Research

This project develops a predictive model for the NFL Draft by integrating both on-field performance metrics and an analysis of a player's psychological and behavioral traits. By combining a player's physical talent with qualitative data from interviews, the model aims to provide a more holistic forecast of their long-term success and impact in the league.

This project uses a data-driven approach to evaluate college basketball players for the WNBA Draft. Inspired by the work of analysts like Kirk Goldsberry, the model likely uses advanced spatial analytics and on-court metrics to forecast a player's potential, providing valuable insights to help WNBA teams with their drafting decisions.

This project forecasts the NIL value of college baseball players by combining on-field performance metrics, market factors like school brand and city population, and a player's media presence and visibility.