A complete data analysis and visualization project based on the 2022 Indian Premier League (IPL) season. This project uncovers match trends, player performances, venue stats, and strategic insights using Python, Pandas, Matplotlib, and Seaborn — with clear visual storytelling.
- ✅ Analyzed 74 IPL matches from 2022
- 🏆 Identified top teams and players
- 🎯 Explored toss decisions and match outcomes
- 📈 Visualized scoring, bowling, and venue patterns
- 📊 Included 12+ charts and plots for better understanding
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Format: CSV
- Fields: match date, venue, teams, scores, toss decisions, winners, player of the match, top scorer, best bowling figures, etc.
- Bar Chart showing which teams won the most matches
- Count Plot of toss decisions (Bat/Field)
- Count Plot of toss winners by team
- Percentage of matches where toss winner also won the match
- Count Plot showing wins by Runs vs Wickets
- Bar Chart of top 10 players with most awards
- Horizontal Bar Chart of top 5 batsmen by total runs
- Horizontal Bar Chart of top 10 bowlers by total wickets
- Bar Chart showing number of matches played at each stadium
- Match with the largest run margin
- Player with the highest single-match score
- Bowler with the best single-match bowling figure
Here’s a glimpse of the visualizations included:
📁 All plots are saved in the
/imagesfolder. You can regenerate them by running the script.
# Clone the repository
git clone https://github.com/amish5ingh/Cricket-Data-Analytics-IPL.git
# Navigate to the project folder
cd Cricket-Data-Analytics-IPL
# Launch Jupyter Notebook
jupyter notebook
# Open the notebook file
ipl_analysis.ipynb
# Run all cells step-by-step to see data analysis and visualizations