This module offers a hands-on introduction to data-driven decision-making in the professional sports context. Students learn the fundamentals of statistics and machine learning, and how to collect, process, and visually analyze data from various sources. The course covers basic predictive models and introductory artificial intelligence techniques, exploring their applications in sports management and across the professional sports value chain. Students receive practical guidance on using modern large language models in Python via Vibe Coding. A strong emphasis is placed on the responsible use of AI and machine learning, including critical reflection on data quality, bias, and black-box challenges.
- Analyze and interpret sports-related data to draw informed conclusions for sports management
- Apply basic data analysis and visualization techniques to present sports-relevant information clearly and support decision-making
- Strategically use data to inform decision-making in sports contexts, such as optimizing performance, marketing, or organizational processes
- Critically reflect on the potential and limitations of data-driven decision-making, with particular attention to ethical, legal, and practical implications in sports
Prof. Dr. Kevin Bauer
Dr. Kevin Bauer holds a PhD in Economics from Goethe University, where he was also a visiting researcher at the University of Michigan. He completed a master degree in Information Systems with a focus on Artificial Intelligence during his doctoral studies. He regularly lectures on AI, machine learning, blockchain, and other decision technologies for European financial supervisors. Previously, he taught Behavioral Economics (Goethe University), Managerial Economics (University of Essex), and Applied Deep Learning in Finance (Goethe Business School).