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.

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.

Cricket Dataset V1

## Overview

Cricket Dataset V1 is a dataset for object detection tasks - it contains Game States annotations for 1,920 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).

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

Dataset Card for "llama-cricket-dataset"

More Information needed

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

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

BGT Cricket Players Tracking

## Overview

BGT Cricket Players Tracking is a dataset for object detection tasks - it contains Players annotations for 430 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 IPL Dataset comprises cricket statistics and player performance data, encompassing various key metrics recorded during matches. The dataset includes information such as the match ID (mid), date, and venue where the match took place, providing context for each data entry. Additionally, it contains details of the batting and bowling teams as well as individual player data such as batsman and bowler names. The dataset records the runs scored by batsmen, the wickets taken by bowlers, and the number of overs bowled. These metrics offer insights into match dynamics, player contributions, and team strategies throughout the tournament. Analysts and enthusiasts can leverage this dataset to analyze player performance, assess team strengths and weaknesses, and derive valuable insights into the game of cricket.

nitv/cricket-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Analysis of ‘Cricket data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mahendran1/icc-cricket on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Any aspiring datascientist will look everything in view of data. Even when chilling with friends, watching cricket live and cheering for the favorite team.

Content

It includes ODI, Test, t20 statistics of all the players in all the three category (batting ,bowling and fielding).

Acknowledgements

We wouldn't be here without the help of cricket. Thank you for all the great cricketers for the wonderful contribution.

--- Original source retains full ownership of the source dataset ---

rishika0704/cricket dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

The "most run in cricket" dataset for September 2023 provides a detailed and up-to-date account of the most runs scored by cricketers across various formats of the game, including Test matches, One Day Internationals (ODIs), and Twenty20 Internationals (T20Is). This dataset covers the top 90 highest run scorers of all time.

Content

This dataset contains the top 90 players from international cricket who have scored the most runs in international cricket.

Data Columns and dictionary - Player( Cricketer's name) - Span(Players playing duration) - Mat(Matches) - Inns(Innings) - No(Not out) - Runs(Run scored) - HS(Highest run scored) - Ave( Average run) - BF( Balls faced) - SR(Batting strike rate) - 100( No. of time hundred scored) - 50(No. of time fifty scored) - 0(Ducked scored) - 4s(No. of boundary 4s) - 6s(No. of boundary 6s) - Country( Name of the country)

Acknowledgements This data was scrapped from espncricinfo.com

Inspiration This dataset is a testament to the dynamic nature of cricket and its athletes' relentless pursuit of excellence. This dataset provides an invaluable resource for cricket enthusiasts, and data analysts to explore and celebrate the remarkable run-scoring achievements of cricketers in this exciting era of the game.

Cricket

## Overview

Cricket is a dataset for object detection tasks - it contains Objects annotations for 1,007 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).

Football Vs Cricket

## Overview

Football Vs Cricket is a dataset for object detection tasks - it contains Football And Cricket Ball annotations for 868 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).

As of 2025, India had played the most all-time games in the ICC Champions Trophy cricket tournament. The three-time title winners played 33 games across nine editions of the ICC event.

Ball by ball data for cricket test matches between 1998 and 2006 inclusive in which a target was set for the team batting last. These are secondary data.

.CRICKET Whois Database, discover comprehensive ownership details, registration dates, and more for .CRICKET TLD with Whois Data Center.

This dataset provides empirical data on the impact of wearing cricket protective gear on agility and sprint performance among competitive cricket players. The study was conducted using two standardized tests: the New Multi-Change of Direction Agility Test (NMAT) and the Bangsbo Sprint Test, with performance recorded both with and without cricket gear. The dataset includes measurements from 144 male cricket players, categorized into three age groups: Under-16 (U16), Under-18 (U18), and Under-23 (U23). Key attributes include demographic details (age, height, weight, BMI), test performance times, and dominant hand preference. This dataset can be used for sports analytics, machine learning-based performance prediction, and optimizing training methodologies for cricket players.

Keywords: Cricket performance, agility, sprint test, protective gear, NMAT, Bangsbo Sprint Test, machine learning in sports, athlete performance analysis

Dataset Information: Subjects: 72 male competitive cricket players Age Groups: U16, U18, U23 Tests Conducted: NMAT (agility), Bangsbo Sprint Test (sprint performance) Conditions: With and without protective cricket gear Variables Included: Age, height, weight, BMI, NMAT times, Bangsbo sprint times, dominant hand, and player division

Column Descriptions: Age Group: U16, U18, U23 categories

Height (cm): Player's height in centimeters

Weight (kg): Player's weight in kilograms

BMI: Body Mass Index calculated from height and weight

NMATwithout Cricket Gears in sec: Agility test time without gear

NMATwith Cricket Gears in sec: Agility test time with gear

Bangsbo test wihout Cricket Gears in sec: Sprint test time without gear

Bangsbo test With Cricket Gears in sec: Sprint test time with gear

Methodology: Study Design: Cross-sectional study Testing Area: Cricket training facility with controlled conditions Equipment Used: Standard cricket gear (pads, gloves, helmet) Electronic timing gates for precise measurements

Procedure: Players completed NMAT and Bangsbo Sprint Test under both conditions (with/without gear). Each test was performed after a warm-up, with sufficient recovery time between trials to minimize fatigue. Performance times were recorded and analyzed.

Potential Research Applications: Sports Performance Analysis: Evaluating how wearing cricket gear influences speed and agility. Injury Prevention & Biomechanics: Understanding the potential risk of injury due to restricted mobility. Sports Equipment Optimization: Informing the development of lighter, performance-friendly cricket gear. Machine Learning for Sports Analytics: Predicting performance outcomes using AI-driven models.