The use of large data sets and innovative statistical methods has revolutionized professional and intercollegiate sports. This course introduces students to the academic and professional world of contemporary sports science. The course will meet biweekly with instructor lectures on sports science and with guest speakers from collegiate and professional sports. Students will be introduced to the primary data sources for sports science, to methods used to collect sports performance and outcomes data, and to the statistical tools used for sports analytics (for example, logistic regression, regression trees and random forest, network models, time series, and natural language processing). Students will be responsible for weekly writing or homework assignments based on readings and speaker presentations, as well as a quarter-long sports analytics research project. Students should have some background in econometrics, statistics and probability, data science, or machine learning.