Project 7 — IPL Sports Analytics Dashboard¶
Turn ball-by-ball cricket data into the kind of analytics that team strategists, broadcasters, and fantasy league players actually use.
Overview¶
The Indian Premier League (IPL) is one of the most data-rich sporting competitions in the world — every ball of every match is recorded. This project uses that granular data to answer real questions: Who are the most valuable players? Which teams win the toss and the match? What does a winning powerplay look like? It's the most engaging project in the course because the domain is fun and the data is genuinely deep.
Business Objective¶
Build an analytics product for a fantasy cricket platform that helps users pick winning teams — surfacing player form, matchup advantages, venue effects, and the statistical patterns that separate good picks from great ones.
Tools and Skills¶
| Tool | Used for |
|---|---|
| Python (Pandas) | Loading and joining ball-by-ball + match data |
| SQL | Player aggregations, partnership analysis, win-rate queries |
| Tableau | Interactive player/team dashboard |
| matplotlib/seaborn | Wagon wheels, manhattan charts, distribution plots |
Difficulty¶
Intermediate — the data is large (250k+ rows) and requires joins, aggregations, and careful handling of cricket-specific logic (extras, dismissals, innings).
Dataset¶
- Source: IPL Complete Dataset (ball-by-ball + matches)
- Download: Kaggle IPL Dataset
- Files:
matches.csv(~1,000 matches),deliveries.csv(~250,000 balls) - Schema details: dataset-guide
Project Files¶
| File | Description |
|---|---|
| business-problem | Business context and questions |
| dataset-guide | Dataset schema and cricket terminology |
| data-cleaning | Cleaning ball-by-ball data |
| sql-analysis | Player and team SQL queries |
| dashboard-design | Tableau dashboard design |
| insights-and-recommendations | Findings and fantasy recommendations |
| interview-questions | Project interview questions |
Learning Outcomes¶
- Work with large, granular event data (ball-by-ball)
- Handle domain-specific logic (extras, dismissals, innings)
- Build player performance metrics (strike rate, economy, average)
- Analyse situational data (powerplay, death overs, venue effects)
- Build an engaging, interactive sports dashboard