🏏 Pakistan Cricket Team - Player Performance & Fitness Analysis

🎯 A comprehensive Python data analysis project that processes and analyzes player performance metrics for the Pakistan Cricket Team using pure Python - no external libraries required!

Keywords: cricket-analytics sports-data-science python-data-analysis fitness-tracking player-statistics beginner-project jupyter-notebook pure-python educational-project data-science-tutorial

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📊 Project Overview

This project analyzes 30 cricket players across various metrics including:

• Batting Performance: Averages and strike rates
• Fitness Levels: Fitness scores and risk assessment
• Player Roles: Batsmen, Bowlers, All-Rounders, and Wicket-Keepers
• Feedback Analysis: Performance evaluations

🎯 Features

Data Processing

• Parse raw player data strings into structured Python dictionaries
• Type conversion and data validation
• Clean data structure for analysis

Statistical Analysis

• Player Counts: Total players and role distribution
• Fitness Metrics: Average fitness by role, low fitness identification, risk tagging
• Batting Analysis: Highest/lowest averages, strong batsmen identification
• Strike Rate Analysis: Players above threshold, improvement projections
• Feedback Distribution: Count and categorization of performance feedback

Advanced Features

• Fitness grade assignment (A/B/C/D scale)
• 10% strike rate improvement simulation
• Role balance verification
• Undervalued performer identification
• Player summary cards generation

📁 Project Structure

Lab_01/
│
├── Task_01.ipynb          # Main analysis notebook
├── data.txt               # Raw player data
├── README.md              # Project documentation
└── .gitignore            # Git ignore rules

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• Jupyter Notebook or JupyterLab

Installation

1. Clone the repository:

git clone https://github.com/ZohaibCodez/pakistan-cricket-performance-analysis
cd pakistan-cricket-performance-analysis

2. Open the notebook:

jupyter notebook Task_01.ipynb

3. Run all cells sequentially to reproduce the analysis

📝 Analysis Questions Covered

The notebook addresses 20 comprehensive analysis questions:

1. Player Record Conversion - Convert raw data to structured dictionaries
2. Count Total Players - Dataset size analysis
3. Role-wise Fitness Summary - Average fitness by player role
4. Low Fitness Identification - Players with fitness < 75
5. Highest Batting Average - Top batting performer
6. Lowest Batting Average - Bottom batting performer
7. Strike Rate Above 95 - High strike rate players
8. Unique Roles Count - Distinct role identification
9. Feedback Type Count - Performance feedback distribution
10. Overall Team Batting Average - Team-wide batting statistics
11. Fitness Grade Assignment - A/B/C/D grading system
12. Strike Rate Improvement - 10% improvement projection
13. Role Summary - Player count and fitness by role
14. Strong Batsmen - Batsmen with average > 50
15. Fitness Risk Tag - High Risk vs OK classification
16. Sort by Batting Average - Ranked player list
17. Undervalued Performers - High average but non-excellent feedback
18. Feedback Normalization - Lowercase conversion and counting
19. Role Balance Check - Team composition verification
20. Player Summary Cards - Individual player reports

🔍 Key Insights

• Team Size: 30 players analyzed
• Role Distribution: Balanced mix of batsmen, bowlers, all-rounders, and wicket-keepers
• Fitness Levels: Identifies players requiring fitness attention
• Performance Leaders: Highlights top and bottom performers
• Team Balance: Verifies adequate role distribution

🛠️ Technologies Used

• Python 3.x - Core programming language
• Jupyter Notebook - Interactive development environment
• Built-in Python Libraries:
  • typing - Type hints for better code clarity

📊 Data Format

Each player record contains:

• id: Unique player identifier
• name: Player name
• role: Position (Batsman/Bowler/All-Rounder/Wicket-Keeper)
• avg: Batting average
• strike_rate: Strike rate percentage
• fitness: Fitness score (0-100)
• feedback: Performance evaluation

📈 Sample Output

# Example player dictionary
{
    'id': 1,
    'name': 'Babar Azam',
    'role': 'Batsman',
    'avg': 58.4,
    'strike_rate': 88.0,
    'fitness': 92.0,
    'feedback': 'Excellent'
}

🤝 Contributing

This is an academic project. Feedback and suggestions are welcome!

📄 License

This project is created for educational purposes as part of the Introduction to Data Science (IDS) course.

👤 Author

Created as part of Uni Third Semester - IDS Lab Assignment

🌟 Why This Project?

• ✅ Pure Python Implementation - Learn without library dependencies
• ✅ Real-World Dataset - Actual cricket player statistics
• ✅ 20 Analytical Questions - Comprehensive problem-solving
• ✅ Beginner-Friendly - Clear explanations for each step
• ✅ Portfolio-Ready - Professional documentation and structure

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

How to Contribute:

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

⭐ Show Your Support

Give a ⭐️ if this project helped you learn something new!

📞 Connect

• GitHub: @ZohaibCodez
• LinkedIn: Zohaib Khan
• Email: itxlevicodez@gmail.com

🙏 Acknowledgments

• Pakistan Cricket Team for inspiring the dataset
• Introduction to Data Science (IDS) Course
• Open-source community for resources and inspiration

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