About This Dataset: Explore the dynamic world of international cricket with this comprehensive dataset featuring players from A to Z. Dive into the rich details of each player, including their birthdates, country of origin, and performance statistics in Test, ODI, and T20 formats. Whether you're a cricket enthusiast, analyst, or simply curious about the global cricket landscape, this dataset provides a valuable resource for understanding the diverse profiles of cricket players across different nations. Uncover trends, compare player performances, and gain insights into the fascinating world of cricket through this meticulously curated dataset. 🌐🏏

Key Features

How to Use This Dataset:

1. Exploring Player Profiles:

   • Use the "Name" column to identify specific players.
   • Utilize "Date_Of_Birth" for understanding the age of each player.
   • Explore "Country" to analyze the diversity of players from different nations.

2. Analyzing Performance Statistics:

   • Examine "Test," "ODI," and "T20" columns to understand the number of matches played in each format.
   • Identify trends by comparing statistics across players or countries.

3. Filtering Data:

   • Use filtering mechanisms to focus on specific subsets of players based on criteria such as country, age, or playing format.

4. Missing Data Handling:

   • Note that "N/A" in "ODI" and "T20" columns indicates that the player hasn't played matches in these formats.

5. Visualizations:

   • Create visualizations, such as bar charts or scatter plots, to represent player distributions, age trends, or performance comparisons.

6. Statistical Analysis:

   • Conduct statistical analyses to identify patterns or correlations between variables.

7. Contributions and Feedback:

   • If you find interesting insights or have suggestions for improvement, consider contributing your analyses or providing feedback.

8. Acknowledgments:

   • If you use this dataset in your work, acknowledge the source and any applicable terms of use.

Akshitha-M/cricket-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is a collection of images from the internet, played really amazing player (So, can hope for perfection in shots).

Here are a few use cases for this project:

1. Sports Training & Improvement: Coaches or players can use the images from the "Cricket" model to study cricket playing styles, strategies, and techniques. The model can identify cricket equipment, players, and positions helping sportspersons analyze game practices.

2. Sports Journalism & Broadcasting: The model can be used by sports broadcasting networks to automatically analyze and tag certain moments of a cricket match, such as a player's stance, delivery style, or field settings. This can provide real-time insights and stats during live broadcast.

3. E-commerce: Online sports retailers can use this model to create more accurate items' descriptions, tag their cricket product images for easier searchability, and improve user experience.

4. Gaming and Virtual Reality: Computer game developers can use this model to create more realistic and detailed cricket games. The AI model can help model the movements of players, the trajectory of the cricket ball, and other nuances of the sport.

5. Security and Surveillance: In stadiums or sports facilities, the model can be used to monitor crowd behavior during a cricket match assisting security personnel's activities. It can detect any potential unauthorized field intrusions or unwanted activities.

Note: Please consider the data example given, it mentions a blurry image of a group of fish, which doesn't align with the described use cases. It seems like it belongs to a different dataset. Please verify and provide correct data samples.

lallantop/cricket dataset hosted on Hugging Face and contributed by the HF Datasets community

Here I present the data and code that has been used to study the statistical evolution of ODI cricket. The preprint for this research is available at:

https://doi.org/10.48550/arXiv.2406.11652

Focus to learn more

Datasets Mole Cricket

## Overview

Datasets Mole Cricket is a dataset for object detection tasks - it contains Mole Cricket annotations for 250 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).

UPVOTE is appreciation!

2024 Ball by Ball Dataset

SA20 2025 | SA20 2024 | SA20 2023

ILT20 2025 | ILT20 2024 | ILT20 2023

BGT 2024-25

IPL 2024

All 2024 Cricket Series ball by ball dataset available

Any Queries or requirements, Please feel free to share them with me!!

Author - Sahil Tailor

bhuvaneshprasad/odi-cricket-dataset-1971-2014 dataset hosted on Hugging Face and contributed by the HF Datasets community

samhitmantrala/cricket dataset hosted on Hugging Face and contributed by the HF Datasets community

nirmalkumar/cricket-commentary dataset hosted on Hugging Face and contributed by the HF Datasets community

The ICC Men's T20 World Cup first took place in 2007 and has been held on a two or four-year basis ever since. The West Indies, England, and India are the most successful teams in the history of the tournament, having all lifted the trophy on two occasions. India won the most recent T20 World Cup in 2024, beating South Africa in the final.

The videos have been used in the training and testing of model for detecting Legal Balls, and Wide Balls. Some videos are already augmented with flipping rotation in horizontal direction. The dataset ahs been split in train and test making it easier to use. The videos format is mp4.

Underlying Github Repo: https://github.com/soodoku/get-cricket-data HTML Files

251 images of playing cricket, football & baseball.

Original Dataset from Kaggle - Bikram Saha

This dataset contains 252 images of playing cricket, football, and baseball. (1) cricket - 95 images (2) football - 77 images (3) baseball - 79 images

This is a dataset for image classification in sports. This model will help to identify if the sport or activity occurring in the image or video feed is, or most closely resembles, cricket, football, or baseball.

The raw image versions (v1 or v5) of the dataset can be downloaded, or the entire dataset can be cloned, to your own project for image classification, or to label the figures in the images for object detection, instance or semantic segmentation, etc.

Dataset

This dataset was created by MKRISHNAHARI

Released under MIT

Contents

Cricket

## Overview

Cricket is a dataset for classification tasks - it contains Players annotations for 6,929 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).

Real Time Detection Of Cricket

## Overview

Real Time Detection Of Cricket is a dataset for object detection tasks - it contains Ball annotations for 9,907 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).

Cricket Analysis Software Market Outlook

In 2023, the global cricket analysis software market size was valued at approximately USD 1.2 billion and is projected to grow to around USD 3.5 billion by 2032, registering a compound annual growth rate (CAGR) of approximately 12.5% during the forecast period. The primary growth factor driving this market is the increasing demand for data-driven decision-making in sports to enhance player and team performance.

The significant growth in the cricket analysis software market is largely driven by the increasing adoption of advanced technologies in sports. Cricket teams worldwide are increasingly relying on data analytics to gain a competitive edge. The inclusion of detailed performance metrics and real-time analysis helps coaches and players make informed decisions, thus improving their game strategies and overall performance. This growing reliance on data-driven insights is a crucial factor contributing to the market's expansion.

Another critical growth factor is the rising popularity of cricket globally. Cricket is no longer confined to just a few countries; it has garnered a substantial following in regions such as North America and parts of Europe. This expansion has led to increased investments in cricket infrastructure, including training facilities equipped with the latest analytical software. Furthermore, the advent of various cricket leagues and tournaments has amplified the need for advanced performance analysis tools, thereby driving market growth.

Technological advancements and innovations in software capabilities are also playing a significant role in market growth. Modern cricket analysis software offers features such as high-definition video analysis, 3D visualization, and predictive analytics. These sophisticated tools enable a more comprehensive analysis of player techniques and team strategies. The integration of artificial intelligence (AI) and machine learning (ML) in these software solutions is further enhancing their effectiveness, making them indispensable for professional and amateur teams alike.

From a regional perspective, the Asia-Pacific region holds a substantial market share, primarily due to the enormous popularity of cricket in countries like India, Australia, and Pakistan. The region is also experiencing rapid technological advancements and increased investments in sports infrastructure. North America and Europe are emerging markets, showing significant potential due to the growing interest in cricket and the adoption of advanced analytical tools. These regions are expected to witness robust growth rates over the forecast period.

Cricket and Field Hockey share a rich history and cultural significance in many regions around the world. Both sports have evolved significantly over the years, with cricket often being considered a gentleman's game, while field hockey is known for its fast-paced and dynamic nature. The strategic elements inherent in both sports have led to the adoption of data analytics to enhance performance and strategy. As cricket continues to grow globally, field hockey is also seeing a resurgence in popularity, particularly in countries where it has been a traditional sport. The use of technology in these sports is not only improving player performance but also enriching the spectator experience by providing deeper insights into the games.

Component Analysis

The cricket analysis software market is segmented into Software and Services. The Software segment includes various types of analysis tools and platforms designed to collect and interpret data related to player and team performance. These software solutions offer a range of features from basic statistical analysis to advanced machine learning algorithms capable of predicting player performance and match outcomes. The growing demand for such sophisticated tools is a significant driver for this segment, as teams seek to gain a competitive edge through data-driven insights.

Within the Software segment, real-time data analytics is becoming increasingly popular. This involves the use of high-speed cameras, sensors, and other data collection devices to provide instantaneous feedback during matches and training sessions. Real-time data allows coaches and players to make immediate adjustments, thereby enhancing performance. The continuous evolution of software technologies, including the integration of AI and ML, is expected to further propel the growth of this