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.
FEATURES:
Provides hands-on experiences
Students are taught the fundamental methodology first. This serves as the foundation to subsequent analyses and data manipulations. In this manner, students learn much more than how to talk about sports. They learn to generate, manipulate, visualize, and analyze illuminating data.
Fosters the use of visualizations for analysis and presentation
The use and importance of data visualizations are stressed throughout both as a way of more richly understanding problems and as a way to present key findings, especially to non-technical audiences.
Motivates students with real-world problems
Each chapter about a sport includes a case study regarding a key challenge that invites analysis. These are designed to motivate learning and stimulate discussion.
Prepares students for further advanced work
Each chapter includes a Research Block that engages sports analytic questions currently addressed in the literature and provides links. In an evolving, highly competitive field, this provides students pursuing a career in sports analytics with an exceedingly valuable skill, habit, and resource.
An eTextbook version is available at VitalSource.com for $44.65 USD. ISBN: 978-1-958303-06-1.
