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

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).

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.

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).

Dataset

This dataset was created by Vallabh Kulkarni

Released under CC0: Public Domain

Contents

Here are a few use cases for this project:

1. Sports Analysis Tool: The "sports ball" computer vision model could be used in a variety of sports analysis tools. These tools could automatically track the ball during a game, assessing player strategies, speed, and overall game dynamics.

2. Game Highights Creation: The model could be used to automate the creation of game highlights. By recognizing when and how a sports ball is used in action, it could automatically identify the key moments of a game.

3. Sports Equipment Inventory Management: The model can be utilized for inventory management in sports stores by automatically identifying different types of sports balls in storage.

4. Real-time Match Statistics: The model can be used in real-time applications, providing statistics on ball possession, passes, shots and goals during live sports broadcasts.

5. Sports-themed Video Games: The model could be used to design smarter, more realistic sports-themed video games. This could allow for dynamic play and more interactive gaming experiences.

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).

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).

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.

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 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.

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.

kcrl/Sports-Comment 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.

Discover the explosive growth of the cloud-based sports analytics market. This comprehensive analysis reveals key trends, market size projections (reaching $7.7B by 2033!), regional breakdowns, and leading companies driving innovation in player performance, fan engagement, and broadcast management. Learn how data is revolutionizing the sports industry.

USA Sports Database: 112,189+ verified business email contacts across all 50 US states. Decision-makers: 38%. Last verified Q2 2026.

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 TTv9129

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.