A master thesis from Aalborg University
Classifying Corner Kicks in Football
Author(s)
Term
4. term
Education
Publication year
2024
Submitted on
2024-05-24
Pages
14 pages
Abstract
In association football, even a marginal advantage can translate to a significant scoring advantage, often determining the outcome of a match. This study investigates the classification of set pieces using machine learning. We specifically target corner kicks, to provide valuable insights for analysts and coaches. Our research utilizes a dataset capturing player and ball positions, recorded at a frequency of 25 frames per second, over 2132 corner kick situations. From this dataset, we extract eight distinct features to evaluate their effectiveness in classification tasks, either by themselves or in combination with other features. From a coaching guide, we identify six different types of corner kicks, which we have annotated the dataset with. The study also investigates the impact of upsampling on this dataset. Our analysis finds that our features and combination of features lays a good foundation for future research into corner kick classification.
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