🏏 Cricbuzz Cricket Analytics Dashboard

An end-to-end data engineering and visualization project that fetches real-time cricket data from the Cricbuzz API and builds insightful dashboards using Snowflake and Power BI.

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📌 Project Overview

This project captures live and historical cricket data — including player stats, match outcomes, series insights, partnerships, and rankings — to build a robust analytical platform for cricket lovers, analysts, and fantasy players.

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🚀 Tech Stack

• Data Extraction: Python + RapidAPI (Cricbuzz API)
• Data Warehouse: Snowflake
• Data Transformation & Cleaning: Python (pandas), SQL (Snowflake)
• Dashboard & Visualization: Microsoft Power BI

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🧱 Project Architecture

Cricbuzz API
    ↓
Python ETL Scripts
    ↓
Cleaned & Transformed Data
    ↓
Snowflake Data Warehouse
    ↓
Power BI Dashboards

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📂 Data Model

Table Name	Description
icc_rankings_main	ICC player & team rankings (Test, ODI, T20)
team_players_main	Player roster and details per team
player_batting_stats_main	Historical batting performance
player_bowling_stats_main	Historical bowling performance
series_main	Series-level metadata
matches_main	Match details including teams, results
innings_main	Inning-wise stats like score, RR, wickets
battingperformance_main	Player-wise batting performance per innings
bowlingperformance_main	Over-by-over bowling breakdown
partnerships_main	Batting partnerships analysis
wickets_main	Fall of wickets detail

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📈 Dashboard Modules

1. ICC Rankings

• Top players by format and role
• Filter by country and position

2. Team & Player Insights

• Batting and bowling performance
• Player career trends and match impact

3. Series & Match Analysis

• Win/loss summaries
• Toss impact vs match results
• Match-wise performance drilldown

4. Innings & Micro Stats

• Ball-by-ball impact
• Top partnerships
• Fall of wickets patterns

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🔧 How to Run Locally

1. Clone the Repository

git clone https://github.com/AkshSurani/cricbuzz-cricket-data-pipeline-dashboard.git
cd cricbuzz-dashboard

2. Install Dependencies

pip install -r requirements.txt

3. Configure Environment

Create a .env file:

RAPIDAPI_KEY=your_cricbuzz_api_key
SNOWFLAKE_ACCOUNT=your_account
SNOWFLAKE_USER=your_username
SNOWFLAKE_PASSWORD=your_password
SNOWFLAKE_DATABASE=cricket_db
SNOWFLAKE_SCHEMA=public
SNOWFLAKE_WAREHOUSE=compute_wh

4. Run the ETL Scripts

Run python Files.

5. Load to Snowflake

Configure your Snowflake connection and execute the load script using Python or SQL.

6. Open Power BI Dashboard

• Open Cricbuzz_Cricket_Dashboard.pbix
• Connect to Snowflake
• Refresh visuals to see the latest data

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🔍 Key KPIs Tracked

🏆 Player KPIs

• Top Run Scorers (avg, SR, consistency)
• Top Wicket Takers (econ, avg, impact)
• Player Rankings (format-wise, role-wise)

📊 Series KPIs

• Win/Loss records
• Toss decision impact
• Series-wide score summaries

🔬 Micro Analytics

• Batting Partnerships
• Powerplay & Death Over performance
• Fall of Wickets Timeline

📈 Team KPIs

• Squad composition by roles
• Margin of victory
• Country-wise dominance

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🧠 Advanced Ideas (Future Enhancements)

• ⏰ Real-time Auto Refresh using GitHub Actions or Airflow
• 📱 Fantasy Cricket Insights & Player Selection Advisor
• 🧮 Predictive Analysis using ML models (Win Predictor, Player Form)

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👤 Contributor

Aksh Surani – Data Engineer LinkedIn

Divy Kaila – Data Engineer LinkedIn

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📄 License

This project is intended for educational and portfolio use.
API access is bound by Cricbuzz and RapidAPI terms of service.

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📸 Dashboard Screenshots

🧭 Overview Dashboard

🏏 Batting Performance Page

🎯 Conversion and Centuries Page

📊 Matches Analysis Page

📈 Player Batting Stats In Series Page

📉 Total Runs, Contribution Per Match Page

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🌟 If you like this project...

Give it a ⭐ on GitHub!
Or share it with your cricket analytics buddies. 🏏