“Sport is the biggest global cultural practice. We in [the social sciences], I think, have neglected it at our peril.” -Silke-Maria Weineck

Teaching

Data Analysis with Generative AI

Graduate course, Central European University, 2024

At the direction of Gábor Békés and with further help from Zsuzsa Vadle. This course will equip students, who are already versed in core data analysis methods, with experience to harness AI technologies to improve productivity. We will focus on using large language models (LLMs) such as OpenAI’s ChatGPT, Anthropic Claude.ai, Mistral’s Le Chat, and Google’s Gemini) to carry out tasks in data analysis. The course will focus on data extraction and wrangling, data exploration and descriptive statistics, and creating reports. The course involves a great deal of hands-on practice with weekly case studies and assignments. Also a bit of Data Analysis where ideas like data wrangling, documentation, and regression interpretation will come back.

Advanced Seminar (MS): Managing Sports Demand

Graduate course, Universität Tübingen, 2023

Special features of organized sport from an economic perspective. Various facets of demand for popular and elite sport are examined from a scientific and practical perspective (e.g. effects of sporting activities, emergence and significance of superstars in sport, influence of manipulation and corruption on demand).

Advanced Seminar (MS): Behavioral Biases in Sports

Graduate course, Universität Tübingen, 2023

Based on the seminal work of Israeli psychologists Amos Tversky and Daniel Kahneman there is an understanding that humans think heuristically. Tversky and Kahneman (1974) say “These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors.” Those systematic and predictable errors can also be labeled as bias. In this course, you will gain a foundational understanding of behavioral and cognitive bias, and then see how it applies to sports.