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.

This dataset is designed to support research and development in intelligent sports training systems by leveraging real-time data from Internet of Things (IoT) devices. It simulates data collected from athletes and students during physical training sessions using wearable sensors, motion trackers, and smart garments. The dataset combines physiological signals, motion dynamics, and session metadata to enable advanced data mining, pattern recognition, and performance optimization.

Key features include:

Physiological Data: Heart rate, respiratory rate, body temperature

Motion Data: Accelerometer (X, Y, Z), gyroscope (X, Y, Z), step count

Session Metadata: Age, gender, sport type, exercise performed, session duration

Advanced Features: Power Spectral Density (PSD) values from selected signals

Target Label: Performance_Score, representing training output and efficiency

The dataset is particularly suited for machine learning applications such as:

Performance prediction

Injury risk classification

Fatigue detection

Personalized training recommendation systems

Dataset

This dataset was created by Jyothish

Contents

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

Object Detection Sports

## Overview

Object Detection  Sports is a dataset for object detection tasks - it contains Sports Person annotations for 1,140 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 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).

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 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 Analysis

## Overview

Sports Analysis is a dataset for object detection tasks - it contains Sports annotations for 3,985 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.

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.

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...

kcrl/Sports-Comment dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

Sports Analytics Market Outlook

According to our latest research, the sports analytics market size reached USD 4.2 billion in 2024, reflecting a strong momentum in the adoption of data-driven strategies by sports organizations worldwide. The market is projected to grow at a robust CAGR of 22.9% from 2025 to 2033, reaching an estimated USD 32.1 billion by 2033. This remarkable growth is primarily driven by the increasing need for competitive advantage, enhanced fan engagement, and the integration of advanced technologies such as AI and machine learning in sports operations. As per our latest research, the sports analytics market is poised for transformative expansion, fueled by the convergence of technology, sports performance, and business intelligence.

One of the primary growth factors for the sports analytics market is the escalating demand for performance optimization among professional sports teams and associations. With the stakes in global sports events rising each year, organizations are investing heavily in advanced analytics solutions to gain actionable insights into player performance, injury prevention, and tactical decision-making. The proliferation of wearable devices and sensors has enabled the collection of granular data on athletesÂ’ physical and physiological parameters, which, when analyzed, offers a detailed understanding of strengths, weaknesses, and potential risks. This data-centric approach is revolutionizing traditional coaching methods and driving a paradigm shift toward evidence-based decision-making in sports management.

Another significant driver of the sports analytics market is the evolution of fan engagement strategies. In an era where digital interaction and personalized experiences define brand loyalty, sports organizations are leveraging analytics to understand fan preferences, behavior, and sentiment. By analyzing data from social media, ticket sales, and merchandise purchases, teams can tailor marketing campaigns, optimize ticket pricing, and enhance in-stadium experiences. The integration of real-time analytics during live events further empowers organizations to engage fans through interactive content, predictive insights, and immersive technologies such as augmented and virtual reality, thereby creating new revenue streams and strengthening fan loyalty.

The increasing adoption of cloud-based analytics platforms is also playing a pivotal role in the expansion of the sports analytics market. Cloud deployment offers scalability, cost-efficiency, and seamless access to advanced analytical tools, making it an attractive option for sports organizations of all sizes. The shift toward cloud solutions is enabling real-time data sharing and collaboration among stakeholders, from coaches and athletes to sports scientists and business managers. This technological advancement is not only democratizing access to sophisticated analytics but also accelerating the pace of innovation and adoption across different sports disciplines, from football and basketball to cricket and esports.

The emergence of Digital Twin Sports Analytics is revolutionizing the way sports organizations approach performance optimization and strategic planning. By creating virtual replicas of athletes, teams can simulate various scenarios and analyze the potential outcomes of different strategies in a risk-free environment. This innovative approach allows for a deeper understanding of player dynamics and team interactions, enabling coaches to make data-driven decisions that enhance performance and reduce injury risks. As the technology advances, Digital Twin Sports Analytics is expected to play a pivotal role in transforming traditional sports management practices, offering unprecedented insights into player development and game strategy.

Regionally, North America continues to dominate the sports analytics market, accounting for over 38% of the global revenue in 2024, followed by Europe and Asia Pacific. The presence of major sports leagues, high investment in sports technology, and a mature digital infrastructure are key contributors to North AmericaÂ’s leadership position. However, the Asia Pacific region is witnessing the fastest growth, driven by the rising popularity of sports, increasing investments in professional leagues, and a growing emphasis on athlete development and fan engagement.

UIUC-Sports dataset

There are millions of sports fans across the United States, from those religiously following an NFL team to avid tennis fans or those who watch every Formula One Grand Prix. During an April 2023 survey in the United States, 44 percent of male respondents stated that they were avid sports fans. Meanwhile, this figure was just 15 percent among female respondents.

National Football League fans in the U.S.

Football is a widely enjoyed sport in the United States, as is evident from the millions of fans who tune in to watch their favorite teams compete every Monday night. The sport enjoys a diverse viewer demographic, with more than two thirds of white, Hispanic, and Black participants in an online survey identifying as either an avid or casual fan of football in January 2023. The survey also investigated the level of interest in the NFL in the U.S. broken down by gender, with a significantly larger share of men identifying as avid fans of the sport than women.

Women’s professional sports fans in the U.S.

Women’s professional sports viewership in the U.S. has grown significantly in recent years, helped at least partially by tournament victories across a wide range of sporting categories. When asked about the reason behind their interest in women’s sport in the U.S., nearly a third of respondents highlighted international events such as the Olympics and the FIFA World Cup as primary motivators for their interest. Meanwhile, when asked about the role of advertisers in promoting the growth of women’s sport in the U.S., more than half of survey participants believed that media agencies had a responsibility to do so.

In 2025, the global sports analytics market is projected to reach approximately USD 6,002.4 million, with expectations to grow to around USD 36,204.9 million by 2035, reflecting a Compound Annual Growth Rate (CAGR) of 22.1% during the forecast period.

Metric	Value
Market Size in 2025	USD 6,002.4 Million
Projected Market Size in 2035	USD 36,204.9 Million
CAGR (2025 to 2035)	22.1%

Country-wise Outlook

Country	CAGR (2025 to 2035)
USA	22.5%

Country	CAGR (2025 to 2035)
UK	21.8%

Region	CAGR (2025 to 2035)
EU	21.6%

Country	CAGR (2025 to 2035)
Japan	22.1%

Country	CAGR (2025 to 2035)
South Korea	22.0%

Competitive Outlook

Company/Organization Name	Estimated Market Share (%)
Catapult Sports	18-22%
Hudl (Agile Sports Technologies)	14-18%
IBM Corporation	12-16%
Sportradar AG	10-14%
Stats Perform	8-12%
Others	26-32%

Comprehensive dataset containing 15,919 verified Sports complex businesses in United States with complete contact information, ratings, reviews, and location data.

The global Sports Data API Interface market is booming, projected to reach $1.06 billion by 2033. Discover key trends, leading companies (Sportradar, Genius Sports), and regional market shares in this in-depth analysis. Learn how AI, esports, and evolving regulations are shaping the future of sports data.