TDDE64 · 6 credits · Advanced level (A1N) · Datateknik, Datavetenskap
Sports Analytics
Active · next start VT 2027
100%
passed the latest regular sitting
MUN1 · 25 May 2026 · 36 students
100%
passed the regular sittings, all sittings over the last 5 years
MUN1 · 55 students
This module only reports passing results, so the pass rate does not show how many did not pass.
Part of 4 programmes
Other module
MUN1 Oral presentation of research paper
- G
Main rounds
100% passed · 55 students
- 100%36
- 100%19
Later rounds
100% passed · 15 students
- 100%6
- 100%9
- Pass a later round: 100% (15 students), compared with 100% (55 students) on the ordinary exam
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%4
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
PRA1 Project work
- 5
- 4
Main rounds
100% passed · 17 students
- 100%10
- 100%7
Later rounds
100% passed · 18 students
- 100%9
- 100%9
- Pass a later round: 100% (18 students), compared with 100% (17 students) on the ordinary exam
- Get 4 or 5: 100% (35 students)
Show small sittings (16)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%2
- 100%3
- 100%3
- 100%2
- 100%1
- 100%1
- 100%1
- 100%2
- 100%1
- 100%3
- 100%1
- 100%3
- 100%1
- 100%2
- 100%4
About the course
Aim
- 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
Content
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
- Examiner
- Patrick Lambrix
- Scheduled hours
- 0 h
- Main field of study
- Datateknik, Datavetenskap