🏏 Cricket Data Analytics Dashboard

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📌 Project Title

T20 World Cup Cricket Data Analytics – Team Selection & Dashboard with Python + Power BI

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📊 Project Summary

This project provides a data-driven deep dive into the 2022 T20 Cricket World Cup, with a creative twist. It starts with scraping match and player data from ESPNcricinfo, processes the data using Python (Pandas), and visualizes it via an interactive Power BI dashboard. It provides a detailed breakdown of individual and team performances, using advanced data modeling and DAX calculations in Power BI. The project ends with an algorithmically chosen Best XI players based on performance statistics.

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📺 Project Demo

🎥 Cricket Data Analytics – T20 World Cup Dashboard

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🚀 What I Did

• 🔍 Web Scraping: Collected structured cricket data from the ESPNcricinfo website
• 🧹 Data Preprocessing: Cleaned and formatted the raw data for analysis
• 📐 Data Modeling in Power BI: Created relationships between batting, bowling, and match tables
• 🧠 DAX Calculations: Developed calculated fields for strike rate, economy, batting average, etc.
• 📊 Dashboard Design: Designed an interactive Power BI dashboard for user-friendly insights
• 🧩 Best XI Selection: Built logic to extract the top 11 performing players across all teams

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🗝️ Key Features

• 📈 Power BI dashboard with filters for roles, countries, and metrics
• 🏅 Role-based player analysis: openers, middle-order, finishers, all-rounders, bowlers
• 📊 Performance KPIs: Batting average, strike rate, wickets, bowling economy, total runs.
• 🔍 Timeline Analysis: Match-wise performance throughout the tournament
• 🧠 Applied DAX to derive new metrics for meaningful insights
• 🎯 Best XI Team: Data-driven selection of the best performing players

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📁 Files Included

• Cricket_T20_WC_2022.pbix – Interactive Power BI Dashboard
• t20_files.csv – Scraped and preprocessed data used for modeling
• README.md – Project summary and guide

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🛠️ Tools & Tech

• 🐍 Python (Pandas) – Data cleaning and transformation
• 📊 Power BI – Dashboard creation and DAX metrics
• 🌐 ESPNcricinfo – Data source
• 🧮 DAX – Advanced measures and calculated fields (SR, Economy, AVG)

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📊 Use Cases

• 🏏 Select top-performing players for specific roles
• ⚖️ Compare players across teams and positions
• 📊 Identify the most balanced playing XI based on stats
• ⚙️ Support coaches, selectors, and analysts with data-backed decisions

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🙌 Acknowledgments

• ESPNcricinfo for match and player data
• The cricket analytics community for inspiration

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📬 Connect

For questions or collaborations, feel free to reach out!