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.”

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.

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...

The National Basketball Association (NBA) is a professional basketball league in North America composed of 30 teams. It is one of the major professional sports leagues in the United States and Canada and is considered the premier professional basketball league in the world.

The NBA draft combine is a multi-day showcase that takes place every May before the annual NBA draft. At the combine, college basketball players are measured and take medical tests, are interviewed, perform various athletic tests and shooting drills, and play in five-on-five drills for an audience of National Basketball Association (NBA) coaches, general managers, and scouts. Athletes attend by invitation only. An athlete's performance during the combine can affect perception, draft status, salary, and ultimately the player's career.

This dataset includes the anthropometric measurements collected during Draft Combine events in years 2000-2023. It has been acquired using NBA Stats API. The units have been converted from imperial to metric system (inches to centimeters and pounds to kilograms). The numbers have been rounded to two decimal points.

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.

1. General Information

League ID: A unique identifier (e.g., "L001") assigned to each league to allow easy referencing within the dataset. League Name: Official name of the sports league (e.g., Premier League, NBA). This field helps distinguish leagues by their global or regional branding. Country: The primary country or region where the league is based, giving insights into the geographical spread and local fan base.

2. Sport Type

Sport: Specifies the type of sport played in the league, such as Football, Basketball, American Football, or Cricket. This field is valuable for categorizing leagues and comparing similar sports across countries.

3. Financial Metrics

Revenue (USD): Estimated annual revenue generated by the league, presented in millions of USD. Revenue figures can reflect league profitability and influence on the sports economy. Average Player Salary (USD): The average annual salary of players within the league, also in millions of USD. This can indicate the level of investment in player talent and competitiveness within the league.

4. Teams and Structure

Top Team: A notable or high-performing team within the league, which helps identify prominent clubs or franchises that may drive popularity and revenue. Total Teams: The total number of teams participating in the league, providing a sense of the league's size and structure. Larger leagues may indicate more regional diversity and fan engagement. Founded Year: The year the league was established, offering historical context and allowing analysis of how older versus newer leagues perform financially and in popularity.

5. Popularity and Viewership

Viewership: Estimated viewership numbers in millions, indicating the league's global or regional popularity. High viewership can often correlate with higher sponsorships, broadcasting rights, and overall league valuation.

6. Analysis Applications

This dataset can be used for a variety of analyses:

Market Size Comparisons: Compare leagues by revenue and viewership across different countries and sports. Player Salary Trends: Assess trends in player salaries across leagues, helping understand the financial draw of each league. Viewership Patterns: Analyze which leagues have the largest fan bases and where these are located geographically. League Growth Potential: Determine which leagues are growing in revenue and popularity over time based on the founded year and financial metrics.

Version 2 Update - Merged "Racing" and "Auto Racing" both into just "Racing" category, fixed a minor issue where one Basketball player had sport labeled as "Basketbal"

Since 1990 Forbes has tracked the highest-earning athletes in the world. For each year (except 2001 when they switched the time period for which the data was tallied), the magazine has listed between 40 or so and 100 athletes earning the most in salary and endorsements.

This data was compiled for quizzes on my site such as: https://hugequiz.com/quizzes/forbes-highest-paid-athletes-by-year/

(UNCLASSIFIED) Recreation is broken down into the following types: Sports Facility, Pool, Park and Other. Sports Facilities include any field where sports are played professional or leisurely and include basketball courts, soccer stadiums and fields, tennis courts, etc. Parks include recreational areas around the cities.Development of Liberia’s recreational locations has taken a backseat in the post-war era to rehabilitation of critical infrastructure and services. Despite the resulting scarcity of parks and sports facilities throughout the country, soccer has surged in popularity and is by far the country’s most popular sport. The Liberian national team, nicknamed the Lone Stars for the Liberian flag, has seen a surge in popularity despite never having qualified for a World Cup. Semi-professional local teams have also experienced growing interest and have seen a 40 percent increase in match-attendance since 2014. An inter-county tournament is held annually for the sport’s highest award in the country, the Barclay Shield. Basketball, swimming, and squash are popular in Liberia’s urban areas, especially Monrovia, despite a lack of facilities outside of hotels and expatriate clubs. School children play soccer and kickball—typically on bare patches of earth rather than formal fields—as well as marbles (usually using dried seeds).Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name ADM1_NAME - Administration level one identification / name ADM2_NAME - Administration level two identification / name NAME - Name of recreation area TYPE - Classification in the geodatabase CITY - City location available SPA_ACC - Spatial accuracy of site location (1 – high, 2 – medium, 3 – low) COMMENTS - Comments or notes regarding the recreation area SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThis feature class was generated utilizing data from Wikimapia, OpenStreetMap, and other sources. Wikimapia is open-content mapping focused on gathering all geographical objects in the world. OpenStreetMap is a free worldwide map, created by crowd-sourcing.Consistent naming conventions for geographic locations were attempted but name variants may exist which can include historical or less widespread interpretations.The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Metadata information was collected from an encyclopedia entry, an article published by BET, as well as a book on Liberian culture and recreation.Sources (HGIS)DigitalGlobe, “DigitalGlobe Imagery Archive.” Accessed October 03, 2014. Google, October 2014. Accessed October 03, 2014. www.google.com.OpenStreetMap, “Liberia.” October 2014. Accessed October 03, 2014. http://www.openstreetmap.org.Wikimapia, “Liberia.” October 2014. Accessed October 03, 2014. http://wikimapia.org.Sources (Metadata)Hicks, Jonathan P. “In Liberia, Soccer Is Bringing People Together: The West African nation is seeing a resurgance in the sport known as football, with attendance and sponsorships up.” BET. April 03, 2014. Accessed October 03, 2014. http://www.bet.com.Levy, Patricia and Michael Spilling. Cultures of the World: Liberia. 2010. Accessed October 03, 2014. http://books.google.com.Petterson, Donald Rahl. “Liberia: Sports and Recreation.” Encyclopedia Britannica Online. August 27, 2014. Accessed October 03, 2014. http://www.britannica.com.