title: Patterns of barbell acceleration during the snatch in weightlifting competition.
abstract: The purpose of this study was to determine the association between weightlifting performance and vertical barbell acceleration patterns. Barbell kinematic time-series data were tracked from 18 snatches from six weightlifters during a regional weightlifting competition. These data were used to calculate vertical barbell accelerations. Time-series data were normalised to 100% of lift phase, defined as the time interval between barbell lift-off and maximum height of the barbell during each snatch lift. The time-series data were then entered into a pattern recognition algorithm that extracted principal patterns and calculated principal pattern scores. Body mass-normalised lift weight, which was used to quantify weightlifting performance, was significantly correlated (r = 0.673; P = 0.033) with a pattern that captured a difference in peak vertical barbell acceleration between the transition and the second pull phase. This correlation indicated that barbell acceleration profiles of higher weight snatch lifts were characterised by smaller decreases in acceleration during the second knee bend and smaller peak acceleration during the second pull phase. Weightlifting coaches and sports scientist should monitor and track vertical acceleration of the barbell, with focus on acceleration profiles that limit (1) deceleration during the transition phase between the first and second pull and (2) peak acceleration during the second pull phase of the snatch.
authors.affiliation: a Department of Physical Therapy , Marquette University , Milwaukee , WI , USA.
authors.forename: Kristof
authors.initials: K
authors.lastname: Kipp
authors.forename: Chad
authors.initials: C
authors.lastname: Harris
mesh_terms: D000054:Acceleration; D054874:Athletic Performance / Q000502:physiology*; D001696:Biomechanical Phenomena; D006801:Humans; D025341:Principal Component Analysis; D013996:Time and Motion Studies; D014891:Weight Lifting / Q000502:physiology*
keywords: biomechanics; functional data analysis; principal component analysis