Bayesian Analysis of Dyadic Data Arising in Basketball
The goal of this project is to use statistical methods to identify players and combinations of players which affect a basketball team's performance. The traditional statistics which are recorded tell us only about the contribution of individual players (eg. points scored, rebounds, etc). However, there are subtle aspects of play such as defensive help, setting screens and verbal communication that are known to be important but are not routinely recorded. The model we propose is based on the Bayesian social relations model. The results help us identify aspects of player performance. Data from the NBA 2004 and NBA 2005 finals are used throughout the project to illustrate our approach.
This type of interdisciplinary work is a hallmark of our program in Applied Statistics at Simon Fraser University. For more information, please contact Lucy Liu (email@example.com) or her supervisor Tim Swartz (firstname.lastname@example.org), Department of Statistics and Actuarial Science.
Keywords: Bayesian social relations model, Dyadic data, WinBUGS