🏏 International Cricket Data Analytics Dashboard

📊 Web Scraping and End-to-End Data Visualization & Analysis Project on combined ODI, T20 & Test Cricket Statistics

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🔍 Project Summary

This project presents a comprehensive cricket analytics dashboard that merges ODI, T20, and Test match statistics into a unified visualization. Built using Python (for data scraping & preprocessing) and Power BI (for visual storytelling), it answers critical questions about player performance, country dominance, and trends across decades.

Key questions addressed:

• 🏆 Who are the top performers across formats?
• 🌍 Which countries dominate international cricket?
• 📈 What are the performance trends over time?
• 🎯 Which captains, bowlers, and batsmen have had the biggest impact?

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⚙️ Tech Stack

Tool	Purpose
Python	Web scraping, data wrangling
BeautifulSoup	Extracting data from web pages
Pandas, NumPy	Cleaning, transformation, aggregation
Jupyter Notebook	EDA and preprocessing
Power BI	Dashboard creation and modeling

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🌐 Web Scraping

📍 Sources:

• ESPNcricinfo – Player and country stats
• Cricwindow.com – Historical data on captains, keepers, match awards, etc.

📂 Script:

📄 Web Scrapping for cricket data.ipynb

🧹 Data Collected:

Data Type	Source	Details
🏏 Most Runs	ESPNcricinfo	Player, Matches, Runs, Start–End Year
🎯 Most Wickets	ESPNcricinfo	Wickets, Matches
🧤 Most Catches	Cricwindow	Fielder Name, Dismissals
🧢 Match Awards	Cricwindow	MoM & MoS data
🧑‍✈️ Captain Stats	Cricwindow	Matches, Wins, Losses, Win %
🌍 Country Performance	ESPNcricinfo	Matches, Wins, Losses, Win rate

Tech Used: requests, BeautifulSoup, pandas

✅ Special logic was written to clean raw scraped strings like "1.2K", "10K", "234M" into numeric values.

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🧪 Exploratory Data Analysis (EDA)

📄 Exploratory Data Analysis on Cricket data.ipynb

✅ Key Tasks:

• Converted strings like '1.3K', '1M' → integers
• Extracted StartYear and EndYear for all players
• Created combined_odi_t20_test_stats.xlsx file with merged data
• Ensured data consistency across formats and metrics

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📊 Power BI Dashboard

📄 Cricket data Visualisation.pbix

🔍 Dashboard Preview:

🎯 Dashboard Highlights:

Visual Tile	Description
Top Batsman	Most international runs
Top Bowlers	Leading wicket-takers
Best Keepers	Dismissals by wicket-keepers
Best Fielders	Most catches
Top Captains	Matches led, wins
Man of the Match	Top award winners
Man of the Series	Series performance highlights
Top Countries	Country-level wins, losses, win %

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🧭 Filters & Time Slicer

• ✅ Country Filter: Slice stats by selected country

• 🕒 (Coming Soon): A unified Year Slicer via YearTable = GENERATESERIES(1980, 2025, 1)

  • You’ll be able to filter all visuals by specific year or range

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📂 Project Structure

Cricket-Analysis/
│
├── Dataset/
│   └── combined_odi_t20_test_stats.xlsx       # Final unified dataset
│
├── Python Scripting/
│   ├── Web Scrapping for cricket data.ipynb   # Web scraping logic
│   └── Exploratory Data Analysis on Cricket data.ipynb
│
├── Visualisation/
│   ├── Cricket data Visualisation.pbix        # Power BI dashboard
│   └── dashboard_screenshot.png
│
└── README.md

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🚀 How to Run Locally

1. Clone the repo
2. Run Web Scrapping for cricket data.ipynb to refresh data (optional)
3. Open Exploratory Data Analysis...ipynb to preprocess
4. Load Cricket data Visualisation.pbix in Power BI Desktop
5. Interact with the dashboard

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🧠 Key Learnings

• 🔗 Complex relationship modeling in Power BI
• 🧹 Transforming scraped text like "1.2K" or "1M"
• 📊 Using DAX to normalize metrics like Runs/Year
• 📈 Creating meaningful dashboards for storytelling

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📸 Dashboard Preview