SRH Cricket Performance Analysis (IPL 2022-2024)

The Story Behind the Analysis

As a passionate cricket enthusiast, I've followed the Indian Premier League (IPL) since its inception, witnessing the rise and fall of various teams. The transformation of Sunrisers Hyderabad (SRH) particularly caught my attention. In 2022 and 2023, SRH was struggling at the bottom of the table, a shadow of the team that had once been IPL champions. But something remarkable happened in 2024 - they not only improved but became serious contenders for the title.

What intrigued me most was the dramatic nature of this turnaround. How did a team go from being consistent underperformers to title challengers in such a short span? The appointment of Pat Cummins as captain - fresh from his World Cup victory with Australia - was clearly a factor, but I suspected there was more to the story.

This curiosity led me to dive deep into the data. As someone who believes in the power of analytics in sports, I wanted to understand:

• Was this improvement purely due to leadership changes?
• Had there been subtle shifts in team composition that the casual observer might miss?
• Could data reveal patterns that explained this transformation?

The project became more than just an analysis - it became a story of resilience, strategic thinking, and the importance of data-driven decision-making in modern cricket.

Project Overview

This analysis explores Sunrisers Hyderabad's (SRH) remarkable performance improvement in the Indian Premier League (IPL) from 2022 to 2024. Using R and statistical analysis, the project revealed a 20% improvement in player performance following strategic lineup changes and achieved a 15% increase in win prediction accuracy through advanced statistical modeling.

Key Findings

• Performance Improvement: Identified significant upward trend in team's run rate from -0.590 in 2023 to positive territory in 2024
• Team Restructuring: Analysis showed optimized player role distribution in 2024 compared to previous seasons
• Leadership Impact: Quantified the effect of Pat Cummins' captaincy on team strategy and performance
• Strategic Success: Documented how data-driven decisions and player acquisition strategies contributed to team improvement

Why This Matters

The findings from this analysis have implications beyond just SRH:

1. For Teams: Demonstrates how data-driven decisions can transform team performance
2. For Fans: Provides deeper insights into the game they love
3. For Analysts: Shows how statistical analysis can reveal patterns in sports performance
4. For Cricket: Contributes to the growing field of cricket analytics

Technical Implementation

Dependencies

• R (version 4.0+)
• tidyverse
• rvest
• httr
• knitr
• kableExtra
• viridis
• ggplot2

Data Sources

• IPL T20 official website
• StatisticsTimes.com
• CricTracker.com

Project Structure

.
├── SRH_Performance_Analysis.Rmd    # Main analysis document
├── data/
│   ├── raw/                        # Raw scraped data
│   └── processed/                  # Cleaned and transformed data
├── images/                         # Project images
└── output/                         # Generated visualizations

Key Features

1. Web Scraping Pipeline

   • Automated data collection from multiple cricket statistics websites
   • Custom functions for handling different webpage structures
   • Data validation against official IPL sources

2. Data Processing

   • Player role standardization
   • Team performance metrics calculation
   • Time series analysis of performance indicators

3. Visualization Suite

   • Team performance trends
   • Player role distribution analysis
   • Comparative team analysis

Reproduction Steps

1. Clone the repository
2. Install required R packages:

install.packages(c("tidyverse", "rvest", "httr", "knitr", "kableExtra", "viridis", "ggplot2"))

3. Open SRH_Performance_Analysis.Rmd
4. Run all chunks to reproduce analysis

Methodological Highlights

• Robust data validation against multiple sources
• Statistical significance testing for performance metrics
• Time series analysis of team evolution
• Cross-validation of prediction models

Lessons Learned

Through this analysis, I discovered that:

1. Cricket's complexity can be understood through data
2. Team success often comes from multiple small improvements rather than one big change
3. Leadership changes must be accompanied by strategic roster decisions
4. Data analysis can reveal insights that even experienced observers might miss

Limitations and Future Work

• Web scraping dependence on consistent HTML structure
• Limited historical data availability
• Potential for expanded player-specific analysis
• Opportunity for more advanced predictive modeling

Contributing

Contributions are welcome! Please read the contribution guidelines before submitting pull requests.

License

This project is licensed under the MIT License.

Author

Bhavya Bhargava "The Fourth Umpire"

Acknowledgments

• IPL T20 for providing official statistics
• StatisticsTimes.com for historical data
• CricTracker for player information

Contact

For questions or collaboration opportunities, please open an issue in the repository.

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Last updated: 2024-08-31