In Spring 2026, I am teaching a new course,
STA 345: Statistical Methods for Sports Analytics.
In this course, we learn about statistical methods for analyzing data generated by games of sport. These methods may include, for example, statistical modeling, machine learning, causal inference, computer simulation, data visualization, and data management. We consider applications across a variety of professional and collegiate sports. As there is not a ``standard'' set of topics, we emphasize data analysis projects and readings of scholarly works in sports analytics.
In Fall 2025, I taught
STA 322/522: Design of Surveys and Causal Studies.
In this course, we learn about different designs for collecting data
and their implications for statistical inference. We cover two main
topics: how to design surveys of populations in ways that give
reliable estimates, and how to design studies in ways that allow for
valid causal claims. With regard to surveys, we investigate the
mathematical underpinnings of randomization as a tool for data
collection. We focus on the benefits and pitfalls of deviating from
purely randomized samples, including stratification, clustering, and
convenience sampling. We learn how to design and analyze
complicated surveys typically employed by government agencies. We
also discuss issues of fairness and generalizability when using big
data to train algorithms for predictive analytics. With regard to
causal studies, we again discuss the central role of randomization
as a tool for ensuring fair comparisons of treatments. We focus on
the benefits and pitfalls of variations on randomized designs,
including blocking and factorial designs. We discuss design for
observational studies, focusing on methods like propensity score
matching. Throughout we discuss a variety of genuine designs
spanning applications in public policy, health, tech, and the social and natural sciences.