Unveiling the Game: Advanced Football Analysis Through Machine Learning and Player-Centric Insights

Shetty, Pratik Umesh (2023) Unveiling the Game: Advanced Football Analysis Through Machine Learning and Player-Centric Insights. Masters thesis, Dublin, National College of Ireland.

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Abstract

The landscape of football analysis is undergoing a transformative journey fueled by advanced machine learning integration. This study utilizes Gradient Boosting Classifier and Logistic Regression on a comprehensive dataset comprising 9,000+ soccer matches from the top five European leagues. Collected meticulously from reputable sources, the dataset goes beyond traditional statistics, including textual commentary, metadata, market odds, and categorical variables. Covering the 2011/2012 to 2016/2017 seasons, the research aims to predict outcomes and explore player-centric analysis, particularly through expected goals (xG) models. By pushing the boundaries of football analytics, the study provides nuanced insights into player performance and team strategies. Traditional statistics fall short, and the integration of advanced machine learning models enhances our understanding of events shaping a football match. The chosen models, Gradient Boosting Classifier and Logistic Regression, handle intricate datasets, investigating questions like the significance of a shot, ideal scoring conditions, and player performance variations. The research’s implications extend to soccer enthusiasts, analysts, and professionals by uncovering underlying patterns, enhancing comprehension beyond surface-level statistics, and redefining football analysis through the power of machine learning and player-centric insights. In essence, ”Unveiling the Game” represents an innovative effort to reshape football analysis.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Milosavljevic, Vladimir UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science G Geography. Anthropology. Recreation > GV Recreation Leisure > Sports > Soccer Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	22 May 2025 16:04
Last Modified:	22 May 2025 16:04
URI:	https://norma.ncirl.ie/id/eprint/7610

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