Figure 6 - uploaded by Peter Lamb
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Second SOM representing the similarity of different basketball shot types by each of the players in Lamb, Bartlett and Robins (2010). For example, Player 1's hook shot is abbreviated as 1(H). Adapted from Lamb, Bartlett and Robins (2010).
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... et al. (2010) used a second SOM to show the relationship between the different basketball shots performed by each of the four players. The authors referred to the original SOM as the phase SOM (Figure 4) and the second as the trial SOM (see Figure 6). The output of the trial SOM was visualised on a grid for which, because of its smaller size, the distances between all nodes could be calculated fairly accurately and plotted. ...
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... The ongoing monitoring and analysis of patterns of coordination and control in relation to performance outcomes over practice sessions should enable the sports biomechanist to distinguish between those patterns of coordination and control that tend to produce successful performance outcomes from those that do not, thereby enabling a biomechanical profile representing an 'optimal' technique for each athlete to be created. The process of constructing this profile may be facilitated by the use of emerging paradigms, such as coordination profiling (Button, Davids, & Schöllhorn, 2006), which combine analytical methods, such as continuous relative phase (e.g., Lamb & Stöckl, 2014), vector coding (e.g., , self-organising maps (e.g., Lamb & Bartlett, 2013), and principal component analysis (e.g., Federolf, Reid, Gilgien, Haugen, & Smith, 2014) with an individualbased, longitudinal, repeated measures design. This profile can then be used as a basis for providing transition information that specifies changes required to patterns of coordination and control on the trial following an unsuccessful trial that may result in improved performance outcomes (e.g., . ...
Biomechanical feedback technologies are becoming increasingly prevalent in elite athletic training environments but how the kinematic and kinetic data they produce can be best used to improve sports techniques and enhance sports performance is unclear. This paper draws on theoretical and empirical developments in the motor control, skill acquisition, and sports biomechanics literatures to offer practical guidance and strategic direction on this issue. It is argued that the information produced by biomechanical feedback technologies can only describe, with varying degrees of accuracy, what patterns of coordination and control are being adopted by the athlete but, crucially, it cannot prescribe how these patterns of coordination and control should be modified to enhance sports performance. As conventional statistical and theoretical modelling paradigms in applied sports biomechanics provide limited information about patterns of coordination and control, and do not permit the identification of athlete-specific optimum sports techniques, objective criteria on which to base technical modifications that will consistently lead to enhanced performance outcomes cannot reliably be established for individual athletes. Given these limitations, an alternative approach, which is harmonious with the tenets of dynamical systems theory and aligned with the pioneering insights of Bernstein (1967) on skill acquisition, is advocated. This approach involves using kinematic and kinetic data to channel the athlete’s search towards their own unique ‘optimum’ pattern of coordination and control as they actively explore their perceptual-motor workspace during practice. This approach appears to be the most efficacious use of kinematic and kinetic data given current biomechanical knowledge about sports techniques and the apparent inability of existing biomechanical modelling approaches to accurately predict how technique changes will impact on performance outcomes for individual athletes.
