Data and writeups from an analysis of winning the toss in cricket

Statistical and exploratory Analysis of Cricket Data

Analysis and visualisation of cricket data with ball tracking

The Cricket Analysis Dashboard in Power BI provides insights into match results, player stats, and team performance. It features interactive visuals for runs, wickets, strike rates, and comparisons. Filters allow analysis by tournament, year, and venue, enabling data-driven cricket strategies.

Exploratory data analysis of Indian Premier League (Cricket) Ball-By-Ball Cricket Data

Cricket statistics chatbot powered by a Large Language Model (LLM) that converts natural language prompts into efficient Pandas code. It enables users to query a cricket match dataset for player performance, bowling economy, and more. Built with Python, Pandas, and Streamlit for data analysis and visualization.

Cricket analysis on cricsheet.org data

Visualization and Analysis of Cricket game using Seaborn and Matplotlib

The T20 Cricket Data Analysis shows the Web Scraping and Data Cleaning through Python & Visualization through the Power BI.

Analysis of T-20 cricket data using Pyrhon

Exploratory Data Analysis (EDA) on IPL Dataset The Indian Premier League (IPL) is one of the most popular and competitive T20 cricket leagues in the world. With a vast amount of data available, performing an Exploratory Data Analysis (EDA) on the IPL dataset can provide valuable insights into the game, teams, and players.

IPL_ML – Playing_with_ML is your sandbox for experimenting with machine learning and data analysis techniques using IPL (Indian Premier League) datasets. Designed for hands-on learning, it serves as a flexible foundation for exploring cricket analytics through code, models, and insights.

Analysis of Cricket Matches dataset

A Java-based platform for comprehensive management and analysis of cricket player's data.

Cricket score prediction is the process of using statistical analysis, historical data, and machine learning techniques to estimate the possible score a team might achieve in a match.

Cricket prediction can be viewed as one of the objectives of sports analyt- ics, which aims at helping decision makers to gain competitive advantage. Data Analysis is becoming more common specially in Sports. Using data analysis results has become familiar in sports organization such as Interna- tional Cricket Council (ICC), International Federation of Association Foot- ball (FIFA), GrandSlam of the International Tennis Federation.The difculty of this task depends on many factors, like the availability of data for the past events, the ability to gather data for future events, the knowledge needed to interpret gathered data, and others. In sports like Cricket and Football predicting the results become more difcult due to the changing nature of the Game. People focus on sports results,which is an important aspect, and the result of the game has become the focus and concentration of sports game. Various techniques for modelling a cricket match exist that yield dif- ferent result prediction algorithms.The modelling can be put under the four generic categories which are empirical models,dynamic systems,statistical techniques,articial intelligence(including expert systems.In the articial intel- ligence category, there are several approaches that focus on Bayesian network modelling. The Matrix factorization technique became very popular in the eld of multimedia content recommender systems where it showed good scal- ability and predictive accuracy. The idea behind using the latent features in our case is to be able to build a successful model. The outcome of this project will be a system which will predict the results of the upcoming matches.

Work Integrated Learning Programmes Division M.Tech (Data Science and Engineering) (S1-19_DSECLZG519) (Data Structures and Algorithms Design) Academic Year 2019-2020 Assignment 2 – PS7 - [ Cricket Batting Order ] - [Weightage 13%] 1. Problem Statement For the upcoming world-cup, the Indian Cricket Selection Committee has to come up with a possible batting order for their players. Instead of using the traditional approach they have decided to use computer algorithms to come up with all the possible batting orders and then decide from that. The algorithm however requires the possible batting positions for each player. The algorithm takes a list of 11 players. Each player can have more than one position they can bat at. Your job for now is to help the selection committee calculate the total number of unique batting charts such that every player gets exactly one batting position from their list of positions and no two players are given the same batting position in one batting chart. Requirements: 1. Formulate an efficient algorithm using dynamic programming to perform the above task. 2. Analyse the time complexity of your algorithm. 3. Implement the above problem statement using Python 3.7 Input: Input should be taken in through a file called “inputPS7.txt” which has the fixed format mentioned below using the “/” as a field separator: Player <num> / < position 1> / < position 2> / < position 3>.... Ex: P1 / 1 / 2 / 3 / 4 P2 / 1 / 5 / 9 / 2 / 6 / 7 / 8 P3 / 1 / 2 / 7 / 10 / 3 P4 / 1 / 9 / 2 / 6 / 7 / 10 / 3 / 4P5 / 5 / 9 / 2 / 8 / 3 / 4 P6 / 1 / 5 / 3 / 6 P7 / 6 / 7 / 4 P8 / 1 / 9 / 2 / 4 P9 / 9 / 6 / 11 / 3 / 4 P10 / 1 / 5 / 9 / 7 / 8 / 4 P11 / 6 / 11 / 7 / 10 Note that the input data shown here is only for understanding and testing, the actual file used for evaluation will be different. Output: Syntax of the output should be: The total number of allocations possible is: <number of possible unique combinations> Ex: The total number of allocations possible is: 4646. Display the output in outputPS7.txt. 2. Deliverables • Word document designPS7_<group id>.docx detailing your algorithm design and time complexity of the algorithm. • Zipped AS2_PS7_CBO_[Group id].py package folder containing all the modules classes and functions for the employee node, binary tree and the main body of the program. • inputPS7.txt file used for testing • outputPS7.txt file generated while testing 1. Instructions a. It is compulsory to make use of the data structure/s mentioned in the problem statement. b. It is compulsory to use Python 3.7 for implementation. c. Ensure that all data structures and functions throw appropriate messages when their capacity is empty or full.d. For the purposes of testing, you may implement some functions to print the data structures or other test data. But all such functions must be commented before submission. e. Make sure that your read, understand, and follow all the instructions f. Ensure that the input, prompt and output file guidelines are adhered to. Deviations from the mentioned formats will not be entertained. g. The input, prompt and output samples shown here are only a representation of the syntax to be used. Actual files used to test the submissions will be different. Hence, do not hard code any values into the code. h. Run time analysis is provided in asymptotic notations and not timestamp based runtimes in sec or milliseconds. 2. Deadline a. The strict deadline for submission of the assignment is 16 th Feb, 2020. b. The deadline is set for a month from the date of rollout to accommodate for the semester exams. No further extension of the deadline will be entertained. c. Late submissions will not be evaluated. 3. How to submit a. This is a group assignment. b. Each group has to make one submission (only one, no resubmission) of solutions. c. Each group should zip the deliverables and name the zipped file as below “ASSIGNMENT1_[BLR/HYD/DLH/PUN/CHE]_[G1/G2/...].zip” and upload in CANVAS in respective location under ASSIGNMENT Tab. d. Assignment submitted via means other than through CANVAS will not be graded. 4. Evaluation a. The assignment carries 13 Marks. b. Grading will depend on a. Fully executable code with all functionality b. Well-structured and commented code c. Accuracy of the run time analysis and design document c. Every bug in the functionality will have negative marking.d. Source code files which contain compilation errors will get at most 25% of the value of that question. 5. Readings Text book: Algorithms Design: Foundations, Analysis and Internet Examples Michael T. Goodrich, Roberto Tamassia, 2006, Wiley (Students Edition)

Predict a cricket match ball by ball. Simulation of a cricket match using Big Data analysis.

Cricket-Data-Analysis – A cricket analytics dashboard that visualizes match and player statistics for deeper insights and performance analysis.

A data analysis project that explores cricket match data to uncover patterns, player performance, and team statistics using Python. Includes data cleaning, visualization, and insights generation.

A basic step to find out that how analysis and visualization of Data Works.

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Data Analysis of All Cricket World Cup Centuries Using Machine Learning with Google Colab

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Cricket Players Expletory Data Analysis and Machine learning model to detect winning players by using cricket players dataset

A Streamlit-based analysis of ODI vs World Cup player performances using real cricket data.

I used international ODI Cricket data and Python Programming Language to perform some various data analysis tasks to answer one of the biggest question in Cricket Field whether Sachin is better or Virat Kohli ?

🏏📊🔍"Explore cricket's depths with IPL Data Analysis. Unveil player performance, team strategies, collaborations' impact, historical trends, and global fan engagement. Engage cricket fans, analysts, and curious minds.