1. sports

“Technique, style and performance in
sport: biomechanical variations on a
theme?”
By
Carlton Cooke, Chris Low, Nassos Bissas, Giorgos
Paradisis & Barney Wainwright
(Carnegie Research Centre for Sport Performance)
The presentation







Defining skill, technique, style and constraints (High
Jump)
Analysing technique – 3 main steps
Biomechanical Models – understanding variations in
technique and style in performance (Kayak paddling)
Variations in response to training (Sprint running)
Dynamical systems theory (Gymnastics)
The Uncontrolled Manifold (Football)
Conclusion
Definitions:
Biomechanical classifications of movement
• General Movement Patterns (e.g. Jumping)
• Skill (e.g. High Jump)
• Technique (e.g. Fosbury Flop)
• Style (Individual variation in the
performance of Technique)
• Primary Mechanical Purpose (height of
clearance, Objective/Outcome/Performance)
(Kreighbaum & Barthels, 1996)
Mechanics of the Fosbury flop

Approach velocity is a predictor of height
jumped

Hip height at take off is a predictor of height
jumped
Why do some international high jumpers
“buckle” ?
(i.e. not even leave the ground)


Not all Fosbury flops are the same (variation)
Dapena (1980a and b) Medicine and Science in Sports and Exercise
Factors effecting “Style” in Fosbury flop

Factors effecting “Style” i.e. constraints
• Leg strength and power
• Flexibility
• Height
• Weight
• Body composition
• Individual constraints are variable between
jumpers
• What about variations within a jumper
between attempts?
Dapena (1980a and b) Medicine and Science in Sports and Exercise
Analysis of technique

3 main steps:
observation - several aids developed
evaluation - fault diagnosis
intervention - poorly addressed
Observation



Phase Analysis - descriptive process to divide
movements into constituent parts
Temporal Analysis - builds on phase analysis
by specifying the timing of a movement
Critical Features - components of movement
that are essential to the performance of a skill
Evaluation




Coaching Manuals - descriptive templates
based on expert performance
Diagnosis of faults determined by deviations
from the template
Aware of variations in performance level and
individual differences
Criticisms of this approach based on premise
that success and high technical skill have a
reciprocal relationship (Hay & Reid, 1982;
Bartlett, 2007)
Hierarchical or deterministic models
The model must be based upon fundamental mechanics that govern the
movement, and each factor must be completely determined by those
factors that appear in the level directly below it.
(Glazier et al., 2007; Hay & Reid, 1982)
Novel Sprint Running Training
(uphill-downhill ramp 3 degree slope)
()
DCM
()
()
Bissas and Paradisis (PhDs)
()
Hierarchical Model of Sprint Running


Running Speed
Step Length


DCM TO
Step Rate

Step Time

DCM TD
Flight Distance


Physique

Posture
 knee angle ()
 hip angle ()
 shank angle ()
 trunk angle ()

Contact Time

Eccentric

Concentric
Acceleration (g)
Height TO
Air Resistance
Speed TO
 thigh angle ()
Velocity TD
Velocity change
Force Exerted
Paradisis and Cooke (2001) Journal of Sports Sciences
Flight Time
Time Forces Act
-1
Running Velocity (m.s )
Group changes in max running velocity (MRV)
Bissas PhD
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
**
**P<0.01
Training
Control
Group changes in stride rate
Stride Rate (Hz)
0.3
0.2
0.1
0
-0.1
**
-0.2
**P<0.01
Training
Bissas PhD
Control
Individual variation in response to training
25.0%
Improvement
Post-Pre Training Changes (%)
20.0%
15.0%
10.0%
5.0%
0.0%
-5.0%
Decline
-10.0%
N=10
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
MRV
4.0%
2.4%
-1.4%
6.4%
2.4%
3.4%
5.5%
9.1%
11.1%
1.9%
2.5%
SR
3.6%
6.3%
0.0%
3.2%
2.7%
3.5%
3.0%
6.6%
10.2%
0.0%
0.0%
SL
0.5%
-3.8%
-1.4%
3.2%
-0.5%
0.0%
2.3%
2.2%
0.7%
1.9%
2.5%
CT
4.7%
6.7%
6.7%
5.9%
5.6%
6.3%
6.3%
0.0%
0.0%
0.0%
5.9%
FT
2.3%
5.6%
-5.9%
0.0%
0.0%
0.0%
0.0%
11.1%
18.8%
0.0%
-7.1%
Participants
Bissas PhD
Dynamical Systems Theory

Motor control theory that looks at how multiple
degrees of freedom are controlled (Utley & Astill,
2008)

The athlete is considered as a complex, biological
system (Davids et al., 2008)

Consider the system as a whole, where the parts of
the system interact and affect each other.
Dynamical Systems Theory

Functional role of variability in analysis of movement

DST contrasts with information processing view that
variability is noise in the sensorimotor system that
needs to be removed

In DST concept of representative trial does not exist
Dynamical Systems Theory
(Newell 1986 model)
Coherent framework for understanding how co-ordination
patterns emerge during goal directed behaviour
Environmental
Perception
Functional
co-ordination
pattern
selected
under
Action
constraint
Organismic
Task
(Davids et al., 2008)
Participant and performance


A former member of the men’s national
gymnastics squad performed one trial of 12
continuous backward longswings on the Men’s
Horizontal Bar at self-selected speeds in the
following order: 3 normal, 3 fast, 3 slow, 3
fast
He then completed a second trial performing a
Kovacs. All trials were performed on a
standard competition high bar.
Data capture
Qualisys Capture System
Capture freq:150Hz
Ave. Residual of cameras <
1mm
S.D. Wand length 2mm
Data Processing



Motion data into
Visual3D
Butterworth filter with
cut-off at 10Hz
Calculated planar
angles at shoulder and
hip
wrist
shoulder
hip
knee
Mean RMSD values between Kovacs Prep &
Action and Longswings performed at different
self-selected speeds
θS (°)
ωS (°s-1)
θH (°)
ωH (°s-1)
Kovacs Prep
Kovacs Action
Kovacs Prep
Kovacs Action
Kovacs Prep
Kovacs Action
Kovacs Prep
Kovacs Action
Normal
5
6
46
61
7
22
70
183
Fast 1
5
6
47
56
7
19
54
156
Kovacs Prep = initial longswing
Kovacs Action = longswing before Kovacs
Slow
5
7
56
62
7
23
73
183
Fast 2
6
5
47
51
5
18
41
151
Kovacs and variations in longswings



The lower RMSD values for the fast longswings
indicates that varying the speed of the
longswing can lead to greater similarities
between the longswing action and the Kovacs
skill.
Functional variability of the longswing action
may therefore be useful in the acquisition of
the Kovacs, suggesting that longswing
progressions should encourage the
development of variable longswing
movements.
Interestingly, there were greater similarities in
the hip joint motion observed in the fast
longswings performed after a series of slower
longswings, suggesting that sequence of
speed variation may be important.
Low and Cooke (2008)
Conclusions on Kovacs & longswings

Sequential variation in the speed of
longswings induced movements that have a
greater similarity to those movements
associated with a high level skill.

Functional variability in the longswing action
may therefore be beneficial to gymnasts in
terms of acquisition of high level skills, such
as the Kovacs.
Low and Cooke (2008)
What is next?



Chris will keep working on gymnastics
New PhD student looking at intra subject
variability in football kicking
Both will be looking at the possibility of
partitioning variability into functional and
dysfunctional variation using a quantitative
technique known as the “uncontrolled
manifold” (UCM) (Latash et al, 2003).
The Uncontrolled Manifold (UCM)



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The UCM establishes if trial-to-trial variability of
elemental variables shows a stability in performance
variables (Latash et al, 2007).
The elemental variables describe degrees of freedom in
the motor system for the task.
The performance variable(s) describe what is essential
in fulfilling the task variable (e.g. foot position and
velocity when kicking the ball).
The task variable depends on the outcome of a specific
performance variable (e.g. the task variable of kicking
accuracy is dependent on the performance variable of
foot position relative to the ball at the time of the kick).
The Uncontrolled Manifold (UCM)

The UCM links the variance of elemental variables and
variance of a performance variable, using the Jacobian
matrix.

The Jacobian matrix partitions the variance of the
elemental variables into two:
1. that indicates flexible combinations of elemental
variables across trials leading to the same value of
the performance variable or,
2. changes in the performance variable.

If 1 is greater than 2 the performance variable is
stabilised by compensation among the elemental
variables and a SYNERGY is said to exist . The higher 1
is, the greater the amount of compensated variability,
which suggests a stronger synergy and more stability.

Therefore, the UCM goes beyond analysing the
variability within a technique by also indicating whether
the variability is useful or not.
Conclusion




Variability can be positive and negative in
sports-specific tasks
Variation can assist in providing flexible
movement solutions for successful
performance
Constraints can limit performance
Understanding the different dimensions of
inter and intra variability in technique, style
and how they do or don’t explain performance
in sport is key to not only biomechanists, but
also performers, coaches, and teachers.
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