Ulrik Brandes: Catalogue data in Spring Semester 2023 |
Name | Prof. Dr. Ulrik Brandes |
Name variants | Ulrik Brandes |
Field | Social Networks |
Address | Professur für Soziale Netzwerke ETH Zürich, WEP J 14 Weinbergstr.109 8006 Zürich SWITZERLAND |
Telephone | +41 44 632 21 96 |
ubrandes@ethz.ch | |
Department | Humanities, Social and Political Sciences |
Relationship | Full Professor |
Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||
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851-0252-04L | Behavioral Studies Colloquium ![]() | 0 credits | 2K | C. Hölscher, U. Brandes, D. Helbing, M. Kapur, C. Stadtfeld, E. Stern, V. Zimmermann | ||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This colloquium offers an opportunity to discuss recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. The colloquium features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Participants are informed about recent and ongoing research in different branches of the behavioral sciences. Presenting doctoral students obtain feedback on their dissertation research plan. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This colloquium offers an opportunity to discuss recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. It covers a broad range of areas, including theoretical as well as empirical research in social psychology, research on higher education, sociology, modeling and simulation in sociology, decision theory and behavioral game theory, economics, research on learning and instruction, cognitive psychology and cognitive science. The colloquium features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Doctoral students in D-GESS can obtain 1 credit point for presenting their research in the colloquium. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
851-0557-00L | Soccer Analytics ![]() Students should be comfortable with mathematical derivations and scripting for data analysis. | 3 credits | 2G | U. Brandes | ||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Soccer analytics refers to the use of data in tactical decision-making, recruitment, strategic planning, and fan engagement in association football. This course is first and foremost about data, problems, and methods. They are discussed, however, with reference to the broader context of measurement and data science in sports and society. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Students gain insight into the role of data science in professional football. They learn to capture aspects of the beautiful game in observable data to inform tactical, strategic, and communicative decision-making. By appreciating difficulties that arise even in activities with highly regulated interactions such as team sports, they reflect on the use of data science in the study of collective behavior. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The content is organized into two streams. The first stream consists of lectures in which principles, methods, and their application are introduced and discussed. The following is a rough overview, with exemplary aspects listed for each topic. 1. Introduction - history of measurement and analytics in sports - laws of the game: equipment, space, time, players - data: master, match, event, tracking; sources, availability, uses 2. Scores - competitions: tournaments, leagues - ranking teams: coefficients, latent strengths - predicting results: odds, statistics 3. Individual Actions - running: heatmaps, pitch control - passing: line breaking, crosses - shooting: expected goals & co. 4. Match Phases - set pieces: penalties, free kicks, etc. - game cycle: states, principles, expected threat 5. Collective Behavior - formations: shapes, distributions, networks - lineups: composition, contributions, interactions 6. Environment - recruitment: player profiles, transfer market, agents - governance: clubs, leagues, associations, confederations - engagement: attendance, merchandise, social media - simulation and betting In the second stream, students gain first-hand experience by collaboratively analyzing data from all of the 64 matches of the FIFA World Cup 2022. Groups of five create a report for one match each. This is the second, updated edition of the course. Since student interest persists, we plan to make it an annual offering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Most references will be to research articles and other more technical resources, but any of the following popular books may help to set the mood. Many of them are available in updated editions. * Chris Anderson & David Sally (2011). The Numbers Game: Why Everything You Know About Football is Wrong. Penguin Books * Christoph Biermann (2019). Football Hackers: The Science and Art of a Data Revolution. Bonnier Books * Tobias Escher (2020). Der Schlüssel zum Spiel: Wie moderner Fußball funktioniert. Rowohlt * Simon Kuper & Stefan Szymanski (2009). Soccernomics. Nation Books * Timo Jankowski (2015). Successful German Soccer Tactics: The Best Match Plans for a Winning Team. Meyer & Meyer * David Sumpter (2016). Soccermatics: Mathematical Adventures in the Beautiful Game. Bloomsbury * Tifo-The Athletic (2022). How to Watch Football: 52 Rules for Understanding the Beautiful Game, on and off the Pitch. Particular Books * James Tippett (2019). The Expected Goals Philosophy: A Game-Changing Way of Analysing Football. Independently Published * Jonathan Wilson (2008). Inverting the Pyramid: The History of Football Tactics. Orion | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Credits are awarded for active participation in the group project. To get the most out of this project, basic knowledge of programming languages such as Python or R is advisable. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies![]() |
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851-0586-03L | Applied Network Science: Influence Networks ![]() | 3 credits | 2S | U. Brandes | ||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | We study applications of network science methods, this time in the domain of social and political influence. Topics are selected for diversity in research questions and techniques. Student teams present results from the recent literature, possibly with replication, in a mini-conference at the end of the lecture period. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Network science as a paradigm is entering domains from engineering to the humantities but application is tricky. By examples from recent research on social and political influence and opinion formation, students learn to appreciate that, and how, context matters. They will be able to assess the appropriateness of approaches for substantive research problems, and especially when and why quantitative approaches are or are not suitable. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Original research articles will be introduced in the first session. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
860-0045-00L | Applied Network Science: Influence Networks - Research Paper ![]() Permit by the lecturer is required. Students must be enrolled in the lecture 851-0586-03L Applied Network Science: Influence Networks. | 3 credits | 2A | U. Brandes | ||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This is an activity course augmenting the topical project seminar 851- 0586-03 Applied Metwork Science with a research paper that combines the discussion of a result from the literature with work of their own (as conducted in the associated project seminar). Papers are cross-reviewed and revised before their final submission. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Students practice collaborative academic writing and peer review. |