Modelling the Influence of Task Constraints on Goal Kicking Performance in Australian Rules Football
Browne, Peter ORCID: 0000-0002-3943-707X, Sweeting, Alice ORCID: 0000-0002-9185-6773 and Robertson, Samuel ORCID: 0000-0002-8330-0011 (2022) Modelling the Influence of Task Constraints on Goal Kicking Performance in Australian Rules Football. Sports Medicine - Open, 8. ISSN 2199-1170
Abstract
Background: The primary aim of this study was to determine the influence of task constraints, from an ecological perspective, on goal kicking performance in Australian football. The secondary aim was to compare the applicability of three analysis techniques; logistic regression, a rule induction approach and conditional inference trees to achieve the primary aim. In this study, an ecological perspective has been applied to explore the impact of task constraints on shots on goal in the Australian Football League, such as shot type, field location and pressure. Analytical techniques can increase the understanding of competition environments and the influence of constraints on skilled events. Differing analytical techniques can produce varying outputs styles which can impact the applicability of the technique. Logistic regression, Classification Based on Associations rules and conditional inference trees were conducted to determine constraint interaction and their influence on goal kicking, with both the accuracy and applicability of each approach assessed. Results: Each analysis technique had similar accuracy, ranging between 63.5% and 65.4%. For general play shots, the type of pressure and location particularly affected the likelihood of a shot being successful. Location was also a major influence on goal kicking performance from set shots. Conclusions: When different analytical methods display similar performance on a given problem, those should be prioritised which show the highest interpretability and an ability to guide decision-making in a manner similar to what is currently observed in the organisation.
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Item type | Article |
URI | https://vuir.vu.edu.au/id/eprint/45095 |
DOI | 10.1186/s40798-021-00393-9 |
Official URL | http://dx.doi.org/10.1186/s40798-021-00393-9 |
Subjects | Current > FOR (2020) Classification > 4207 Sports science and exercise Current > Division/Research > Institute for Health and Sport |
Keywords | machine learning, performance analysis, constraints-led approach, visualisations |
Citations in Scopus | 3 - View on Scopus |
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