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).

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).

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

1) Data Introduction • The Cricket Player Debuts & Last Matches (4 Nations) Dataset is a comprehensive collection of debut times and final matches for cricketers from four countries in India, Australia, Pakistan, and Afghanistan, structuring career data by format, including Test/ODI/T20I/IPL.

2) Data Utilization (1) Cricket Player Debuts & Last Matches (4 Nations) Dataset has characteristics that: • This dataset, along with player name, team, and player ID, provides the debut opponent, date, location, and last match opponent, date and location for each format (Test/ODI/T20I/IPL) in columns. • It was collected in a programming manner through the Cricbuzz API and focuses on player information from four countries considering data completion. (2) Cricket Player Debuts & Last Matches (4 Nations) Dataset can be used to: • Player career pattern analysis: A career management strategy can be derived by comparing the age of debut by format, duration of activity, and frequency of format transitions (e.g., Test→T20) by country. • Cricket Distribution Network Dashboard: Visualization of career history for each player can be used for scouting, fan service, media content planning, and more.

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).

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

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).

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

Dataset Card for "llama-cricket-dataset"

More Information needed

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 ---

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.

Cricket Ball

## Overview

Cricket Ball is a dataset for object detection tasks - it contains Cricketball annotations for 218 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).

****Context**

The 2023 ICC Men's Cricket World Cup is the 13th edition of the Cricket World Cup, a quadrennial One Day International (ODI) cricket tournament contested by men's national teams and organized by the International Cricket Council (ICC). The tournament is being hosted by India, it started on 5 October and is scheduled to conclude on 19 November 2023. England are the defending champions, having won the 2019 edition by defeating New Zealand in the final at Lord's, London.

Reference: https://en.wikipedia.org/wiki/2023_Cricket_World_Cup

Content**

• Batting_Stats.csv: This file contains the batting statistics of each player in each match, such as the number of runs, balls, fours, sixes, strike rate, etc.
• Bowling_Stats.csv: This file contains the bowling statistics of each player in each match, such as the number of overs, runs, wickets, economy, etc.
• Best_Team.csv: This file contains the ranking of the best teams in the ICC World Cup 2023, based on their points, net run rate, wins, losses, etc.
• Point_Table.csv: This file contains the point table of the ICC World Cup 2023, showing the points, wins, losses, net run rate, etc. of each team in each group.
• Max_Catches.csv: This file contains the list of the players who took the maximum number of catches in the ICC World Cup 2023, along with their country and number of catches.
• https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15862256%2F8a1ed8c0ca7720bc4c4fb07e67d6b3f9%2FScreenshot%202023-11-29%20011129.png?generation=1701206180030200&alt=media" alt="">Partnership.csv: This file contains the list of the best partnerships in the ICC World Cup 2023, based on the runs, balls, and partnership rate.

Inspiration

Analyze the team's performance Make predictions using existing cricket datasets on the platform**

.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.

bhuvaneshprasad/t20i-cricket-dataset-2005-2014 dataset hosted on Hugging Face and contributed by the HF Datasets community

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