Introduction to Sports Biomechanics: Analysing Human Movement ...

involved as an analyst. These activities include athletic throwing and jumping events,
cricket, basketball, hockey and rugby. The lack of any ‘acrobatic’ sports in the last
sentence probably colours the inclusion or otherwise of some movement principles in
Box 2.3 (and 2.4). I also include in Box 2.4, somewhat tongue-in-cheek, some so-called
movement principles that I have not found very useful in my work.
Deterministic modelling
Principles of deterministic modelling
QUALITATIVE ANALYSIS OF SPORTS MOVEMENTS
Deterministic models of sports activities, also known as hierarchical models as they
descend a hierarchical pyramid, can be developed using a structure chart, for example
that in Microsoft PowerPoint (Microsoft Visio, probably less familiar to most readers, is
far better for such modelling). The first principle of hierarchical modelling is to
identify the ‘performance criterion’, the outcome measure of the sporting activity. This
is often, in track and field athletics for example, to go faster, higher or further. In such
cases, we have a clear and objective performance criterion, such as race time, which we
seek to minimise, or distance jumped or thrown, which we seek to maximise. Splitting a
movement into phases, as I have done below for the long jump (see also Appendix 2.2),
can not only aid the establishment of a deterministic model but also help the identification
of objective performance criteria for each phase.
In sports involving a subjective judgement, such as gymnastics, identifying a performance
criterion may be far more difficult. The score awarded by the judges will
not depend upon a single performance factor, whether objective or subjective, but on
guidelines established by the sport. The constituent parts of such performances may
be analysable by deterministic models, for example the performance of a twisting
somersault or the pre-flight phase of a vault. On the other hand, they may be better
approached using the judging guidelines of that sport, which are based mainly on technical
elements of the skills involved; this approach will be discussed briefly on pages 71–2.
Some sports, such as ski jumping, combine objective (distance) and subjective (style)
criteria – the former lends itself to being modelled deterministically, unlike the latter.
The next stage is to subdivide the performance criterion, where possible, as in the
example below. Then comes the crucial stage of identifying critical features, also known,
particularly when the model is developed for quantitative rather than qualitative
analysis, as performance variables or parameters. Once this is done, and the model has
been developed to the necessary stage – which should arrive at observable features in
a qualitative analysis or measurable ones for a quantitative analysis – it needs to be
evaluated and its limitations noted. Generally, the critical features highlighted in the
model will be biomechanical features or variables such as joint angles, or body segment
parameters such as a skater’s moment of inertia. Generally, it is advisable not to use
ambiguities, such as ‘timing’ or ‘flexibility’ or even, perhaps, ‘coordination’. If critical,
these should be identified more precisely, such as specifying why hamstring flexibility is
important because it improves the joint range of movement, or which joint movements
need to be coordinated and how.
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