An end-to-end interactive data analytics project that builds a Power BI dashboard showcasing the Best XI players from the T20 World Cup 2022 using web-scraped data (via Bright Data), Python for ETL, and Power BI for analysis and visualization.
The dashboard highlights top performers across:
- Opening Batsmen
- Middle-order Players
- All-rounders
- Tail-enders
It enables cricket enthusiasts, analysts, and learners to explore player and team statistics intuitively, understand performance metrics, and dynamically visualize the Best Playing XI from the tournament using a .pbix file in Power BI Desktop.
- Build a visually appealing, interactive dashboard for cricket performance analysis.
- Utilize real-world data scraping and ETL workflows with Bright Data and Python.
- Learn and implement Power BI best practices including DAX, Power Query, and dashboard design.
- Enable dynamic filtering and analysis of player performances for insightful decisions.
- Share a reusable
.pbixfile for open learning and portfolio showcasing.
The dashboard allows users to:
✅ Explore the dynamically selected Best Playing XI based on KPIs and role-specific stats. ✅ Analyze batting and bowling performance including:
- Batting Average
- Strike Rate
- Runs Scored
- Boundary %
- Bowling Economy
- Dot Ball %
- Wickets Taken
- ✅ Compare performance across teams, matches, and player roles.
- ✅ Use interactive slicers to filter by team, role, or match.
- ✅ Hover for in-depth player insights and role-wise breakdown.
- ✅ Navigate across report pages: Batting, Bowling, Team Overview, Best XI.
| Tool / Technology | Purpose |
|---|---|
| Python | Data scraping (ETL), cleaning, transformation |
| Bright Data | Reliable web scraping infrastructure to extract Cricinfo data |
| BeautifulSoup | Parsing scraped HTML data |
| Pandas | Data cleaning, manipulation, and exporting to CSV |
| Jupyter Notebook | Scripting and documenting ETL processes |
| Power BI | Interactive visualization, DAX-based metrics, dashboard creation |
| Power Query Editor | Final data shaping within Power BI |
| DAX | KPI measures and calculated columns for performance analysis |
- Primary Data: ESPN Cricinfo — Match and player-level stats.
- Web Scraping Tool: Bright Data for structured, scalable, and paginated data extraction.
- Data parsed using BeautifulSoup, cleaned using Pandas, and exported to
.csvfor Power BI.
Identified a need for a cricket performance dashboard highlighting a dynamic Best XI team using statistical insights.
Used Bright Data to efficiently scrape T20 WC 2022 data from Cricinfo, covering player stats, match info, and role-based attributes. Handled structured HTML, pagination, and dynamic content using BeautifulSoup.
Used Pandas in Python (via Jupyter Notebook) to:
- Handle missing values
- Normalize formats and types
- Derive key features: Boundary %, Dot Ball %, Performance Index, etc.
- Merge and structure datasets into analysis-ready
.csvfiles
Imported clean .csv datasets into Power BI using Power Query Editor for final shaping.
Defined relationships among tables for slicing, filtering, and calculations.
Created powerful DAX measures and calculated columns for:
- Role-specific KPIs
- Dynamic Best XI logic
- Team-wise comparison
- Visual cue formatting
Built a multi-page interactive dashboard with:
- KPI cards
- Slicers
- Tooltips
- Filters
- Role breakdown visuals
Packaged everything into a reusable .pbix file for learning, feedback, and personal portfolio use.
- ✅ ETL Pipeline: End-to-end understanding of data flow — from Bright Data extraction to Power BI presentation.
- ✅ Web Scraping Automation: Real-world scraping with Bright Data and Python.
- ✅ Pandas-based Data Cleaning & EDA: Preparing structured cricket data for BI consumption.
- ✅ Power BI Skills: Mastered Power Query transformations, DAX measures, and storytelling through visuals.
- ✅ Cricket Analytics: Developed the ability to analyze player impact using advanced metrics.
Want to collaborate on data analytics, Power BI dashboards, or cricket-based insights? 📧 Email: shrutijaiswal2905@gmail.com
This project combines the ETL pipeline, sports data scraping, and Power BI visualization in a single end-to-end workflow. A great hands-on project for aspiring data analysts, Power BI learners, and sports analytics enthusiasts looking to create impactful, data-driven dashboards.