Voronoi diagrams and how they shape up offense analytics in women’s football

dc.contributor.authorMugwe, A. I.
dc.date.accessioned2025-04-16T08:57:05Z
dc.date.available2025-04-16T08:57:05Z
dc.date.issued2024
dc.descriptionFull - text thesis
dc.description.abstractVilar et al. (2013) introduces a method for analyzing collective offensive and defensive behavior, finding that maintaining numerical dominance in key areas of the field is crucial for both defensive stability and offensive opportunity. The consideration of offensive tactics we try to employ is looking at spotting defensive weaknesses, expected goal improvements and exploiting the opposing team’s defense when attacking. The use of the expected goal metric is important to a team as it serves beneficial from the aspect of seeing where to improve the offense by creating opportunities that have higher expected goals, and as well help in the defense by learning the expected model of the other teams and adequately positioning the team in order to make the opponent make shots from the low expected goal regions. The expected goal metric to be used will employ the use of machine learning techniques such as logistic regression, bagging algorithms, decision trees and deep learning techniques such as Multilayer Perceptron models so as to help in the dealing with the imbalanced goals variable. The expected goals model cannot be a stand alone feature and would need the incorporation of other metrics to determine what key factors per team lead to the creation of higher goal scoring opportunities, because of this, Voronoi diagrams were used in the exploration of how different team shapes at different moments during the game lead to either more goals or chances being created dependant on the space that the team occupies.
dc.identifier.urihttp://hdl.handle.net/11071/15680
dc.language.isoen
dc.publisherStrathmore University
dc.titleVoronoi diagrams and how they shape up offense analytics in women’s football
dc.typeThesis
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