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🏏 Indian Premier League (IPL) Data Analysis (2008–2020)

This project analyzes data from the Indian Premier League (IPL) seasons held between 2008 and 2020.
It focuses on team performance, player statistics, and match dynamics using match-level and ball-by-ball datasets.
The goal is to uncover insights and trends that highlight the evolution of the IPL over time.


📂 Dataset

  • Matches DataIPL Matches 2008-2020.csv
  • Ball-by-Ball DataIPL Ball-by-Ball 2008-2020.csv

Both datasets contain detailed information such as:

  • Match results, teams, venues, umpires, toss outcomes
  • Ball-level runs, wickets, batting/bowling stats

⚙️ Technologies Used

  • Python 3
  • Pandas – Data manipulation
  • NumPy – Numerical operations
  • Matplotlib & Seaborn – Data visualization

🔍 Analysis Covered

📊 Season-Level Insights

  • Matches played per season
  • Runs scored per season
  • Runs per match across years
  • Toss decisions and outcomes
  • Tournament winners

🏏 Team Analysis

  • Most matches played & won
  • Winning percentages
  • Lucky venues for teams
  • Run rates in powerplay & death overs
  • 200+ team scores (scored & conceded)

👨‍💻 Player Analysis

  • Most runs, fours, sixes
  • Highest strike rates
  • Leading wicket-takers
  • Most balls faced
  • Most Man of the Match awards

⚔️ Match Dynamics

  • Toss vs match outcome correlation
  • Chasing vs defending wins
  • Biggest win margins
  • Venue-wise match hosting

📌 Visualizations

The project includes a variety of plots:

  • Bar charts (team wins, toss decisions, run rates)
  • Pie charts (inning-wise runs, tournament winners)
  • Trend plots (runs, fours, sixes per season)

🚀 How to Run

  1. Clone the repository
    git clone https://github.com/your-username/IPL-Data-Analysis.git
    cd IPL-Data-Analysis

About

End-to-end data analysis project with EDA, data cleaning, visualization, and actionable insights — built with Python, Pandas, Matplotlib, and Seaborn in Jupyter Notebook.

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