Ball-by-ball cricket analytics project exploring Royal Challengers Bengaluru's performance, player impact, winning patterns, and tactical trends across IPL 2025–2026.
Royal Challengers Bengaluru have consistently been one of the most followed franchises in the Indian Premier League, featuring world-class players such as:
- Virat Kohli
- Rajat Patidar
- Tim David
- Krunal Pandya
- Bhuvneshwar Kumar
This project uses ball-by-ball IPL data to analyze RCB's overall performance and identify the key factors influencing match outcomes.
The analysis combines team-level and player-level metrics to uncover patterns in batting, bowling, partnerships, venue performance, and winning strategies.
visit here to look at analysis -> https://abhi13shek.github.io/RCB/RCB.html
🔗 YOUR_GITHUB_PAGES_LINK
The website contains the full notebook, visualisations, methodology, and conclusions.
What factors contribute most to Royal Challengers Bengaluru's success?
To answer this question, the project examines:
- Team win percentage
- Virat Kohli contribution analysis
- Tim David impact analysis
- Top run scorers
- Top wicket takers
- Venue-wise performance
- Opponent-wise performance
- Partnership analysis
- Powerplay, middle overs, and death overs performance
- Match-winning player contributions
Ball-by-ball IPL data from Cricsheet.
| Season | Matches |
|---|---|
| IPL 2025 | XX |
| IPL 2026 | XX |
| Total | XX |
All analyses are based on Royal Challengers Bengaluru matches from these two seasons.
The workflow followed in this project:
- Extract IPL JSON files from Cricsheet
- Parse ball-by-ball data
- Construct analytical dataframes using pandas
- Perform batting and bowling analysis
- Calculate player contribution metrics
- Generate visualisations
- Identify winning patterns
- Interpret insights and draw conclusions
| Category | Tools |
|---|---|
| Programming | Python |
| Data Analysis | pandas |
| Visualisation | matplotlib |
| Notebook Environment | Jupyter Notebook |
| Development Environment | VS Code / Google Colab |
Virat Kohli remains one of the most significant contributors to RCB's batting success. The analysis evaluates how match outcomes change when Kohli plays substantial innings.
Players such as Tim David provide valuable finishing power, particularly during death overs, helping accelerate scoring rates in the final phase of innings.
RCB's win percentage varies significantly across venues, highlighting locations where the team performs best and where improvement is needed.
The project evaluates the effectiveness of RCB bowlers through wicket-taking ability, economy rates, and performance under pressure.
Several batting partnerships emerge as critical contributors to team success, revealing key combinations that consistently produce runs.
- Matches Played
- Wins
- Losses
- Win Percentage
- Top Run Scorers
- Strike Rate Analysis
- Boundary Analysis
- Kohli Contribution %
- Top Wicket Takers
- Economy Rates
- Bowling Impact
- Best Performing Venues
- Worst Performing Venues
- Venue-wise Win %
- Win % Against Each Team
- Strongest Matchups
- Toughest Opponents
- Top Partnerships
- Kohli Partnership Network
- Match-winning Partnerships
For the best experience:
- Team Performance Overview
- Venue Analysis
- Opponent Analysis
- Top Batting Performances
- Top Bowling Performances
- Virat Kohli Contribution Analysis
- Partnership Analysis
- Final Conclusions
Potential extensions of this project include:
- Season-wise comparison
- Win probability modelling
- Boundary percentage analysis
- Bowling phase analysis
- Partnership network visualisation
- Opposition-specific player matchups
- Predictive match outcome modelling
- Interactive dashboard development
Cricket analytics enthusiast exploring team strategy, player performance, and match-winning patterns through ball-by-ball IPL data analysis.
Built using Python, pandas, matplotlib, and Cricsheet IPL datasets.