🏏 IPL Auction Analysis (2013 - 2022)

📖 Project Overview

This project provides a comprehensive analysis of the Indian Premier League (IPL) Player Auctions from 2013 to 2022.

The workflow integrates Data Engineering, Database Management, and Visual Analytics to uncover trends in franchise spending, player valuations, and auction dynamics over a decade.

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📂 Repository Structure

File Name	Description
IPLPlayerAuctionData.csv	Raw Dataset: Original auction records with player names, prices, and teams.
Cleaned_IPL_Auction_Data.csv	Processed Dataset: Cleaned data with normalized columns and new features.
Main_SQL.ipynb	ETL Pipeline: Python notebook that cleans raw data and loads it into a MySQL database.
Main_NoSQL.ipynb	Data Cleaning: Standalone notebook for cleaning data (CSV output only, no SQL).
cleaned_ipl_import.sql	SQL Script: Database schema definition and 10+ analytical queries.
IPL Visualization.pbix	Dashboard: Interactive Power BI report visualizing the insights.

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📊 Dataset Details

The dataset tracks the financial history of IPL auctions. Key variables include:

• Player: Name of the cricketer.
• Role: Playing category (Batsman, Bowler, All-Rounder, Wicket Keeper).
• Amount: Auction price in INR.
• Team: Purchasing franchise.
• Year: Auction year (2013-2022).
• Player_Origin: Domestic (Indian) vs. International (Overseas).
• Amount_Cr: Calculated field representing price in Crores.
• Repeated: Boolean flag indicating if a player appeared in multiple auctions.

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🛠️ Prerequisites

To run this project locally, you will need:

• Python 3.x (with pandas and mysql-connector-python libraries)
• MySQL Server (local instance)
• Microsoft Power BI Desktop (to view the .pbix file)

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🚀 Usage Instructions

1. Database Setup

1. Open your SQL client (e.g., MySQL Workbench).
2. Run the cleaned_ipl_import.sql script. This will:
   • Create the ipl_auction database.
   • Create the auction_data table schema.

2. Data Cleaning & Loading

1. Open Main_SQL.ipynb in Jupyter Notebook.
2. Update the database connection settings (host, user, password) in the notebook.
3. Run all cells to:
   • Load and clean IPLPlayerAuctionData.csv.
   • Export Cleaned_IPL_Auction_Data.csv.
   • Insert records directly into your MySQL database.

3. Analysis & Visualization

• SQL Analysis: Use the queries provided at the bottom of cleaned_ipl_import.sql to generate insights like "Top 10 Most Expensive Players" or "Team-wise Spend".
• Dashboard: Open IPL Visualization.pbix in PowerBI to explore the data visually.

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🔎 Key Insights Generated

The analysis answers critical questions such as:

• Spending Power: Which teams spend the most on average?
• Role Valuation: Do All-Rounders command higher prices than purely Batsmen or Bowlers?
• Inflation: How has the total auction purse increased from 2013 to 2022?
• Player Demand: Who are the most frequently auctioned players?

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📊 Dashboard Overview

👤 Author

Rishabh

• GitHub Profile
• LinkedIn