five

Threshold for defensive specialisation.

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Figshare2023-08-16 更新2026-04-28 收录
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https://figshare.com/articles/dataset/Threshold_for_defensive_specialisation_/23970719
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The objective of this study is to automate and analyse the quantification of external load during an elite men’s handball match. This study was carried out using data from a local positioning system and inertial measurement units. The literature review leads us to assume that physical demands are different depending on position, player specialty and phases of the game. In order to do this analysis, raw data was used from professional competitors of a Spanish club during National and European competition matches. First, a game phase algorithm was designed to automate phase recognition. Then, a descriptive evaluation of the means and standard deviation was performed with the following variables: total distance, total time, total Accel’Rate, the percentages of distance and time per speed and displacement direction. A Kruskal Wallis test was applied to normalized distance and normalized Accel’Rate. Defensive play showed the highest values on covered distance (930.6 ± 395.0 m). However, normalized distance showed significant differences (p
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2023-08-16
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