I worked alonside two classmates and good friends, Pramit Vyas and Tyler Quinn, to deliver a data solution to the United States Tennis Association (USTA) to help them identify and predict the performance of promising American junior tennis talent.
As a massive fan of tennis for 10 years at the time of writing this, I was obviously extremely excited to work on this project. My expertise in professional tennis certainly helped us tremendously as we worked on this project to understand the direction of the project as well as the data and other resources at our disposal.
For my part in the project, I took complete ownership of building a Python-based pipeline to scrape, clean, and structure over 4 million rows of historical player records from the web. I led the documentation and knowledge transfer processes, built an interactive Tableau dashboard with robust filters, and served as the primary technical point of contact for the client. I also helped the others develop the models.
The project culminated in a final presentation we delivered live to key stakeholders at the USTA, a final report explaining our motivation, methodology, and results, and a project bundle composed of all important datasets, documentation, URLs (e.g, GitHub repository), and more related to the project.