Mathematical Modelling of Football, 5 credits

Academic year 2022/2023

  • Autumn 2022, Mixed, 33%, Distance learning

    Start date: 29 August 2022

    End date: 30 October 2022

    Application deadline: 19 April 2022

    Application code: UU-11813 Application

    Language of instruction: English

    Location: Flexible

    Selection: Students are selected based on a total appraisal of quantity and quality of previous university studies.

    Outline for distance course: This is a web-based course, completely without physical meetings (including the examination). Reading instructions and other communication is handled through the course home page and email. Internet access is a requirement for this course. The teaching language is English, which is required for all the individual work. Contact with the teacher can be made in Swedish.

    Number of mandatory meetings on campus: 0

    Number of voluntary meetings on campus: 0

    Registration: 28 July 2022 – 5 September 2022

Entry requirements: 120 credits including Probability and Statistics, Linear Algebra, Single Variable Calculus and a course in introductory programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Fees:

If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application or tuition fees. Formal exchange students will be exempted from tuition fees, as well as the application fee. Read more about fees.

Application fee: SEK 900

Tuition fee, first semester: SEK 12,083

Tuition fee, total: SEK 12,083

About the course

This course gives the required set of tools to work as a data scientist within a professional football club, national body or the media. It covers the technical knowledge anyone working in this area should have to contribute to a football organisation:

  • statistical methods and visualisation;
  • data sources and relevant cloud computing;
  • standards for handling and storing data;
  • classification and regression;
  • expected goals and action value models;
  • logistic regression;
  • neural networks and other machine learning methods applied to football;
  • basic analysis methods for tracking data;
  • simulation methods; and
  • pitch control.
The examples in the course are primarily from football, with a focus on practical applications found within footballing organisations, focusing on creating key performance indexes for players and teams. It shows how data and models can be used to communicate with coaches, scouts, sporting directors, players and fans.

More information

Contact

Department of Information Technology

hus 10, Lägerhyddsvägen 1

Box 337, 751 05 UPPSALA

Email: info@it.uu.se

Student counsellor

studievagledare@it.uu.se