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Sports Analytics2019VT
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Course plan
Lectures
12h lectures + 12h seminars
Recommended for
Doctoral students in computer science.
The course was last given
New course.
Goals
- To gain an understanding of the research issues related to sports analytics
- To obtain knowledge about problems in sports analytics and algorithms for
solving these problems
- To be able to use relevant algorithms in a sports analytics application
Prerequisites
Recommended: a course in machine learning, statistics, data mining or big data analytics.
Contents
Sports analytics deals with using data related to sports events to obtain
insights about the sport and its surroundings. The insights can relate to such
things as player and team performance, strategies, training, injuries, and
rules of the game.
Sports:
- ice hockey
- football
- baseball
- basketball
- others based on student interest
Sports analytics problems:
- development and visualization of performance statistics
- player, lineup and team valuation
- player career trends
- team management
- team strategies
- game event detection
- injury detection and classification
Techniques:
- Machine learning
- Image recognition
- Visualization
- Knowledge representation
Organization
The course comprises a lecture part and a project part. During the lecture part different research topics are discussed. Lectures are given by the teachers, guests as well as students. During the project part the students investigate a course-related topic of their choice under supervision of the teachers.
Literature
Research articles.
Lecturers
Patrick Lambrix, Niklas Carlsson, Marcus Bendtsen, Isak Hietala and guests (including Signality, Liu Football Research Group)
Examiner
Patrick Lambrix
Examination
- Presentation of a research topic
- Sports analytics project
Credit
6 HEC
Page responsible: Director of Graduate Studies
