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Dhanush is a 2nd-year Computer Science Engineering student at RV College of Engineering, India. He is enthusiastic about exploring the world of technology and its creative applications. He is always eager to learn and innovate and finds joy in deciphering complex challenges. Beyond academics, he is an avid reader and a curious explorer of new horizons in the tech realm and an unapologetic anime binge watcher. His fascination with technology and love for captivating stories converge in his academic pursuits and leisure time. He is eager to explore the intricacies of computer science while immersing myself in the vibrant worlds of anime. Balancing between coding challenges and epic anime quests, Dhanush aspires to infuse innovation into both realms.. He is looking forward to contributing positively to the field and embracing every opportunity to grow and excel.

Poster Abstract

Introduction: 

The project aims to leverage T20 statistics from the ICC website to aid in the optimal selection of players for the Indian Premier League (IPL). By employing web scraping techniques, player performance data is collected and processed. Evaluation metrics such as batting average, strike rate, bowling average, and economy rate are utilized to assess players. This project provides a data-driven approach to enhance the team selection process, facilitating informed decisions based on statistical analysis. 

Objective: 

The proposed project is to analyse T20 cricket statistics from the ICC website and use evaluation metrics to identify and select high-performing players for the Indian Premier League (IPL) teams. By leveraging data-driven insights, the project aims to enhance the efficiency and effectiveness of IPL team selection, ultimately improving team performance in the tournament. 

Methodology: 

First, data is collected from the ICC website, specifically focusing on T20 cricket statistics. Web scraping techniques are employed to extract the required data. Once collected, the data is preprocessed to clean and transform it into a usable format. Next, evaluation metrics are defined based on the project's objectives and requirements. These metrics may include batting average, strike rate, bowling average, economy rate, and other performance indicators. The extracted and pre-processed data is then analysed using statistical techniques and calculations. Additionally, data visualization techniques like plots and charts can aid in identifying trends and patterns. By applying the defined evaluation metrics to the data, players' performance can be assessed and ranked. The analysis helps in identifying high-performing players based on the specified criteria.

Presentation

In this Video Recording, Dhanush provides a tour and explanation of his poster content.

Data-Driven IPL Team Selection: Leveraging T20 Statistics

Click on the poster for a larger image.

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