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.
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