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.

Cricket

## Overview

Cricket is a dataset for object detection tasks - it contains Ball annotations for 7,628 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).

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

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).

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

Dataset

This dataset was created by MKRISHNAHARI

Released under MIT

Contents

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

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

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

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 Match Performance Dataset Documentation

Dataset Overview

This dataset captures detailed performance metrics of two cricket teams during a fictional cricket match. It is designed for statistical analysis, data visualization, and predictive modeling in sports analytics. The dataset can be used for tasks such as player performance evaluation, team strategy analysis, and cricket match outcome prediction.

Dataset Structure

The dataset contains the following columns:

General Information

1. Match_ID: Unique identifier for the match.
2. Date: The date when the match took place.
3. Venue: The location where the match was played.

Team Information

1. Team_Name: The name of the team (Team A or Team B).
2. Total_Runs: Total runs scored by the team.
3. Wickets_Lost: Total number of wickets lost by the team.
4. Overs_Played: Total number of overs played by the team.

Player Performance Metrics

1. Player_Name: Name of the player.
2. Runs_Scored: Runs scored by the player.
3. Balls_Faced: Balls faced by the player.
4. Fours: Number of boundaries (4 runs) hit by the player.
5. Sixes: Number of sixes (6 runs) hit by the player.
6. Strike_Rate: Strike rate calculated as (Runs_Scored / Balls_Faced) * 100.
7. Wickets_Taken: Number of wickets taken by the player (if the player is a bowler).
8. Overs_Bowled: Total overs bowled by the player.
9. Maidens: Number of maiden overs bowled.
10. Economy_Rate: Runs conceded per over, calculated as (Runs_Conceded / Overs_Bowled).
11. Catches: Number of catches taken by the player.
12. Run_Outs: Number of run-outs contributed by the player.

Match Highlights

1. Highest_Scorer: Name of the player with the highest runs scored in the match.
2. Best_Bowler: Name of the bowler with the most wickets taken in the match.

File Format

The dataset is available in CSV format with comma-separated values. Each row represents an individual player’s performance metrics, along with team-level and match-level information.

Example Rows

Applications

This dataset can be used for: 1. Performance Analytics: Analyzing individual player performance across different matches. 2. Team Strategy: Understanding team strengths and weaknesses based on historical data. 3. Data Visualization: Creating plots for runs, wickets, economy rate, and more. 4. Predictive Modeling: Building machine learning models to predict match outcomes or player performances. 5. Fantasy Cricket: Assisting in creating optimal fantasy cricket teams based on player metrics.

Suggested Visualizations

Here are some visualizations you can create using this dataset: 1. Runs scored by each player (bar chart). 2. Strike rate distribution of players (histogram). 3. Total runs scored by each team (pie chart). 4. Economy rates of bowlers (box plot). 5. Match-wise total scores (line chart).

License

This dataset is shared under the CC BY 4.0 license. You are free to use, modify, and share the dataset with proper attribution.

Citation

If you use this dataset, please cite it as follows: Author: Arif Miah Title: Cricket Match Performance Dataset Platform: Kaggle Date: 2025-01-06

Feedback

For feedback or suggestions, please contact the dataset creator via Kaggle or email at [arifmiahcse@gmail.com].

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

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

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.

Cricket Balls

## Overview

Cricket Balls is a dataset for object detection tasks - it contains Balls annotations for 3,723 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).

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.

Headers

• match_id
• season
• start_date
• venue
• innings
• ball
• batting_team
• bowling_team
• striker
• non_striker
• bowler
• runs_off_bat
• extras
• wides
• noballs
• byes
• legbyes
• penalty
• wicket_type
• player_dismissed
• other_wicket_type
• Other_player_dismissed

Context

• Most of the fields above should, hopefully, be self-explanatory, but some may benefit from clarification
• "innings" contains the number of the innings within the match. If a match is one that would normally have 2 innings, such as a T20 or ODI, then any innings of more than 2 can be regarded as a super over.
• "ball" is a combination of the over and delivery. For example, "0.3" represents the 3rd ball of the 1st over.
• If a wicket occurred on a delivery then "wicket_type" will contain the method of dismissal, while "player_dismissed" will indicate who was dismissed.
• There is also the, admittedly remote, possibility that a second dismissal can be recorded on the delivery (such as when a player retires on the same delivery as another dismissal occurs). In this case "other_wicket_type" will record the reason, while "other_player_dismissed" will show who was dismissed.