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Sports Analytics

2022VT

Status Active - open for registrations
School IDA-gemensam (IDA)
Division ADIT
Owner Patrick Lambrix
Homepage https://www.ida.liu.se/~753A01/index.en.shtml

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Course plan

Lectures

preliminary: 12h lectures + 12h seminars

Recommended for

Doctoral students in computer science.

The course was last given

2020vt

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 and guests

Examiner

Patrick Lambrix

Examination

- Presentation of a research topic
- Sports analytics project

Credit

6 HEC


Page responsible: Director of Graduate Studies