Spring 2021

Statistical Aspects of Competition

Listed in: Mathematics and Statistics, as STAT-375


Ryan P. McShane (Section 01)


Competitions, which can include individual and team sports, eSports, tabletop gaming, preference formation, and elections, produce data dependent on interrelated competitors and the decision, league, or tournament format. In this course, students will learn to think about the ways a wide variety of statistical methodologies can be applied to the complex and unique data that emerge through competition, including paired comparisons, decision analysis, rank-based and kernel methods, and spatio-temporal methods. The course will focus on the statistical theory relevant to analyzing data from contests and place an emphasis on simulation and data visualization techniques. Students will develop data collection, manipulation, exploration, analysis, and interpretation skills individually and in groups. Applications may include rating players and teams, assessing shot quality, animating player tracking data, roster construction, comparing alternative voting systems, developing optimal strategies for games, and predicting outcomes. Prior experience with probability such as STAT 360 may be helpful, but is not required.

Requisite: STAT 230 and STAT 231. Limited to 24 students. Spring semester. Professor McShane.

If Overenrolled: Priority for Statistics majors.


Quantitative Reasoning


2022-23: Not offered
Other years: Offered in Spring 2021, Fall 2021