Content

This file contains the comprehensive information on collegiate sports programs across various institutions in the United States. It includes data on student enrollment, sports participation, revenue, and expenditures, categorized by gender and sport. The dataset can be used to analyze trends, financial aspects, and gender disparities in collegiate sports.

Key Insights

Enrollment Data: The dataset includes the total number of male and female students enrolled in each institution, providing insights into the gender distribution of the student body.

Sports Participation: Participation data is broken down by gender and sport, allowing for analysis of gender representation in different sports.

Financial Data: Revenue and expenditures for men's and women's sports are detailed, enabling financial analysis of sports programs.

Institutional Classification: Institutions are classified by type and sector, which helps in comparing different categories of schools (e.g., NCAA Division I, II, III).

Context

Geography: USA

Time period: 2015- 2019

Unit of analysis: US Collegiate Sports Dataset

Variables

Acknowledgements

Datasource: Equity in Athletics Data Analysis , hattip to Data is Plural

Inspiration: Additional articles from US NEWS, [USA Facts](https://usafacts.org/articles/coronavirus-college-football-profi...

The Sports-1M dataset is licensed under Creative Commons 3.0 and contains 1,133,158 video URLs which have been annotated automatically with 487 Sports labels using the YouTube Topics API. To download the dataset, check out our GitHub Repository, or simply use:

$ git clone https://github.com/gtoderici/sports-1m-dataset.git

Curated sports image dataset featuring 22 sports categories for computer vision, action recognition, and machine learning projects.

'sports'

## Overview

'sports' is a dataset for classification tasks - it contains Cards annotations for 707 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Olympic Sports dataset consists of video sequences of athletes practicing 16 different sports. The dataset contains an overall number of 113,516 frames, covering a rich set of human postures.

The BD Sports-10 Dataset is a comprehensive collection of 3,000 high-resolution videos (1920×1080 pixels at 30 frames per second) showcasing ten culturally and traditionally significant Bangladeshi sports. It is designed to support research in action recognition, cultural heritage preservation, sports video classification, and machine learning applications. The BD_Sports_10 folder contains two subfolders: Annotation and Dataset. The Dataset folder includes 10 subfolders, each corresponding to a sports class. Each sports category comprises 300 videos, ensuring a balanced distribution for supervised learning tasks.The dataset includes the following Bangladeshi sports:Hari VangaJoldangaKanamachiLathimMorog LoraiToilakto Kolagach Arohon (Kolagach)Nouka BaichKabaddiKho KhoLathi Khela

Combat Sports

## Overview

Combat Sports is a dataset for object detection tasks - it contains Combat annotations for 382 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Dataset Summary

QASports is the first large sports-themed question answering dataset counting over 1.5 million questions and answers about 54k preprocessed wiki pages, using as documents the wiki of 3 of the most popular sports in the world, Soccer, American Football and Basketball. Each sport can be downloaded individually as a subset, with the train, test and validation splits, or all 3 can be downloaded together.

🎲 Complete dataset: https://osf.io/n7r23/ 🔧 Processing scripts:… See the full description on the dataset page: https://huggingface.co/datasets/PedroCJardim/QASports.

The database contains the physical characteristics and athletic performance of middle school students between the ages of 11 and 19. The data includes age, sex, weight, height, BMI, sprint and long jump performance. This database can be used for a number of research purposes: analysis of student performance, physical characteristics of adolescents, application of artificial intelligence in sport, etc.

Discover the booming sports data service market! This analysis reveals a $3.146 billion market in 2025, projected for rapid growth (12-15% CAGR) driven by data analytics, esports, and fantasy sports. Explore key trends, segments (sports data collection, analysis), top companies, and regional insights.

Activity and attendance records from the "Summer Sports Experience" program, which provides sports instruction to children ages 8 to 14. This dataset contains information specific to the Summer Sports Experience programming from 2017 to 2021. For Summer Sports Experience Open Data from 2022 onwards, go here Learn more about this program on the NYC Parks website: Summer Sports Experience Note: Summer Sports Experience was on pause due to COVID-19 pandemic. The program resumed 2021.

Digital Card Magazine Dataset

This dataset contains sports card images and their associated metadata for training machine learning models in card recognition, text extraction, and value estimation.

  Dataset Description





  Dataset Summary

A comprehensive collection of sports card images and metadata, including:

Front and back card images OCR-extracted text with confidence scores AI-analyzed card attributes Card details (player, team, year, etc.) Vision API labels… See the full description on the dataset page: https://huggingface.co/datasets/GotThatData/sports-cards.

Over 9,000 images of sports balls from 15 different sports. These are: american football, baseball, basketball, billiard ball, bowling ball, cricket ball, football, golf ball, field hockey ball, hockey puck, rugby ball, shuttlecock, table tennis ball, tennis ball and volleyball.

Images were scraped from google images and then manually reviewed to remove misclassified images (although I might have missed some). Duplicates were removed using a dHash style algorithm. The train/test split is 80/20.

This is an interesting dataset because several images are misleading, e.g. some balls have been painted to look like other balls. See some examples below:

https://i.postimg.cc/jdHLWfbs/golf-ball-681-copy.jpg">

https://i.postimg.cc/85vT28k1/cricket-ball-1156.jpg">

https://i.postimg.cc/yYs7brMH/american-football-58.jpg">

The Sports Analytics Market Report is Segmented by Sport (Football, Cricket, Basketball, Hockey, American Football, Baseball, Rugby, Other Sports), Component (Software, and Services), Deployment (On-Premise, and Cloud), End User (Sports Teams/Clubs, Leagues and Federations, Individual Athletes, Sports Betting Operators, Other End User), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Sports

## Overview

Sports is a dataset for object detection tasks - it contains Sports annotations for 886 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Discover the explosive growth of the sports data service market, projected to reach $1721.4 million by 2025 with a 9% CAGR. This comprehensive analysis explores market drivers, trends, restraints, key players (Sportradar, Stats Perform, Genius Sports), and regional insights. Learn about the opportunities in sports data collection, analysis, and its applications across professional clubs and state agencies.

Dataset

This dataset was created by Vallabh Kulkarni

Released under CC0: Public Domain

Contents

The global Sports Data Analytics Service market is booming, projected to reach $3067.3 million by 2025, with a 27.5% CAGR. Discover key trends, market segments (professional clubs, state agencies, online/offline services), and leading companies driving this explosive growth in sports analytics.

The first iteration of the Sport and Recreation Spatial web-portal (released December 2012) contains data from the following sources:

Exercise, Recreation and Sport Survey (ERASS)-Australian Sports Commission (ACS)

Data is displayed on the map includes the annual surveys from 2001-2010, and is mapped nationally. The ERASS survey was a joint initiative of the Australian Sports Commission and State and Territory Departments of Sport and Recreation. The ERASS survey involves data from Australian's aged 15 years or over. This data includes information the frequency, duration, nature and type of activities participated in by persons aged 15 years and over for exercise, recreation or sport during the 12 months prior to interview. It includes information on different activities for participants and does not include coaching, umpiring, or being a spectator.

Census population data- ABS

Census region population data (State, SA4, LGA and postcode) is used to calculate participation rates and population estimates for region samples.

Socio-Economic Indexes for Areas (SEIFA) -ABS

Sport and Recreation Facility and Infrastructure data - Victoria -Sport and Recreation Victoria

A comprehensive list of all sport and recreation facilities in Victoria (Coming soon).

Victorian State Sporting Association Participation data - State sporting associations

This includes annual data of sport participants, coaches and umpires for the following sports:

• AFL
• Basketball
• Bowls
• Cricket
• Hockey
• Netball
• Soccer
• Tennis

This data is being mapped at a Victorian state level. Where available the particpant, coach and umpire data includes information on individuals' sport identification codes, date of birth, sex, resident postcode, club name, and sport participation program or level of coach and umpire accreditation. This data will be mapped in the near future.