvements

• You may have noticed that for a lot of players, their national details are absent (Team and kit number) even though the nationality is listed. This may be attributed to the missing data on fifa sites.

GITHUB PROJECT

• There is much more than just 50 attributes by which fifa decides what happens to players over time, how they perform under pressure, how they grow etc. This data obviously would be well hidden by the organisation and thus would be tough to find

Important note for people interested in using the scraping: The site is not uniform and thus the scraping script requires considering a lot of corner cases (i.e. interchanged position of different attributes). Also the script contains proxy preferences which may be removed if not required.

Exploring the data

For starters you can become a scout:

• Create attribute dependent or overall best teams
• Create the fastest/slowest teams
• See which areas of the world provide which attributes (like Africa : Stamina, Pace)
• See which players are the best at each position
• See which outfield players can play a better role at some other position
• See which youngsters have attributes which can be developed

And that is just the beginning. This is the playground.. literally!

Data description

• The file FullData.csv contains attributes describing the in game play style and also some of the real statistics such as Nationality etc.
• The file PlayerNames.csv contains URLs for different players from their profiles on fifaindex.com. Append the URLs after the base url fifaindex.com.
• The compressed file Pictures.zip contains pictures for top 1000 players in Fifa 17.
• The compressed file Pictures_f.zip contains pictures for top 139 female players in Fifa 17.
• The compressed file ClubPictures.zip contains pictures for emblems of some major clubs in Fifa 17.

Inspiration

I am a huge FIFA fanatic. While playing career mode I realised that I picked great young players early on every single time and since a lot of digital learning relies on how our brain works, I thought scouting great qualities in players would be something that can be worked on. Since then I started working on scraping the website and here is the data. I hope we can build something on it.

https://www.xzone.cz/download/products/fifa-17-01.jpg" alt="">

With access to players attributes you can become the best scout in the world. Go for it!

Comprehensive ranking dataset of the top 100 YouTube channels in the Sports category. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 61,800,000 subscribers and 19,881,408,517 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

Athletic Skill Demand Ranking for 60 Sports

An ESPN Expert-Rated Comparison

By Corey Hermanson [source]

About this dataset

Welcome to the Toughest Sport dataset! Here we are bringing you a complete breakdown of 60 sports and their demands for 10 distinct skills that make up athletic capabilities. We collected data from 8 expert panelists and asked them to rate each sport on a scale of 1-10 for every skill ranging from Strength and Speed to Nerve, Hand-eye Coordination, and more. By totalling up the opinions of our experts, we have created an overall degree of difficulty score for each sport in the dataset between 1-100. If you're curious as to which sports require what skill sets, or if you're wondering which is the toughest sport across all ten skillsets - this is your place! Get ready to explore how athleticism guides our understanding of what makes 'Toughest Sport'!

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How to use the dataset

This dataset contains the rankings of 60 sports across 10 skills, as well as the total score and rank of each sport. It is intended to provide an overview of the relative athleticism required across a variety of different competitive sports and can be used to identify which physical attributes are most important in each sport.

To use this dataset, you will need to understand what the different sports measured by this data set represent. Sports like skiing, boxing, wrestling, and football all require very different mental and physical abilities in order to compete successfully. For example, Alpine Skiing will require greater skill related to speed, agility and power than Cross Country running does; while Swimming may rely more heavily on durability than Football does. Once familiar with the included sports then it becomes easier to utilize the scores assigned for each skill in order identify which skills might benefit a particular athlete most when considering a new athletic challenge.

The dataset also provides useful information about how difficult it might be for any one individual athlete or competitor if they were looking at taking up one particular sport from scratch versus another with similar momentum when compared in terms of its overall scores across 10 areas relating specifically as they relate athletics. This could help indicate whether that athlete has a better chance or worse chance when competing against others who may have trained in or specialised within their chosen field longer or shorter than themselves respectively before stepping onto this same playing field together; simply by comparing total Athletics Skill Demand (ASD) Numbers over between both their desired sporting choices (the higher number representing higher difficulty).

Research Ideas

• Identifying the most requested athletic traits by sport. By analyzing the data, one can uncover patterns within certain sports that require certain skills or abilities more than others.
• Determining which sports offer the best opportunity for balance and development of all skillsets by athletes. Specifically, this dataset could be used to identify which sports encourage holistic athletic development and what combination of skill demands those particular sports have in common.
• Developing an optimal training program for athletes interested in perfecting their craft in a particular sport by geotargeting regions with more advanced competition based on their specific skill needs relative to that region’s average competition level as demonstrated by this dataset

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: toughestsport.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | SPORT | Name of the sport. (String) | | END | Endurance score for the sport. (Integer) | | STR | Strength score for the sport. (Integer) | | PWR | Power score for the sport. (Integer) | | SPD | Speed score for the sport. (Integer) | | AGI | Agility score for the sport. (Integer) | | FLX | Flexibility score for the sport. (Integer) | | NER | Nerve score for the sport. (Integer) | | DUR | Durability score ...

This dataset contains data and statistics for some of the greatest players who have played in the National Basketball Association (NBA). You can use these stats to assess for various aspects for these players - and maybe even find out who is the all-time GOAT of basketball.

Explaining some statistics to people unfamiliar to Basketball (Assuming points, assists etc. are obvious)

PER - Player Efficiency Rating - The player efficiency rating (PER) is John Hollinger's all-in-one basketball rating, which attempts to collect or boil down all of a player's contributions into one number. Using a detailed formula, Hollinger developed a system that rates every player's statistical performance.

EWA - Estimated Wins Added - EWA is similar to PER where it boils down all player contributions into 1 statistic. But it is used in a way to show how many wins are added to a team when that certain player plays on the court

WS & WS/48 - Win shares & Win shares per 48 - Win Share is a measure that is assigned to players based on their offense, defense, and playing time. WS/48 is win shares per 48 minutes and invented by Justin Kubatko who explains: “A win share is worth one-third of a team win. If a team wins 60 games, there are 180 'Win Shares' to distribute among the players.”

Overview

Welcome back to the ‘Weekend Hackathon Edition 2- The Last Hacker Standing’ at Machine Hack. In this edition, we will be posing unique problem statements every week, which will test you over various aspects of being a Data Scientist. The Weekend Edition will be held for a 6 week period starting 30 July 2021 to 9 Sept 2021.

This time it is dedicated to passion and fervour which a sport creates. Challenge Name: THE SOCCER FEVER

Introduction

Soccer aka Football is the most popular game in the world. It’s a religion of its own. If groups of 10 people can stop time and make people watch them in awe and reverence, it’s this beautiful game. Also, anybody can play soccer- all it needs is 4 poles, a ground and a ball. You can just get started with the play.

In fact, Nelson Mandela very effectively used Football as the unifying factor when he was elected President of South Africa post the Apartheid era. The sport just cuts across all discriminating factors.

Relevance

An entire ecosystem revolves around this beautiful sport. Clubs, Merchandise, listed football clubs, fan clubs and a group of rivals who can just get into a fight based on the outcome of the game. The amount of currency involved in this game is just phenomenal. It impacts millions of people who depend on it for their livelihood and recreation. Criticality

We live in ambiguity and always need some information to just make a decision. Decisions are made based on possible outcomes. Win/ Loss/ Pass / Fail etc.

The below problem statement is a classic study for decision-making and understanding the odds stacked against a particular situation.

Train

Dataset: 7443*21
Columns: 21
Target Column: Outcome

Evaluation Metric: Log Loss

Test

Dataset: 4008*20
Columns: 20

Submission Format :

Dataset: 4008*1( Column Name - ‘Outcome’)

Skills

Multi-Class Classification
Optimizing Log Loss

This dataset contains comprehensive historical information about the FIFA World Cup, the premier international football (soccer) tournament organized by FIFA (Fédération International de Football Association). The dataset spans multiple decades and covers various aspects of the tournament, including match results, player statistics, team details, and other relevant information related to the tournament.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13571604%2Fce4d4e613ed35074326ddc5537f50381%2FScreenshot%202023-07-30%20195415.png?generation=1690771057695850&alt=media" alt="">

Short Track Speed Skating Database for Sports Data Analysis

What is short track speed skating?

Maybe some people have never heard of this sport. Short track is a competitive and strategic game in which skaters race on ice. Sometimes the smartest or the luckiest guy, rather than the strongest, wins the game (for example).

What's in the data?

The database covers all the international short track games in the last 5 years. Currently it contains only men's 500m, but I will keep updating it.

• Detailed lap data including personal time and ranking in each game from seasons 2012/2013 to present .
• The final time results, ranking, starting position, qualified or penalized information of each athlete in each game.
• All series of World Cup, World Championship, European Championship and Olympic Games.

Original data source

The data is collected from the ISU's (International Skating Union) official website. I have already done the cleaning procedure.

Please make sure that the data are only for personal and non-commercial use.

Explore the data

Interesting questions may be like:

• What will happen in a game when there are more than one athlete from the same team? Are there performance all improved?
• How does the performance of athletes change within a season and over seasons?
• Do some athletes have special patterns in terms of time allocation and surpassing opportunity?
• What is the influence of the implementation of "no toe starts" rules on athletes since July 2015?
• Is there also home advantage like in other sports?
• Who are the most "dangerous" guys that always get penalty?

The Olympic Games are an international multi-sport event held every four years in which thousands of athletes from around the world participate in various sports competitions. The Olympics are one of the most significant and prestigious sporting events globally, promoting unity, friendship, and fair play among nations.

Key facts about the Olympic Games:

History: The modern Olympic Games were inspired by the ancient Olympic Games held in Olympia, Greece, from the 8th century BCE to the 4th century CE. The modern Olympics were revived in 1896 by Pierre de Coubertin, a French educator and historian.

Summer and Winter Games: The Olympics are divided into the Summer Olympic Games and the Winter Olympic Games. The Summer Games typically include sports such as athletics, swimming, gymnastics, and team sports, while the Winter Games feature events like skiing, ice hockey, snowboarding, and figure skating.

Host Cities: Each Olympic Games is hosted by a selected city from around the world. The host city is chosen through a competitive bidding process organized by the International Olympic Committee (IOC).

Olympic Rings: The iconic symbol of the Olympic Games is the five interlocking rings, representing the five continents (Africa, the Americas, Asia, Europe, and Oceania). The colors of the rings (blue, yellow, black, green, and red) were chosen because every nation's flag contains at least one of these colors.

Olympic Motto: The Olympic motto is "Citius, Altius, Fortius," which is Latin for "Faster, Higher, Stronger." It represents the athletes' pursuit of excellence and improvement.

Olympic Flame: The Olympic Flame is lit in Olympia, Greece, several months before the start of the Games. It is then carried by a relay of runners to the host city, where it ignites the cauldron during the opening ceremony.

Participation: The Olympics are open to all National Olympic Committees (NOCs) recognized by the IOC. Athletes must meet specific qualifying criteria to compete in the Games.

Olympic Medals: Gold, silver, and bronze medals are awarded to the top three athletes or teams in each event.

Olympic Values: The Olympic Games promote values such as respect, friendship, fair play, excellence, and solidarity, aiming to foster peaceful coexistence and understanding among nations.

Paralympic Games: The Paralympic Games, also held every four years, are a parallel multi-sport event for athletes with physical, intellectual, or visual impairments.

The Olympic Games are a celebration of sport, culture, and international cooperation, bringing people together from diverse backgrounds to share in the spirit of competition and sportsmanship.

Athletic Achievements of Adaptive Sports Athletes from 2000-2016

Success in International Events and UK-based Paralympic Spending

By DiversityinData [source]

About this dataset

At #DiversityInData, #ProjectHealthViz, and #SportsVizSunday, we have partnered up to create a dataset focusing on adaptive sports athletes with the goal of raising awareness of their incredible achievements. We aim to promote adaptive sports programs that provide a safe and supportive environment for people with disabilities who would otherwise not get access to physical activity.

These adaptive sports such as biking, golfing, skiing and snowboarding give participants both physical and mental benefits. We hope this dataset motivates others to join in on the diverse range of these activities! With this dataset on Adaptive Sports Athletes including marathon wheelchair results from the Boston, Chicago, London & New York Marathons; historical information from the Paralympics; UK Paralympic spending & results for UK athletes - you will be able to visualize the amazing feats accomplished by adaptive sports athletes around the world. We look forward to seeing your visualizations that display different perspectives of this valuable data! Be sure tag all three projects on Twitter (DiversityInData/ProjectHealthViz/SportsVizSunday) and let us know if you have any questions!

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How to use the dataset

This dataset contains records of the athletic achievements of adaptive sports athletes from 2000 to 2016. It includes athlete, gender, nationality and sport information, as well as details about medals won and race times recorded.

The data is organized in columns for easy access. The first column contains the name of the athlete. The second contains information about their year of competition (from 2000-2016). The third (nationality) holds information about their country of origin. The fourth column holds the time it took them to complete the race. This is followed by total time taken in seconds, then a column for the title of race completed, after that is gender and sport name columns following thereafter are all medal-related ones: gold, silver and bronze respectivey with lastly an overview total number won and finally games they participated in completing 2019 Olympics trial races while other seasons held participative world championships in 2020 pandemic cancelations etc..

We encourage all to explore this data according to your particular interests - whether you would like to investigate which countries produce most successful adaptation athletes or look into most performed event individual athletes partake in in this category - whatever way you use it do not forget root purpose behind generating such records: paying tribute those amazing individuals who despite physical limitations have been able achieve excellence on par with other global professional athletes! Best wishes!

Research Ideas

• Creating an interactive map visualization showing the locations of global adaptive sports events.
• Developing a data dashboard that compares success and funding for UK Paralympic athletes over time.
• Analyzing trends in the medal counts of countries with high investments in Paralympic programs to determine which ones provide the greatest return on investment

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Marathon Wheelchair Winners.csv | Column name | Description | |:--------------------------|:------------------------------------------------------------------------------| | Athlete | The name of the athlete. (String) | | Year | The year in which the athlete competed. (Integer) | | Nationality | The country of origin of the athlete. (String) | | Time | The time it took the athlete to complete the race. (String) | | Total Time in Seconds | The total time it took the athlete to complete the race in seconds. (Integer) | | Race | The type of race the athlete competed in. (String) | | Gender | The gender of the athlete. (String) |

**File...

Context

The FIFA World Cup, often simply called the World Cup, is an international association football competition contested by the senior men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The championship has been awarded every four years since the inaugural tournament in 1930, except in 1942 and 1946 when it was not held because of the Second World War. The current champion is Argentina, which won its third title at the 2022 tournament in Qatar.

The current format involves a qualification phase, which takes place over the preceding three years, to determine which teams qualify for the tournament phase. In the tournament phase, 32 teams, including the automatically qualifying host nation(s), compete for the title at venues within the host nation(s) over about a month.

The 22 World Cup tournaments have been won by eight national teams. Brazil has won five times, and they are the only team to have played in every tournament. The other World Cup winners are Germany and Italy, with four titles each; Argentina with three, France, and inaugural winner Uruguay, with two titles each; and England and Spain, with one title each.

The World Cup is the most prestigious association football tournament in the world, as well as the most widely viewed and followed single sporting event in the world. The cumulative viewership of all matches of the 2006 World Cup was estimated to be 26.29 billion with an estimated 715.1 million people watching the final match, a ninth of the entire population of the planet.

18 countries have hosted the World Cup. Brazil, France, Italy, Germany, and Mexico have each hosted twice, while Uruguay, Switzerland, Sweden, Chile, England, Argentina, Spain, the United States, Japan, and South Korea (jointly), South Africa, Russia, and Qatar have each hosted once. The 2026 tournament will be jointly hosted by Canada, the United States, and Mexico, which will give Mexico the distinction of being the first country to host games in three World Cups.

Content

This Dataset consists of Records from all the previous Football World Cups (1930 to 2022)

Structure of the Dataset

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Acknowledgements

This Dataset is created from https://www.fifa.com/. If you want to learn more, you can visit the Website.

Cover Photo: https://wallpapercave.com/fifa-world-cup-wallpapers

This dataset investigates the impact of player injuries on team performance across seven Premier League clubs from 2019 to 2023, including Tottenham, Aston Villa, Brighton, Arsenal, Brentford, Everton, Burnley, and Manchester City. The dataset contains over 600 injury records, offering insights into how player absences influence match results and individual performance metrics.

Data Sources Transfer Market: Provided player injury records and durations. Football Critic: Offered player ratings for pre- and post-injury matches. Sky Sports: Supplemented additional match statistics and player performance data.

Dataset Overview Each entry includes: Player Information: Name, position, age, FIFA rating (spanning five years). Injury Details: Type of injury, date of injury, date of return. Performance Data: Match results (win, draw, loss), opposition, and goal difference (GD) for three matches before the injury, during missed matches, and for three matches after the player's return. Player ratings for each match, before and after the injury.

Key Data Points Performance fluctuations around injury events. Match outcomes during player absences. Ratings of players over time to observe any decline or improvement post-injury. This dataset is ideal for sports analytics, performance modeling, and evaluating the broader implications of player injuries on Premier League teams. Explore how injuries disrupt team dynamics and contribute to competitive outcomes in one of the world’s top football leagues.

Context

International football, also known as soccer, is a sport played by teams from countries around the world. The most prestigious international competition in football is the FIFA World Cup, which is held every four years and features teams from over 200 countries. Other major international tournaments include the UEFA European Championship, the AFC Asian Cup, the CAF Africa Cup of Nations, and the CONMEBOL Copa America.

Content

International football, or soccer as it is known in some countries, has a long and storied history that dates back to the late 19th century. The first international football match was played between Scotland and England in Glasgow, Scotland on November 30, 1872. The match ended in a 0-0 draw, and it was the first of many international contests that would be played between national teams around the world. Over the years, international football has grown in popularity and become a major global sport, with teams from more than 200 countries competing against each other in various tournaments and leagues. Some of the most well-known international football tournaments include the FIFA World Cup, the UEFA European Championship, and the AFC Asian Cup. The FIFA World Cup, which is held every four years, is the most prestigious international football tournament and attracts the best teams from around the world. The first World Cup was held in 1930 in Uruguay, and since then it has been held in a different country each time. Brazil has won the most World Cup titles, with a total of five victories, while Germany and Italy have each won four titles. The UEFA European Championship, which is also held every four years, is a major international football tournament that features teams from Europe. The first European Championship was held in 1960, and it has been held every four years since then. The most successful team in the history of the European Championship is Germany, which has won the tournament four times. In addition to these major tournaments, there are also many other international football competitions that are held around the world, including the AFC Asian Cup, the CAF Africa Cup of Nations, and the CONCACAF Gold Cup. As the sport has evolved over the years, it has also faced its share of controversies and challenges. In the early days of international football, there were often disputes over rules and regulations, and teams from different countries sometimes had difficulty agreeing on a common set of rules. In more recent years, issues such as doping, match-fixing, and racism have also plagued the sport. Despite these challenges, international football remains one of the most popular and widely-followed sports in the world, with millions of fans and players around the globe. As the sport continues to grow and evolve in the coming years, it is sure to remain a major part of the global sporting landscape. Here is a list of some football (soccer) teams that have changed their names in the past: 1. Manchester United FC (Old name: Newton Heath LYR FC) 2. FC Barcelona (Old name: Foot-Ball Club Barcelona) 3. Bayern Munich (Old name: FC Bayern Munich) 4. Juventus FC (Old name: Sport Club Juventus) 5. Paris Saint-Germain FC (Old name: Paris FC) 6. AC Milan (Old name: Milan Foot-Ball and Cricket Club) 7. AS Roma (Old name: Roman Football Club) 8. Ajax Amsterdam (Old name: Amsterdamsche Football Club Ajax) 9. Inter Milan (Old name: Internazionale Football Club Milan) 10. Liverpool FC (Old name: Everton FC)

Acknowledgements

The data is assemble from several sources along with but not limited to Wikipedia, rsssf.com, and individual football associations' websites

Inspiration

1. The passion and pride that players and fans feel for their national teams
2. The thrill of watching top-level international competition and seeing the world's best players face off against one another
3. The sense of unity and community that football can bring, as people from all walks of life come together to support their team
4. The opportunity to experience new cultures and ways of life through the lens of football, as the sport is played and followed in nearly every country on earth
5. The exciting atmosphere of major international tournaments, with packed stadiums and fans from all around the world cheering on their teams
6. The inspiring stories of underdogs and lesser-known nations who rise to the occasion on the international stage. # Columns details. decision.csv including columns: date: the date the game was played.

home_team: the team that played the game on their home field.
away_team: the team that played the game as the visiting team.
home_score: the number of goa...