This project focuses on analyzing T20 cricket data to determine the optimal 11-player combination for the T20 World Cup. The goal was to enhance team performance through data-driven player selection.
- Data cleaning and transformation using pandas
- Comprehensive player performance analysis
- Visualization of key cricket statistics
- Strategic insights for team composition
- Power BI
- Python (pandas)
- Jupyter Notebook
- Used pandas in Jupyter Notebook for data preprocessing and cleaning
- Ensured data accuracy and readiness for analysis
- Created a dashboard in Power BI to visualize key player statistics
- Batting averages across different match conditions
- Bowling performance metrics
- Player consistency and form analysis
- Optimal player combinations based on data
The dataset used for this analysis is available in this repository. You can find it here.
- Data Cleaning and Preprocessing (Python, pandas)
- Data Visualization (Power BI)
- Sports Analytics
- Statistical Analysis
- Dashboard Creation
- Strategic Decision-Making in Sports
- Implement machine learning models for player performance prediction
- Incorporate more historical data for trend analysis
- Develop a real-time updating system for live match data
Arshdeep Kaur - hello@arshdeepkaur.in