Exploratory Data Analysis on the Indian Premier League (IPL) dataset using Python. This project uncovers key insights about match results, player performances, venue statistics, and seasonal trends across 17 IPL seasons.
| # | Analysis | Key Finding |
|---|---|---|
| 1 | Team Wins | MI and CSK are the most successful IPL teams |
| 2 | Toss Impact | Winning toss has no significant impact (50.6%) |
| 3 | Toss Decision | 64.3% of teams choose to field after winning toss |
| 4 | Season Trends | IPL expanded in 2022 with two new teams |
| 5 | Venue Analysis | Eden Gardens is the most used IPL venue (77 matches) |
| 6 | Top Players | AB de Villiers leads in Player of the Match awards |
| 7 | Win by Runs vs Wickets | Chasing is more successful (578 vs 498) |
| 8 | Super Over | Only 1.3% of matches go to a Super Over |
- Python
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook
- Clone the repository:
git clone https://github.com/WarnZzz/IPL-Data-Analysis.git
- Install dependencies:
pip install -r requirements.txt
- Open the notebook:
jupyter notebook notebooks/ipl_analysis.ipynb
IPL-Data-Analysis/
│
├── data/ # Dataset files
├── notebooks/ # Jupyter notebooks
│ └── ipl_analysis.ipynb
├── README.md # Project documentation
└── requirements.txt # Dependencies
- Source: Kaggle — IPL Complete Dataset 2008-2020
- File used:
matches.csv
- Winning the toss does not guarantee winning the match
- Teams strongly prefer to field first due to dew factor in night matches
- Chasing is more successful than defending in IPL
- AB de Villiers is the most impactful player in IPL history by Player of the Match awards
- Eden Gardens has hosted the most IPL matches
Ranjan Paudel
- GitHub: @WarnZzz