VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Soccer Analytics
Last Updated: 2026-06-03 00:14:15
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.
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, actors - data: match, event, tracking; sources, availability, uses 2. Theory - game cycle: states, principles, expected threat 3. Outcomes - competitions: tournaments, leagues - ranking teams: coefficients, latent strengths - predicting results: odds, statistics 4. Individual Actions - running: heatmaps, trajectories, pitch control - passing: networks, line breaking, crosses - shooting: expected goals, expected points 5. Collective Behavior - set pieces: penalties, corner kicks, etc. - formations: positions, shapes - lineups: composition, substitutions 6. Environment - recruitment: player profiles, transfer market, agents - governance: clubs, leagues, associations, confederations - engagement: attendance, merchandise, social media In the second stream, students gain first-hand experience by collaboratively analyzing professional-grade data from the current season of the UEFA Champions League. This is the fifth, updated edition of the course. It is now an annual offering.
Resources
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
General Information
- Language
- English
- Levels
- BSC , DS , DR , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 720
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Soccer Analytics |
|
2 h weekly |
Offered In
-
Science in Perspective (In “Science in Perspective”-courses students learn to reflect on ETH’s STEM subjects from the perspective of humanities, political and social sciences. Only the courses listed below will be recognized as "Science in Perspective" courses.)
-
Type A: Enhancement of Reflection Competence (SiP courses are recommended for bachelor students after their first-year examination and for all master- or doctoral students. All SiP courses are listed in Type A. Courses listed under Type B are only recommendations for enrollment for specific departments.)
-
Type B: Reflection About Subject-Specific Methods and Contents (Subject-specific courses. Particularly relevant for students interested in those subjects. All these courses are also listed under the category “Typ A”, and every student can enroll in these courses.)
-
-
-
Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
-
-
-
-
Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed. All courses listed as core courses (not electives) for one of the following ETH MSc programmes, MSc Statistics, MSc Physics, MSc Computer Science, MSc (Applied) Mathematics, MSc Neural Systems and Computation, MSc Robotics, Systems, and Control, MSc Data Science, MSc Electrical Engineering and Information Technology, can be taken as an elective course in the MSc CSE without prior permission.)
-
-
Doctorate Humanities, Social and Political Sciences (More Information at: )
-