The National Basketball Association, or NBA, gives out a litany of awards every season. From awarding outstanding defense, to acknowledging tremendous improvement in a player, no award is more prestigious than the Most Valuable Player (MVP) award. Each year, the NBA media votes on which player will receive the award, and the player with the highest vote share wins. As statisticians, this begs the question: How can we predict who will receive this award? In this paper we propose a Bayesian approach to modeling, creating a Bayesian beta regression model to predict the MVP vote share a given NBA player will receive at the end of the season.
To read more on the analysis of the results and our methodology, please refer to the FinalReport.pdf. Our model and figures can be replicated using the R notebook, Code_Appendix.Rmd, with the data provided for 2022 in 2022_dat.txt.
This project was done as a final project by my team, Andrea Boskovic, Harshil Desai, and I (Rebecca Lopez) for our graduate statistics course at the University of Washington.