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

The dataset contains year- and match-wise historical data on each match played in all the world cups since 1975. The specifics of data contained of each match includes year in which world cup was held, venue, first and second batting teams, their scores, results, winners, winning margins by number of runs or wickets, types of match, such as league match, quarter finals, semi finals, finals, etc, along with names of host country and season winner.

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 Tracking

## Overview

Cricket Tracking is a dataset for object detection tasks - it contains Person Ball annotations for 1,140 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.

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).

Context

The ICC Champions Trophy, is one of cricket's premier limited-overs tournaments, played a pivotal role in international cricket from its inception in 1998 until its last edition in 2025. Originally known as the ICC KnockOut, the tournament was designed to bring together the world's leading cricketing nations in a competitive, yet entertaining format. This data provides the relevant information of all ICC Champions Trophy matches played played between 1998 to 2025. This could be used to predict results of upcoming matches, draw meaningful insights from data such as which all factors majorly contribute to the success of any team.

Content

The data is collected through web-scrapping of different sections of Cricinfo's official website.

Dataset Information

The dataset has 2 different sections -

1. all_champions_trophy_matches_results - This dataset has 19 different attributes.

• Team1 (string): Team 1 of the match
• Team2 (string): Team 2 of the match
• Toss (string): Team winning the toss, and whether decided to bat or field first
• Match days (string): Either 'day' or 'daynight' match
• Winner (string): Winner of Team 1 vs Team 2. If result ended in a tie, then the value is 'tied', and if that match was abandoned, then the value is 'no result'
• Player of the Match (string): Player of the Match of a particular content. If match was abandoned, value would be null
• Margin (string): Margin of victory of Winner, it could be either in runs or wickets
• Ground (string): Ground at which the match was played
• Match Date (string): Date when the match was played
• ODI Int Match (string): ODI International Match No.
• Team1 Avg Batting Ranking (number): Batting average of all participating players of Team1 in a particular CT edition one-day prior to the match played. If any player is not included in the Top 100 of ICC Men's ODI Batting Ranking, the player is assigned a ranking of 101 to maintain data consistency and avoid missing values. Data is web-scraped through https://www.relianceiccrankings.com/datespecific/odi/batting/2024/07/06/ (for example).Accurate data is scraped by inputting date equal to the day prior of the contest in the url.
• Team2 Avg Batting Ranking (number): Batting average of all participating players of Team2 in a particular CT edition one-day prior to the match played. If any player is not included in the Top 100 of ICC Men's ODI Batting Ranking, the player is assigned a ranking of 101 to maintain data consistency and avoid missing values. Data is web-scraped through https://www.relianceiccrankings.com/datespecific/odi/batting/2024/07/06/ (for example).Accurate data is scraped by inputting date equal to the day prior of the contest in the url.
• Team1 Avg Bowling Ranking (number): Bowling average of all participating players of Team1 in a particular CT edition one-day prior to the match played. If any player is not included in the Top 100 of ICC Men's ODI Bowling Ranking, the player is assigned a ranking of 101 to maintain data consistency and avoid missing values. Data is web-scraped through https://www.relianceiccrankings.com/datespecific/odi/bowling/2024/07/06/ (for example).Accurate data is scraped by inputting date equal to the day prior of the contest in the url.
• Team2 Avg Bowling Ranking (number): Bowling average of all participating players of Team2 in a particular CT edition one-day prior to the match played. If any player is not included in the Top 100 of ICC Men's ODI Bowling Ranking, the player is assigned a ranking of 101 to maintain data consistency and avoid missing values. Data is web-scraped through https://www.relianceiccrankings.com/datespecific/odi/bowling/2024/07/06/ (for example).Accurate data is scraped by inputting date equal to the day prior of the contest in the url
• Team1 Total CTs participated (number): Total participation in CTs by Team1. Data recorded till one day before the start of each CT
• Team1 Total CTs won (number): Total CTs editions won by Team1. Data recorded till one day before the start of each CT
• Team2 Total CTs partipated (number): Total participation in CTs by Team2. Data recorded till one day before the start of each CT
• Team2 Total CTs won (number): Total CTs editions won by Team2. Data recorded till one day before the start of each CT
• Team1 win % over Team2 (number): Team1 W (Win)/L (Loss) ratio Team2 leading upto that match including all ODI Int matches. This data is recorded manually using Cricinfo's popular Statsguru (https://stats.espncricinfo.com/ci/engine/stats/index.html)

2.**all_champions_trophy_players_list** - This dataset has 3 different attributes. This data gives information about the team's (in ascending order, hence starting with Australia) which participated in a particular edition of the CT, along with the player's tak...

Content

These four datasets were created by scraping data from the ESPN Cricket Info website using beautifulsoup and urllib, each containing data on around 450 batsmen, bowlers,fielders and all rounders who've played in T20 matches.

Acknowledgements

This data comes from https://stats.espncricinfo.com/ci/engine/records/index.html

Questions to Answer

1.Best+Most Promising players in each category? 2.Suppose we are data analysts employed by a certain franchise cricket team. Which players should we tell our representatives to target at the auctions, assuming that all of the listed players are participating?

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

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).

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).

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

My Cricket

## Overview

My Cricket is a dataset for object detection tasks - it contains Ball annotations for 287 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

this graph was created in R :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ffd90736223cc5572985e7a2153c51327%2Ffoto3.png?generation=1740349164551931&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F87be218db233c41e5a4260c8f24a9c80%2Fgif2.gif?generation=1740349170058731&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F03d6822c7ea63bcb99b339fd51d2168d%2Fgif1.gif?generation=1740349175430653&alt=media" alt="">

This dataset provides comprehensive information on more than 2400 One Day International (ODI) cricket matches obtained from Cricsheet and includes detailed batting and bowling statistics match summaries and individual player performances with the exception of matches involving Afghanistan’s men’s team or those played in the Afghanistan Premier League due to Cricsheet’s data policy making it an excellent resource for sports analytics machine learning and cricket strategy modeling allowing users to analyze player consistency evaluate team performance predict fantasy cricket outcomes and assess match results the dataset is divided into several files including batter_player_stats.csv which contains batting data such as total runs strike rate matches played and player of the match awards bowler_player_stats.csv which offers bowling data including total wickets economy rate overs bowled and matches played as a bowler detailed_player_data.csv which provides per-match player performance data such as runs scored balls faced wickets taken catches and fantasy points and match_summary.csv which includes match-level information such as toss results match outcomes either by runs or wickets player of the match and venue details potential use cases include player performance analysis to identify the most consistent batters and bowlers across various seasons match outcome prediction by developing models that leverage historical performance data fantasy cricket strategy optimization by selecting teams based on previous player performance and cricket analytics and visualization to explore trends in runs wickets and match-winning performances enabling deeper insights into the game and supporting advanced sports research and data-driven decision-making.

This project uses a Raspberry Pi equipped with the official Pi Camera and a custom-trained YOLOv8 nano model to detect crickets in real time. It leverages picamera2, OpenCV, and the ultralytics library to capture video frames, run inference, and display bounding boxes over detected insects. After detection, a mechanical flipper arm—similar to those found in pinball machines—is triggered to sort the crickets into two separate boxes based on their predicted sex (male or female).

The dataset contains batting details with respect to all the matches played in ICC Men's Champions Trophy Cricket since 1998. The details include batsman name, country, match type, batting position, runs scores, balls faced, strike rate, number of dots, singles, doubles, triples, fours, and sixes hit, etc.

Note: 1) The name was changed to ICC Champions Trophy in 2002. Before that it was called ICC KnockOut Trophy (or Wills International Cup in 1998). 2) All the values in the dataset are in absolute numbers. 3) The sum of individual batting scores in the dataset will not be equal to the total team score all the times as extras need to be added to the score

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