An interactive Streamlit dashboard for exploring Indian Premier League (IPL) match and ball-by-ball data (2008–2024). It covers team performance, player statistics, toss decisions, venues, and scoring patterns across phases of an innings.
The app uses match-level and delivery-level datasets to compute statistics and render charts on demand. It is built to answer questions such as:
- Which teams have performed consistently across IPL seasons?
- How does a given batter or bowler perform against each opponent, and in winning vs losing matches?
- Does winning the toss actually help?
- Which teams dominate which venues?
- How do teams score in the Powerplay, middle overs, and death overs?
| Dataset | Description |
|---|---|
matches.csv |
Match-level information including teams, season, venue, toss, winner, player of the match, umpires |
deliveries.csv |
Ball-by-ball information including batting team, batter, bowler, runs, and dismissals |
- Python
- Streamlit: interactive dashboard
- Pandas: data manipulation and aggregation
- Matplotlib & Seaborn: visualization
ipl-data-analysis/
│
├── app.py # Streamlit dashboard
├── matches.csv
├── deliveries.csv
└── README.md
Use the sidebar to switch between:
| View | What it shows |
|---|---|
| Batter Analysis | Runs, balls, 4s/6s, strike rate, runs vs each opponent, runs in won vs lost matches |
| Bowler Analysis | Wickets, economy, bowling strike rate and average, wickets vs each opponent |
| Team Wins Over Years | Season-wise win trends for all teams |
| Match Summary | Teams, venue, toss, winner, player of the match, umpires for any match |
| Toss Impact Analysis | Toss winner vs match winner, toss decisions overall and by venue |
| Venue Impact Analysis | Busiest venues and team wins per venue |
| Seasonal Analysis | Matches and winners per season, most successful teams |
| Player of the Match Analysis | Top award winners and season-wise heatmap |
| Most Successful Team | All-time win leaderboard |
| Team Performance by Season | Wins per season for a selected team |
| Head-to-Head Comparison | Wins, ties, and no-results between any two teams |
| Phase-wise Analysis | Runs, run rate, and wickets in Powerplay, Middle, and Death overs |
| Stadium-wise Performance | Dominant team at each venue and per-team venue records |
git clone https://github.com/rakshithav2004/ipl-data-analysis.git
cd ipl-data-analysispip install streamlit pandas matplotlib seabornstreamlit run app.pyThen open the local URL Streamlit prints (usually http://localhost:8501).
- Exploratory Data Analysis (EDA)
- Data cleaning and column standardization with Pandas
- Merging, grouping, and aggregation across match and delivery data
- Building an interactive Streamlit application
- Data visualization with Matplotlib and Seaborn
- Cricket analytics: strike rate, economy, phase-wise scoring, head-to-head records
- Season and team filters across all views
- Interactive charts (Plotly)
- Advanced player comparison metrics
- Predictive analysis for match outcomes
- Deploy on Streamlit Community Cloud
Rakshitha Bai V
GitHub: rakshithav2004