Predictive Analytics in Sport

Catalog description

Credits 3. 3 Lecture Hours. Overview of predictive analytics utilizing linear regression-based modeling approaches to analyze sport-related data; learning the iterative process of model building through trial and error and by working on hands-on problems and a team-based project; collection and analysis of sport-related data using predictive analytics, whether the dependent variable is continuous (i.e., attendance or revenue) or binary (i.e., a win or a loss) in nature, or whether group membership is being predicted; selection of appropriate model given the data to be analyzed or research question to be answered, the selection of appropriate independent variables based on knowledge of the sport industry, theory, or the scientific literature, interpretation of coefficients and measures of model fit, and ability to communicate advanced statistical information to a lay audience. Prerequisites: Grade of C or better in STAT 201 , STAT 301 , STAT 302 , or STAT 303 .