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Soccer Analytics
Last Updated: 2026-02-05 16:07:31
Abstract
Soccer analytics refers to the use of data in tactical decision-making, strategic planning, and fan engagement in the context of 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 about attempts 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 lectures with time for reflective discussions and a practical part, in which small teams use free software tools to gain first-hand experience in working with sports data. The following is a tentative overview of course contents, with exemplary aspects listed for each topic. A major element for each of the analytic topics are various forms of visualization such as timelines, step plots, scatterplots, density maps, shot maps, and networks. 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: packing, line breaking, crosses - shooting: expected goals & co. 4. Match Phases - set pieces, penalties, free kicks, etc. - possession, location, organization 5. Collective Behavior - formations: spatial distributions, proximity networks - attacking: possession value, positional play, passing networks - defending: (counter-)pressure, marking networks - team composition: plus/minus, interactions 6. Environment - recruitment: player profiles, transfer market, agents, salaries - governance: clubs, leagues, associations, confederations - engagement: attendance, merchandise, social media - simulation: robocup, esports, fantasy football - betting market Fair warning: This is the first edition of the course and it may be adjusted depending on interest and feedback.
General Information
- Language
- English
- Levels
- BSC , DS , DR
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Soccer Analytics |
|
2 h weekly |
Offered In
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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.)
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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.)
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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.)
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Doctorate Humanities, Social and Political Sciences (More Information at: )