Sports Analytics
Level: 30hp (Advanced level, Master's thesis)
Number of students: 1-2
Area: Computer Science
We have several possible topics available in the field of sports analytics (particularly ice hockey, but other sports may be discussed).
Example topics:
- detect complex events that lead to predefined outcomes in an ice hockey game
- derive key stats for player performance (including minimal sets of key stats and introducing complex stats)
- derive characteristics for successful line-ups
- derive meaningful clusters of players
- analyze injury data
- build a knowledge graph for ice hockey and SHL in particular
Prerequisites:
You should have taken a data mining course (or TDDD43 for the last topic) and have an interest in ice hockey.
Contact: Patrick Lambrix.
Level: 16hp (Bachelor level, Bachelor's thesis)
Number of students: 1-2
Area: Computer Science
The topics above may be available if you have a background knowledge in data mining.
Topics related to design and implementation of different components of sports analytics systems are available.
Contact: Patrick Lambrix.
Page responsible: Patrick Lambrix
Last updated: 2018-04-16