Anthropometry, Physical Fitness, Sport-Specific Performance and the Prediction of Performance Level in Young Canoe Sprint Athletes

Saal, Christian; Helm, Norman; Prieske, Olaf

doi:10.1007/978-3-030-99333-7_11

Cited by 1 publication

(1 citation statement)

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“…In sports science, and in rugby league football, traditional (i.e., physical and technical-tactical) performance indicators that are used to characterize match games [1,2] are widely used as variables for various performance analysis problems. In [3], cluster analysis was primarily carried out on data characterised by traditional indicators (e.g., high-speed running distance and total tackles) to determine the similarity between playing positions (i.e., fullbacks, centres, half-backs, wingers, hookers, prop-forwards, second-rows and loose-forward) of professional rugby league.…”

Section: Introductionmentioning

confidence: 99%

The use of match-based exact movement activities to classify elite rugby league players into positional groups

Adeyemo,

Palczewska,

Jones

et al. 2023

Preprint

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The cluster analysis of elite rugby league players identified groups of distinct playing positions, that can be referred to as broad positional groups. However, the identified positional groups were based on traditional indicators (physical and technical-tactical) that provided no information about the exact match-based movement activities that led to such similarity grouping and the classification of elite rugby league players into these broad positional groups remains unexplored. Hence, this study finds the best model to classify elite rugby league players into positional groups, using data characterised by movement patterns to uncover the similar movement activities of distinct playing positions within a positional group. Key movement patterns for the positional group classification and differences between the groups were also investigated. A total of 18,173 unique movement patterns were derived from 422 players' GPS data across the 2019 and 2020 seasons, where only 36 were identified as key patterns. The highest classification accuracy of 77.58% using all unique patterns and 74.5% accuracy using the key patterns was achieved, outperforming studies that used traditional indicators. Further analyses, based on key patterns revealed differences between forwards and backs. These findings establish movement patterns as viable indicators to classify rugby league players into positional groups, enabling coaches and trainers to develop position-specific training programs that cater to the unique physical demands of each position, leading to better player development and team performance. Movement patterns are therefore recommended as an alternative approach to quantifying players' external loads and obtaining granular information.

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Section: Introductionmentioning

confidence: 99%