The dataset includes essential player details such as name, nationality, team affiliation, maps played, rounds played, KD differential, KD ratio, rating, and event information from the biggest events of the CSGO esport.

This dataset was obtained by scraping data from hltv.org. The scraping code used to collect the data can be found in the GitHub repository: https://github.com/matheusnobrega/major-scrapper.

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...

Homeland Infrastructure Foundation-Level Data (HIFLD) geospatial data sets containing information on Major Sport Venues.

This study aims to explore new educational strategies suitable for the mental health education of college students. Big data and artificial intelligence (AI) are combined to evaluate the mental health education of college students in sports majors. First, the research status on the mental health education of college students is introduced. The internet of things (IoT) on mental health education, a structure based on big data and convolutional neural network (CNN), is constructed. Next, the survey design and questionnaire survey are carried out. Finally, the questionnaire data are analyzed and compared with the mental health status under traditional education. The results show that the CNN model has good accuracy and ability to distinguish symptoms, so it can be applied to the existing psychological work in colleges. In the symptom comparison survey, under the traditional education and big data network, the number of college students with mild mental health problems is found to be 158 (84.9%) and 170 (91.4%), respectively. It indicates that the number of college students with moderate mental health problems decreases significantly. In the comparative investigation of the severity of mental problems, the number of students with normal mental health, subhealth, and serious mental health problems under the background of traditional mental health education is 125 (67.2%), 56 (30.1%), and 5 (2.7%), respectively. The mental health status of college students under the influence of big data networks on mental health education is better than that of traditional mental health education. There are 140 students with normal mental health, a year-on-year increase of 16.7%. In the comparative survey of specific mental disorders, students with obsessive-compulsive symptoms under traditional mental health education account for 22.0% of the total sample, having the largest proportion. In the subhealth psychological group under the big data network on mental health education, the number of hostile students decreases by 7, which is the psychological factor with the most obvious improvement. Hence, the proposed path of mental health education is feasible.

Sponsorship revenue for the major sports leagues in the United States reaches into the millions of U.S. dollars every year. In the 2022 season, sponsorship revenue in the National Football League (NFL) stood at 1.88 billion U.S. dollars. The largest sponsorship category within the league was technology, followed by betting, lottery and gaming.

Attendance at National Football League games

The National Football League is the highest division of professional American Football in the United States. With 32 teams competing annually with the hopes of advancing to and winning the Super Bowl championship game, the league attracts millions of fans from across the country each year. Comparing the average per game attendance of the four major sports league in North America, the NFL attracted close to 70 thousand fans per game on average, ranking it higher than Major League Baseball, the National Basketball Association, and the National Hockey League in the 2022/23 season. Overall, the Dallas Cowboys welcomed the most attendees in the NFL on average in 2022.

Fantasy sports in the U.S.

One way for fans to engage with their favorite professional athletes is through fantasy sports, which involves the creation of a virtual team that tracks the performance of players and enables users to accumulate points after each game week. In 2022, the fantasy sports industry in the United States was worth an estimated 9.48 billion U.S. dollars. According to an August 2022 survey, ESPN’s platform ESPN Fantasy Sports was the most popular to use amongst fantasy football players in the United States. The survey also revealed some of the most important features of fantasy football platforms for users from the United States, with platform simplicity being very important for almost two thirds of respondents.

China Activity Participation Rate: Major Activity Categories: Sports and Fitness data was reported at 49.600 % in 2024. This records an increase from the previous number of 30.900 % for 2018. China Activity Participation Rate: Major Activity Categories: Sports and Fitness data is updated yearly, averaging 30.900 % from Dec 2008 (Median) to 2024, with 3 observations. The data reached an all-time high of 49.600 % in 2024 and a record low of 27.000 % in 2008. China Activity Participation Rate: Major Activity Categories: Sports and Fitness data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OT: Activity Participation Rate.

Snapshot img

Sports Analytics Market Size 2025-2029

The sports analytics market size is valued to increase USD 8.4 billion, at a CAGR of 28.5% from 2024 to 2029. Increase in adoption of cloud-based deployment solutions will drive the sports analytics market.

Major Market Trends & Insights

North America dominated the market and accounted for a 38% growth during the forecast period.
By Type - Football segment was valued at USD 749.30 billion in 2023
By Solution - Player analysis segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 584.13 million
Market Future Opportunities: USD 8403.30 million
CAGR : 28.5%
North America: Largest market in 2023

Market Summary

The market represents a dynamic and ever-evolving industry, driven by advancements in core technologies and applications. Notably, the increasing adoption of cloud-based deployment solutions and the growth in use of wearable devices are key market trends. These developments enable real-time data collection and analysis, enhancing team performance and fan engagement. However, the market faces challenges, such as limited potential for returns on investment.
Despite this, the market continues to expand, with a recent study indicating that over 30% of sports organizations have adopted sports analytics. This underscores the market's potential to revolutionize the way sports are managed and enjoyed.

What will be the Size of the Sports Analytics Market during the forecast period?

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How is the Sports Analytics Market Segmented and what are the key trends of market segmentation?

The sports analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Football
  Cricket
  Hockey
  Tennis
  Others


Solution

  Player analysis
  Team performance analysis
  Health assessment
  Fan engagement analysis
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The football segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, driven by the increasing demand for data-driven insights in football and other popular sports. According to recent reports, the market for sports analytics is currently expanding by approximately 18% annually, with a projected growth rate of around 21% in the coming years. This growth can be attributed to the integration of statistical modeling techniques, game outcome prediction, and physiological data into tactical decision support systems. Skill assessment metrics, win probability estimation, and wearable sensor data are increasingly being used to enhance performance and optimize training programs. Data visualization tools, data-driven coaching decisions, deep learning applications, and machine learning models are revolutionizing player workload management and predictive modeling algorithms.

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The Football segment was valued at USD 749.30 billion in 2019 and showed a gradual increase during the forecast period.

Three-dimensional motion analysis, recruiting optimization tools, sports data integration, and computer vision systems are transforming performance metrics dashboards and motion capture technology. Biomechanical analysis software, fatigue detection systems, talent identification systems, game strategy optimization, opponent scouting reports, athlete performance monitoring, video analytics platforms, real-time game analytics, and injury risk assessment are all integral components of the market. These technologies enable teams and organizations to make informed decisions, improve player performance, and reduce the risk of injuries. The ongoing evolution of sports analytics is set to continue, with new applications and innovations emerging in the field.

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Regional Analysis

North America is estimated to contribute 38% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How Sports Analytics Market Demand is Rising in North America Request Free Sample

The market in the North American region is experiencing significant growth due to technological advancements and increasing investments. In 2024, the US and Canada were major contributors to this expansion. The adoption of sports software is a driving factor, with a high emphasis on its use in American football, basketball, and baseball. Major sports leagues in the US are

The Sports Leagues Dataset (SLD) contains statistical data of the major professional sports leagues in the United States: NFL (National Football League), NBA (National Basketball Association), NHL (National Hockey League) and MLB (Major League Baseball). One collect five topics (Player Expenses, Player Salaries, Players Performance, Team Salaries, Team Valuation) of two dimensions (Finance and Performance) in different seasons (2000-2007) from three data sources (Forbes, Spotrac and Sports Reference).

Please consider citing https://doi.org/10.5281/zenodo.3256432 if you found this dataset useful:

[1] André Albino Bastos, Matheus de Oliveira Salim, Wladmir Cardoso Brandão. (2019). SLD: The Sports Leagues Dataset (Version 1.0) [Data set]. Zenodo.

Context

This dataset presents statistics from each match from CSGO Major. So, after match ended is generated statistics, like scoreboard for each map played, who team was the winner and loser and players stats, K/D, ADR, etc.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

The dataset consists of 75 columns, which are:

Financial overview and grant giving statistics of 49 Degrees North Winter Sports Foundation

In the 2023 season, roughly ** percent of the total revenue generated in the MLB was through local media. Meanwhile, only *** percent of the NFL's total revenue generated in that season was from local media.

China Participants’ Average Daily Time Use: Major Activity Categories: Sports and Fitness data was reported at 70.000 min in 2024. This records a decrease from the previous number of 101.000 min for 2018. China Participants’ Average Daily Time Use: Major Activity Categories: Sports and Fitness data is updated yearly, averaging 86.000 min from Dec 2008 (Median) to 2024, with 3 observations. The data reached an all-time high of 101.000 min in 2018 and a record low of 70.000 min in 2024. China Participants’ Average Daily Time Use: Major Activity Categories: Sports and Fitness data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OT: Participants’ Average Daily Time Use.

In the 2024 season, roughly ** percent of the total revenue generated in the NHL was through team sponsorships. Meanwhile, only ** percent of the NFL's total revenue generated in that season was from sponsorships.

China Residents’ Average Daily Time Use: Major Activity Categories: Sports and Fitness data was reported at 35.000 min in 2024. This records an increase from the previous number of 31.000 min for 2018. China Residents’ Average Daily Time Use: Major Activity Categories: Sports and Fitness data is updated yearly, averaging 31.000 min from Dec 2008 (Median) to 2024, with 3 observations. The data reached an all-time high of 35.000 min in 2024 and a record low of 23.000 min in 2008. China Residents’ Average Daily Time Use: Major Activity Categories: Sports and Fitness data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OT: Residents’ Average Daily Time Use.

Datasets for YouGov polling for Major Sports Events in 2024

The Excel file is organized into the following sheets:

SM_Sheet1 - global bibliometric indicators in the sport sciences

SM_Sheet2 - main indicators at country for two different period

SM_Sheet3 - list of 4159 research topics for world science research

SM_Sheet4 - a detailed description of main research topics in sport sciences

SM_Sheet5 - the number of journals included in the Journal Citation Reports (JCR) in the sport science category and the topic distribution according to the Gini Index are as follows

Dataset

This dataset was created by Leonard Chiru

Released under Data files © Original Authors

Contents

Major League Soccer (MLS) is a professional soccer league in North America, comprising 26 teams from the United States and Canada. The league is a subsidiary of the United States Soccer Federation, the Canadian Soccer Association, and Major League Soccer, LLC. nnThe league operates on a spring-fall schedule, with the regular season typically running from February to October and the playoffs culminating in the MLS Cup. The league has become increasingly popular over the years, attracting top talent from around the world and drawing significant media coverage.

The Major Public Venues dataset is composed of facilities that host events for 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. Missing data for individual records are denoted by 'Not Available' or NULL values. Not Available or NULL denotes information that was either missing in the source data or data that has not been populated current version.

Context

This dataset contains 2022 MLB player stats. Note that there are duplicate player names resulted from team changes.

Content

+900 rows and +20 columns. Columns' description are listed below.

Pitching: * Rk : Rank * Name : Player name * Age : Player's age * Tm : Team * Lg : League * W : Wins * L : Losses * W-L% : Win-Loss percentage * ERA : 9 * ER / IP * G : Games played * GS : Games started * GF : Games finished * CG : Complete game * SHO : Shutouts * SV : Saves * IP : Innings pitched * H : Hits/Hits allowed * R : Runs scored/allowed * ER : Earned runs allowed * HR : Home runs hit/allowed * BB : Bases on balls/walks * IBB : Intentional bases on balls * SO : Strikeouts * HBP : Times hit by a pitch * BK : Balks * WP : Wild pitches * BF : Batters faced * ERA+ : 100 * (logERA/ERA) * FIP : Fielding independent pitching. Measures a pitcher's effectiveness at HR, BB, HBP and causing SO. * WHIP : (BB + H) / IP * H9 : 9 * H / IP * HR9 : 9 * HR / IP * BB9 : 9 * BB / IP * SO9 : 9 * SO / IP * SO/W : SO / W

Batting: * Rk : Rank * Name : Player name * Age : Player's age * Tm : Team * Lg : League * G : Games played * PA : Plate appearances * AB : At bats * R : Runs scored/allowed * H : Hits/hits allowed * 2B : Doubles hit/allowed * 3B : Triples hit/allowed * HR : Home runs hit/allowed * RBI : Runs batted in * SB : Stolen bases * CS : Caught stealing * BB : Bases on balls/walks * SO : Strikeouts * BA : Hits/at bats * OBP : (H + BB + HBP) / (AB + BB + HBP + SF) * SLG : Total bases/at bats or (1B + 2 * 2B + 3 * 3B + 4 * HR) / AB * OPS : On-base + Slugging percentages * OPS+ : 100 * (OBP / logOBP + SLG / logSLG - 1) * TB : Total bases * GDP : Double plays grounded into * HBP : Times hit by a pitch * SH : Sacrifice hits * SF : Sacrifice flies * IBB : Intentional bases on balls

Acknowledgements

Data from Baseball Reference. Image from MLB.

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