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

Sports

## Overview

Sports is a dataset for object detection tasks - it contains Rugby_ball Valleyball annotations for 487 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 [MIT license](https://creativecommons.org/licenses/MIT).

Based on certain given features, we need to predict the status of football match, which consists of two categories won and lost.

Data columns (total 15 columns): # Column

1. home_team
2. away_team
3. id
4. market
5. competition_name
6. prediction
7. competition_cluster
8. status
9. federation
10. is_expired
11. season
12. result
13. start_date
14. last_update_at
15. odds

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.

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

This dataset contains match score data from major international competitions across 12 team ball sports: basketball, cricket, field hockey, futsal, handball, ice hockey, lacrosse, roller hockey, rugby, soccer, volleyball, and water polo. The dataset was obtained by web scraping data available on Wikipedia pages and includes, for each sport, the following information related to individual matches: the year of the competition edition when a match occurred, the names of the two opposing teams, their respective scores, and the name of the winning team.

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.

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

Dataset Card for Dataset Name

Original dataset can be found on: https://amazon-reviews-2023.github.io/

  Dataset Details

This dataset is downloaded from the link above, the category Sports and Outdoors meta dataset.

  Dataset Description

This dataset is a refined version of the Amazon Sports and Outdoors 2023 meta dataset, which originally contained product metadata for sports and outdoors products that are sold on Amazon. The dataset includes detailed information… See the full description on the dataset page: https://huggingface.co/datasets/smartcat/Amazon_Sports_and_Outdoors_2023.

Sports Balls Dataset

A labeled image dataset for sports ball classification, suitable for computer vision experiments and PyTorch/TensorFlow model training.

Number of classes: 15 (e.g., Football, Basketball, Tennis Ball, Volleyball, etc.) Data type: Images in RGB format Use case: Train and evaluate image classification models License: Public domain / CC0

  Load in Python

from datasets import load_dataset

dataset = load_dataset("AIOmarRehan/Sports-Balls")… See the full description on the dataset page: https://huggingface.co/datasets/AIOmarRehan/Sports-Balls.

naaviii/wan-sports-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

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

Comprehensive database of sports records, athlete statistics, and performance data spanning multiple sports and decades.

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.

Object Detection In Sports

## Overview

Object Detection In Sports is a dataset for object detection tasks - it contains Football Players annotations for 429 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).

This dataset covers the modern era of concrete and steel sports venues. It includes all venues opened in US and Canada from 1909 to 2026 that served as the main host for teams in the four major US-based professional sports leagues: Major League Baseball (MLB), National Basketball Association (NBA), National Football League (NFL), and National Hockey League (NHL).

Dataset

This dataset was created by Easypeasysaral

Released under CC0: Public Domain

Contents

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).

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.

Sports audience profile for United States.