The Application of Machine Learning to Enhance Performance Analysis in Australian Rules football
Year: 2020
Type: dissertation
Abstract: In this thesis, machine learning techniques are applied to enhance the development and implementation of methodologies in performance analysis. Ecological dynamics is used as a theoretical framework to underpin these methodologies. Australian Rules football is used as an exemplar to understand the influence and interaction of constraints on player and team dynamics. There is extensive theoretical research on the interaction of constraints in sport, however common analysis techniques have typically only explored one or two constraints and therefore do not fully reflect the complexity of the competition environment. To better understand the competition environment, the nexus of constraints must be considered in the analysis of sport. This thesis aims to address this gap. Firstly, this thesis explores how the use of ecological dynamics may aid the implementation of an interdisciplinary approach to sports performance research. These considerations are applied to Australian Football field and goal kicking, by exploring how multiple constraints interact and impact skilled performance, and how these differ between competition tiers. Furthermore, differences between analysis techniques are identified and aspects such as feasibility and interpretability are highlighted to facilitate an improved translation of research to the applied setting. Additionally, this analysis is furthered by exploring event sequences, determining not only the influence of multiple constraints around a disposal but also the preceding events. This thesis aims to advance the application of methodologies that explore multiple constraints and sequences of events, in order to enhance knowledge of the competition and training environments.
Author: Peter Browne
Language: English
Subfield: Orthopedics and Sports Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3192297822