https://github.com/vvipjain/cricket-league-analysis

Cricket League Analysis
https://github.com/vvipjain/cricket-league-analysis

pandas pandas-dataframe pandas-library pandas-python plotly plotly-express plotly-python plotlyjs python python-3

Last synced: about 1 month ago
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Cricket League Analysis

• Host: GitHub
• URL: https://github.com/vvipjain/cricket-league-analysis
• Owner: VVipJain
• Created: 2024-08-04T10:55:44.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-15T09:05:16.000Z (almost 2 years ago)
• Last Synced: 2024-10-18T13:14:51.910Z (over 1 year ago)
• Topics: pandas, pandas-dataframe, pandas-library, pandas-python, plotly, plotly-express, plotly-python, plotlyjs, python, python-3
• Language: Jupyter Notebook
• Homepage:
• Size: 354 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# CRICKET LEAGUE ANALYSIS

This repository contains a comprehensive analysis of cricket league data using Python, Pandas, and Plotly. The goal of this project is to provide insights into various aspects of the league, such as win distribution, player performance, and match statistics, through interactive visualizations.

 

# INTRODUCTION

In this project, we analyze cricket league data to uncover patterns and trends. We utilize Pandas for data manipulation and cleaning, and Plotly for creating interactive and visually appealing charts and graphs. This project is aimed at cricket enthusiasts, data analysts, and anyone interested in sports analytics.

# DATASET

The dataset used in this analysis contains detailed information about various cricket matches, including team names, player performances, match results, and more. The data is stored in a CSV file named matches.csv.

# ANALYSIS

The analysis is divided into several sections :

* Loading and Cleaning Data: Importing the dataset and performing initial cleaning operations such as handling missing values and converting data types.

* Data Exploration: Exploring the dataset to understand its structure, including summary statistics and unique values.

* Win Distribution: Analyzing how matches are won, either by defending a score or chasing a target.

* Player Performance: Evaluating the performances of players, including those awarded as 'Player of the Match'.

* Match Statistics: Analyzing other relevant match statistics such as venue performance, team performance, etc.

# VISUALISATION

We use Plotly to create interactive visualizations. Some of the key visualizations include :

* Pie Chart : Distribution of wins by defending vs. chasing.

![newplot(1)](https://github.com/user-attachments/assets/ec73d6a8-512a-46ad-b033-eb4dbecc6647)

* Bar Plots : Number of matches won by each team , best bowler , player of the match stats and highest run scorer's in individual matches

![newplot](https://github.com/user-attachments/assets/15230c62-5627-4973-8532-38a11f19c9b2)

![newplot(2)](https://github.com/user-attachments/assets/812cde5e-c8de-464a-b96c-38cca3923a06)

![newplot(3)](https://github.com/user-attachments/assets/050bf295-903d-408a-a6d6-3980deee5646)

![newplot(4)](https://github.com/user-attachments/assets/cc92eea5-0a28-4232-9406-666e53b30393)

* Line Charts : Trends over time, such as the first and second innings score and wkts on a particular day.

![newplot(5)](https://github.com/user-attachments/assets/2606f36a-7413-4bcf-afe8-643585f12983)

![newplot(6)](https://github.com/user-attachments/assets/e81c89d0-cf79-49b2-9649-cd21fc72b288)