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Published education catalogue

Mathematical Modelling of Football

Education information from the published source. The education record and its time-bound offerings are kept separate.

Education facts

Code: 1RT001

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. **Outline for distance course:** This is a web-based course, completely without physical meetings (including the examination). Reading instructions and other communication are 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.

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.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

Source and updates

Skolverket Susa-navet

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Published: .

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Publication version: 8e217193-f5fa-4778-b085-a4521fd03e8d

Checksum: 1b0dc54c0fc8a359f83ba9dc8f9d468479ce432de4c03bce3b8b33dd67fe3f6c

Last changed according to the source: 2026-02-12T18:36:51