Introduction to Sports Analytics Using R

Introduction to Sports Analytics using R prepares students for the programming and analytical challenges of Sports Analytics. Key techniques covered using R and the Tidyverse include data acquisition/web scraping, regression, prediction, simulation, visualization, and cluster models. Sports covered include baseball, basketball, football, ice hockey, soccer, golf, sports betting, and daily fantasy sports. However, the techniques and insights learned should prove valuable for studying other sports. Students are expected to have had a programming course (though not necessarily R) and a statistics class. A prior course in linear programming and optimization models will provide advanced insight into the decision-making processes covered but is not necessary.