Abstract
Background: Aiming point analysis systems (APAS) are commonly used in sports shooting but face four main challenges: they do not account for intra-session variations, they overlook inter-individual shooter preferences, they ignore compensation mechanisms of technical features, and they do not respect the real shot location at the target. Methods: To address the first three challenges, we developed and validated an automated approach detector algorithm (ADA) for movement phase detection. When compared to three independent expert ratings, the ADA demonstrated a high correlation (r = .811). Building on the ADA and addressing challenge 3 and 4, this study applied cluster-analysis and ANOVA to determine the performance relevance of compensation-sensitive shot styles using datasets from a single athlete and 26 advanced to elite level athletes. Results: Significant performance differences in shot styles for both datasets, with each shot style distinctively differing from the others could be found. Conclusions: Shot styles which allow for compensation and intra-individual movement phase differences exhibit performance variations. Coaches and athletes should emphasize holistic training, focusing on combinations of features that allow for compensation.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
