Pakistan Cricket Performance Analysis
Data Science with Pure Python
Comprehensive Jupyter notebook data analysis project analyzing 30 Pakistan Cricket Team players. Processes player performance metrics including batting averages, strike rates, fitness levels, and role-based analysis using pure Python - no external libraries. Features 20+ analytical questions covering statistical analysis, fitness risk assessment, performance ranking, and team balance verification. Educational project demonstrating fundamental data science concepts.
Why it mattered
30 players analyzed; 20 analytical questions answered; Pure Python implementation with no dependencies
What shipped
Comprehensive Jupyter notebook data analysis project analyzing 30 Pakistan Cricket Team players. Processes player performance metrics including batting averages, strike rates, fitness levels, and role-based analysis using pure Python - no external libraries. Features 20+ analytical questions covering statistical analysis, fitness risk assessment, performance ranking, and team balance verification. Educational project demonstrating fundamental data science concepts.
Proof signal
30 players analyzed; 20 analytical questions answered; Pure Python implementation with no dependencies
System flow
Raw Data -> Parse Records -> Statistical Analysis -> Role Analysis -> Fitness Assessment -> Performance Ranking.
Honest maturity snapshot
Raw datasets, disconnected metrics, and hard-to-scan findings.
A structured analysis or dashboard with clear KPIs, visual patterns, and decision-ready insights.
Features
- Python integrated into the workflow.
- Jupyter Notebook integrated into the workflow.
- Data Analysis integrated into the workflow.
- Sports Analytics integrated into the workflow.
- Raw Data stage documented in the delivery flow.
- Parse Records stage documented in the delivery flow.
Engineering challenges
- Keeping the implementation clear enough to explain while still solving the core technical problem.
- Choosing a scope that could be shipped, tested, and documented.
- Turning technical work into proof a visitor can evaluate quickly.
Results
- 30 players analyzed; 20 analytical questions answered; Pure Python implementation with no dependencies
- Documented the engineering path and important learnings.
- Made source code or technical proof available for review.
What I learned
- Tradeoff learned: Keeping the implementation clear enough to explain while still solving the core technical problem.
- Proof learned: 30 players analyzed; 20 analytical questions answered; Pure Python implementation with no dependencies
- Next iteration: Add richer screenshots or a narrated demo.