Syllabus for Mathematical Modelling of Football

Matematisk modellering av fotboll

A revised version of the syllabus is available.

  • 5 credits
  • Course code: 1RT001
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N, Image Analysis and Machine Learning A1N, Mathematics A1N, Data Science A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2020-06-12
  • Established by:
  • Revised: 2020-10-19
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2021
  • Entry requirements:

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

  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course, the student should be able to:

  • Identify statistical relationships and visualize football data with, for example, passing networks and heatmaps.
  • Develop and fit models of expected number of goals and action value models, using logistic regression, neural networks and other classifiers.
  • Implement simulations of football matches, including Poisson models of goals, Markov chain models of player actions and self-propelled particle models of movement.
  • Perform match analysis using position and velocity of players, team formations and models based on tracking data.
  • Perform tactical analysis using fitness data and workload monitoring.


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: The course covers basic statistical methods and visualization; 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 indexs for players and teams. It shows how sata and models can be used to communicate with coaches, scouts, sporting directors, players and fans


Lectures, computer laboratory work, feedback on shorter exercises and supervision of group project.


Shorter exercises in computer laboratories, including creating a pre- and post- match report. Group project with oral examination.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Syllabus Revisions

Reading list

The reading list is missing. For further information, please contact the responsible department.