Grant: CCI Applied Research and Development Grants

The proposed project aims to enhance predictive modeling for NBA player performance in fantasy sports using an advanced Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model. Building on a previous collaboration with D&D Skunkworks, the enhanced SARIMAX model will integrate additional factors such as team and player matchups, player fatigue, and external conditions like injuries or weather. This will allow for more accurate predictions of fantasy points distribution for each quarter of a player's upcoming game. Currently, the SARIMAX model offers baseline performance metrics but lacks predictive accuracy due to its inability to incorporate key contextual factors. The project will refine the model by adding these features, improving its ability to forecast player performance in real-world conditions. The initiative aligns with ongoing research in predictive sports modeling, aiming to provide more accurate and actionable insights for fantasy sports users. The project will provide a competitive edge to the company in the sports gaming market. By working on this project, the student Research Assistants (RAs) will gain valuable hands-on experience in AI and machine learning, making them highly attractive candidates for future careers in data-driven industries.