This project presents an end-to-end exploratory data analysis (EDA) of Indian Premier League (IPL) match data using Python.
The objective is to uncover insightful patterns in team performance, player contributions, toss decisions, venues, and winning strategies through data cleaning, analysis, and visualization.
- Analyze team-wise and season-wise performance trends
- Evaluate the impact of toss decisions on match outcomes
- Identify top-performing players (runs & wickets)
- Study venue dominance and winning margins
- Visualize insights clearly for data-driven storytelling
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook
IPL match-level dataset (CSV format)
Contains information about:
- Teams
- Match results
- Toss decisions
- Venues
- Winning margins
- Player performances
- Data cleaning and preprocessing
- Feature aggregation using
groupby() - Team performance comparison
- Toss win vs match win analysis
- Venue-wise match dominance
- Top run scorers and wicket takers
- Winning margin distribution analysis
- Bar plots for team and player comparisons
- Count plots for toss and venue analysis
- Distribution plots for winning margins
- Trend-based plots for season analysis
All visualizations are designed for clarity and interpretability.
- Certain teams consistently outperform others across seasons
- Toss decisions can influence match outcomes depending on venue
- Home-ground advantage plays a measurable role
- A small set of players contribute disproportionately to team success
- Integrate ball-by-ball data for deeper player analysis
- Build predictive models for match outcome prediction
- Apply machine learning for player performance forecasting
- Create an interactive dashboard using Plotly / Power BI
Mian Rahaib
Aspiring Data Analyst | Python | Data Visualization
📌 If you find this project useful, feel free to ⭐ the repository!