Abstract
Part one of this study described the three-dimensional kinematics of male and female top level and junior elite volleyball players during a spike. Different strategies for generating a high impact speed for the hand were observed between the groups. This part focuses on variability in coordination and performance and will use a single-subject approach for the data-analysis. The research question concerns the relationship between coordination variability and skill level. Two hypotheses exist about this relationship: a continuous decrease of variability or a U-shaped relationship when skill level increases. We used different measures of skill level during this study. The discrete measure (top level vs. junior elite) showed no difference in coordination variability. The continuous measures showed both U-shaped and linear relationships with coordination variability when data from all groups were pooled together. No relationships were observed within the groups. Together with the insight gained from the mechanical analysis from part one, knowledge about the coordination variability can be used for guiding the training and learning process of youth elite volleyball players.
Highlights
Following the dynamical systems theory, variability in coordination is a functional necessity for both normal and optimal motor behavior [1]
In the context of a volleyball spike motion, coordination variability allows athletes to adapt to changing constraints
Normal distribution of coordination variability was assured by the QQ-plot and the Shapiro-Wilk test (p = 0.171) and homogeneity of variance satisfied Levene’s test (p = 0.343)
Summary
Following the dynamical systems theory, variability in coordination is a functional necessity for both normal and optimal motor behavior [1]. Many movements in sports (such as the volleyball spike) are characterized by a very high dimensional (multi-articular, multiple degrees of freedom per joint) discrete action and tools like Self-Organizing Maps (SOMs) can be a better choice [13] These SOMs have been applied to analyze discrete sports motions as discus throwing [14], golf chip shots [15] and basketball shots [16]. Using Self-Organizing Maps, studies of biomechanics in volleyball spikes and services like in part one of this study and [18]–[21] can be complemented by studies on coordination (variability) to construct a more holistic approach of the movement dynamics This may be beneficial for both trainers and physiotherapists working with athletes [22]
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