We will create a customized sports dataset tailored to your specific requirements. Data points may include player statistics, team rankings, game scores, player contracts, and other relevant metrics.

Utilize our sports datasets for a variety of applications to boost strategic planning and performance analysis. Analyzing these datasets can help organizations understand player performance and market trends within the sports industry, allowing for more precise team management and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing player performance analysis, refining team strategies, and optimizing fan engagement efforts.

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

UCF Sports dataset consists of 150 videos from sport broadcasts covering 10 action categories.

Online Sports Betting

A State-by-State Comparison

About this dataset

How prevalent is sports betting across the United States? This dataset provides information on the legal status of sports betting, revenue generated by sports betting, the number of sports betting outlets, and more. Use this dataset to compare the revenue generated by sports betting across different states

How to use the dataset

This dataset can be used to understand the prevalence of sports betting across the United States and to compare the revenue generated by sports betting across states.

Research Ideas

• Understand the prevalence of sports betting across the United States and to compare the revenue generated by sports betting across states
• Understand how the legal status of sports betting affects revenue generated
• Understand how the number of sports betting outlets affects revenue generated

Columns

File: New Jersey.csv | Column name | Description | |:------------------|:--------------------------------------------------------------| | date | The date of the data. (Date) | | New Jersey | The amount of money bet on sports in New Jersey. (Numeric) | | Pennsylvania | The amount of money bet on sports in Pennsylvania. (Numeric) | | Delaware | The amount of money bet on sports in Delaware. (Numeric) | | Mississippi | The amount of money bet on sports in Mississippi. (Numeric) | | Nevada | The amount of money bet on sports in Nevada. (Numeric) | | Rhode Island | The amount of money bet on sports in Rhode Island. (Numeric) | | West Virginia | The amount of money bet on sports in West Virginia. (Numeric) | | Arkansas | The amount of money bet on sports in Arkansas. (Numeric) | | New York | The amount of money bet on sports in New York. (Numeric) | | Iowa | The amount of money bet on sports in Iowa. (Numeric) | | Indiana | The amount of money bet on sports in Indiana. (Numeric) | | Oregon | The amount of money bet on sports in Oregon. (Numeric) | | New Hampshire | The amount of money bet on sports in New Hampshire. (Numeric) | | Michigan | The amount of money bet on sports in Michigan. (Numeric) | | Montana | The amount of money bet on sports in Montana. (Numeric) | | Colorado | The amount of money bet on sports in Colorado. (Numeric) | | Washington DC | The amount of money bet on sports in Washington DC. (Numeric) | | Illinois | The amount of money bet on sports in Illinois. (Numeric) | | Tennessee | The amount of money bet on sports in Tennessee. (Numeric) |

File: PopulationStates.csv | Column name | Description | |:--------------|:----------------------------------------------------| | State | The state in which the data was collected. (String) |

File: homeless.csv | Column name | Description | |:----------------|:----------------------------------------------------| | year | The year the data was collected. (Integer) | | unsheltered | The number of people who are unsheltered. (Integer) |

File: income.csv | Column name | Description | |:------------------|:--------------------------------------------------------------| | Pennsylvania | The amount of money bet on sports in Pennsylvania. (Numeric) | | Delaware | The amount of money bet on sports in Delaware. (Numeric) | | Mississippi | The amount of money bet on sports in Mississippi. (Numeric) | | Nevada | The amount of money bet on sports in Nevada. (Numeric) | | Rhode Island | The amount of money bet on sports in Rhode Island. (Numeric) | | West Virginia | The amount of money bet on sports in West Virginia. (Numeric) | | Arkansas | The amount of money bet on sports in Arkansas. (Numeric) | | New York | The amount of money bet on sports in New York. (Numeric) | | Iowa | The amount of money bet on sports in Iowa. (Numeric) | | Indiana | The amount of money bet on sports in Indiana. (Numeric) | | New Hampshire | The amount of money bet on sports in New Hampshire. (Numeric) | | Michigan | The amount of money bet on sports in Michigan. (Numeric) | | Colorado | The amount of money bet on sports in Colorado. (Numeric) | | Washington DC | The amount of money bet on sports in Washington DC. (Numeric) | | Illinois | The amount of money bet on sports in Illinois. (Nume...

'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).

Dataset

This dataset was created by Jyothish

Contents

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

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

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 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.

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.

The size of the Sports Analytics Market market was valued at USD 2.87 Million in 2023 and is projected to reach USD 18.05 Million by 2032, with an expected CAGR of 30.04% during the forecast period. Recent developments include: October 2023, Texas A&M Athletics Sports Science announced that it has entered into an arrangement with Gemini Sports Analytics to offer the Aggies' staff Gemini’s AI software platform built-for sports that is projected to empower the Aggies to access prognostic analytics in addition to metrics to aid support student-athletes. The Gemini application authorizes stakeholders by offering predictive data analytics to the end users, cumulative interdisciplinary professionals' efficiency, and permitting high-level decision-makers to make game-changing choices faster., February 2023: Gemini Sports Analytics is an AI and Automated Machine learning tool, and SIS (Sports Info Solutions) announced a partnership to pre-integrate SIS data into the Gemini app. Along with the data integration, the two companies would leverage their complementary offerings and develop solutions for their current and future clients. Gemini's mission is to make it faster and easier for sports organizations across the globe to use predictive analytics in their decision-making processes around recruitment, player development, personnel, health and performance, and other management choices.. Key drivers for this market are: Rising Adoption of Big Data Analytics, AI and ML Technologies, Increase in Investments in the Newer Technologies. Potential restraints include: Lack of Awareness About the Benefits of Sports Analytics Solutions. Notable trends are: Football Sport is Expected to Hold Significant Market Share.

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.

UIUC-Sports dataset

its-zion-18/sports-text-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

In 2025, the global sports industry’s market size was estimated to total 417 billion U.S. dollars. The industry's revenue was forecast to grow in the coming years. How big is the global sports betting market? The global sports industry is made up of a long list of subsectors. One of these is the sports betting market. In 2024, the market size of the sports betting industry worldwide was valued at around 70 billion U.S. dollars and was forecast to reach nearly 100 billion U.S. dollars by 2029. Regionally speaking, bettors in Asia made up over half of the amount wagered on sports globally in 2024. What are the most valuable sports teams in the world? In 2024, all 10 of the most valuable sports teams worldwide were based in the United States. Among these, the Dallas Cowboys sat atop the pile, with a valuation of over 10 billion U.S. dollars. Meanwhile, soccer clubs Real Madrid and Manchester United featured in the top 20, with both valued at over six billion U.S. dollars.

Combat Sports

## Overview

Combat Sports is a dataset for object detection tasks - it contains Combat Sports annotations for 9,412 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).

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).

The Sports-1M dataset is a large-scale video dataset for activity recognition. It contains 600 classes of actions, with 50 hours of video footage.