How performance analysis of elite long jumping can inform representative training design through identification of key constraints on competitive behaviours

Abstract

Analysing performance in competitive environments enables identification of key constraints which shape behaviours, supporting designs of more representative training and learning environments. In this study, competitive performance of 244 elite level jumpers (male and female) was analysed to identify the impact of candidate individual, environmental and task constraints on performance outcomes. Findings suggested that key constraints shaping behaviours in long jumping were related to: individuals (e.g. particularly intended performance goals of athletes and their impact on future jump performance); performance environments (e.g. strength and direction of wind) and tasks (e.g. requirement for front foot to be behind foul line at take-off board to avoid a foul jump). Results revealed the interconnectedness of competitive performance, highlighting that each jump should not be viewed as a behaviour in isolation, but rather as part of a complex system of connected performance events which contribute to achievement of competitive outcomes. These findings highlight the potential nature of the contribution of performance analysis in competitive performance contexts. They suggest how practitioners could design better training tasks, based on key ecological constraints of competition, to provide athletes with opportunities to explore and exploit functional intentions and movement solutions high in contextual specificity.

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European Journal of Sport Science
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How performance analysis of elite long jumping
can inform representative training design through
identification of key constraints on competitive
behaviours
Chris Mccosker, Ian Renshaw, Daniel Greenwood, Keith Davids & Edward
Gosden
To cite this article: Chris Mccosker, Ian Renshaw, Daniel Greenwood, Keith Davids & Edward
Gosden (2019): How performance analysis of elite long jumping can inform representative training
design through identification of key constraints on competitive behaviours, European Journal of
Sport Science
To link to this article: https://doi.org/10.1080/17461391.2018.1564797
Published online: 07 Jan 2019.
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ORIGINAL ARTICLE
How performance analysis of elite long jumping can inform
representative training design through identification of key constraints
on competitive behaviours
CHRIS MCCOSKER
1,2
, IAN RENSHAW
1,2
, DANIEL GREENWOOD
3
,
KEITH DAVIDS
4
, & EDWARD GOSDEN
1
1
Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia;
2
School of Exercise
& Nutrition Sciences, Queensland University of Technology, Brisbane, Australia;
3
Movement Science, Australian Institute of
Sport, Bruce, Australia &
4
Centre for Sports Engineering Research, Sheffield Hallam University, Sheffield, UK
Abstract
Analysing performance in competitive environments enables identification of key constraints which shape behaviours,
supporting designs of more representative training and learning environments. In this study, competitive performance of
244 elite level jumpers (male and female) was analysed to identify the impact of candidate individual, environmental and
task constraints on performance outcomes. Findings suggested that key constraints shaping behaviours in long jumping
were related to: individuals (e.g. particularly intended performance goals of athletes and their impact on future jump
performance); performance environments (e.g. strength and direction of wind) and tasks (e.g. requirement for front foot
to be behind foul line at take-off board to avoid a foul jump). Results revealed the interconnectedness of competitive
performance, highlighting that each jump should not be viewed as a behaviour in isolation, but rather as part of a complex
system of connected performance events which contribute to achievement of competitive outcomes. These findings
highlight the potential nature of the contribution of performance analysis in competitive performance contexts. They
suggest how practitioners could design better training tasks, based on key ecological constraints of competition, to provide
athletes with opportunities to explore and exploit functional intentions and movement solutions high in contextual specificity.
Keywords: Performance analysis, long jump, representative learning design, ecological dynamics, interacting constraints
Highlights
.The use of ecological dynamics to guide the analysis of competition data shows how performance analysis can be enhanced
to enrich the understanding of athlete interactions during competition.
.Key constraints shaping behaviours in long jumping were related to: individuals, performance environments and tasks.
.Each jump should not be viewed in isolation, but rather as part of a complex system of connected events contributing to the
achievement of competitive outcomes.
.Integrated manipulation of key constraints in training can provide opportunities for athletes to adapt to major physical and
emotional demands of performance environments.
Performance analysis in sport competition provides a
quantitative link between application, science and
theory through an objective audit of athlete or team
behaviours (Hughes & Bartlett, 2002; McGarry,
2009). Performance is traditionally described
through evidence gained from notational analysis
using competition, technical and tactical indicators,
as well as biomechanical technique descriptors
using kinematic and kinetic variables. In sports like
track and field, performance analysis has predomi-
nantly taken the form of movement analysis. For
example, in long jump, most analyses have been
driven by biomechanical (e.g. Bridgett & Linthorne,
2006; Hay, 1993) and motor control research
(e.g. Glize & Laurent, 1997; Montagne, Glize,
Cornus, Quaine, & Laurent, 2000) in controlled,
experimental or training environments (for an excep-
tion, see Hay, 1988). Whilst these studies have
© 2019 European College of Sport Science
Correspondence: Chris McCosker, School of Exercise & Nutrition Sciences, Queensland University of Technology, Victoria Park Road,
Kelvin Grove, QLD 4059, Australia. E-mail: chrismccosker05@gmail.com
European Journal of Sport Science, 2019
https://doi.org/10.1080/17461391.2018.1564797

increased understanding of performance variables,
insufficient attention has been paid to analysing
how long jump performance under the specific con-
straints of competition environments might impact
self-regulation in athletes. Performance analysis,
investigating competition behaviours, could enrich
understanding of self-regulatory interactions of ath-
letes with the environment during practice, revealing
links between strategies, psychological states,
emotions and actions in individual athletes (Ander-
son, 2018; Hughes & Bartlett, 2002).
With a large range of variables available to analyse
during long jump competition performance, it is
important that selection and interpretation of data
are guided by an appropriate theoretical framework.
One proposed framework is ecological dynamics
which has enhanced the understanding of perform-
ance and learning in a variety of sport contexts
(Araújo, Davids, & Hristovski, 2006; Vilar, Araújo,
Davids, & Button, 2012; Warren, 2006). Ecological
dynamics proposes how human behaviour emerges
through continuous interactions with affordances
(opportunities for action) available during perform-
ance, as multiple constraints act on the (athlete-
environment) system (Araújo et al., 2006; Araújo,
Davids, & Passos, 2007; Gibson, 1979), providing
rich information for self-regulation. Adopting this
theoretical framework to guide the analysis and
interpretation of performance in long jump, moves
performance analysis beyond merely documenting
discrete variables from isolated events within compe-
tition. Such an approach allows for the recognition of
the conditioned coupling evident in dynamic per-
formance environments where constraints are
deeply intertwined to shape athlete performance
(Vilar et al., 2012). Practically, identifying these con-
straints provide practitioners with the opportunity to
enhance the development of representative training
designs where intentions, perceptions and actions
emerge in faithful simulations of a performer’s
actions in competition (Pinder, Davids, Renshaw, &
Araújo, 2011).
Current empirical research on how ecological
dynamics can enrich performance analysis highlights
the unique interactions of individual, environmental
and task constraints that shape the emergence of
athlete performance behaviours (Travassos, Duarte,
Vilar, Davids, & Araújo, 2012; Vilar, Araújo,
Davids, & Bar-Yam, 2013; Vilar et al., 2012). Pre-
vious research on personal constraints suggests that
a key variable that shapes the perception–action
couplings of athletes is specific intentions during
the performance. Athlete intentionality concerns the
adoption of specific performance goals (i.e. winning
a competition, making the podium, qualifying for a
final, jumping conservatively to avoid a ‘no-jump’),
constrained by the particular needs, wishes and
desires of an athlete at a particular point in time
(Araújo, Davids, & Renshaw, 2019). To exemplify,
intentions to make a ‘safe’jump or a jump for
maximal distance clearly influence running velocity
and foot placement error on the take-off board (Brad-
shaw & Sparrow, 2000; Maraj, Allard, & Elliot, 1998).
This practical example illustrates how athletes might
deliberately adapt movement behaviours in order to
complete a task in a specific way, related to current per-
formance goals or competitive needs. The successful
(or unsuccessful) execution of specific performance
strategies is likely to impact future jump performance
as the athlete adapts to his/her emerging needs in an
unfolding competitive event, with interconnected
performance trials. Each jump within a competition
comprises a complex system, a series of connected
events to influence overall competitive performance
outcomes (Renshaw & Gorman, 2015). This
complex system of competitive jumps can be per-
turbed by emerging cognitive-emotional-physical
demands at a specific performance event (Headrick,
Renshaw, Davids, Pinder, & Araújo, 2015).
Environmental constraints, including physical (i.e.
wind, ambient light, temperature, altitude, air
density) and social variables (i.e. family support,
peer groups, an evaluating audience and cultural
norms) can also influence athletic performance. In
long jumping, the influence of wind speed and direc-
tion on jump performance is unique as the stability of
running and jump components can be perturbed
during task execution. Mathematical modelling has
suggested influences on long jump distance of
between 0.08 and 0.12 m for a 1 m/s increase or
decrease in wind velocity (de Mestre, 1991; Ward-
Smith, 1985). The effects from drag during the
aerial phase and running velocity during the approach
run are primary causes of an increase in jump per-
formance (Ward-Smith, 1985). The influence of
wind on jump performance is compounded by sport
regulations preventing a change in the direction of
an athlete’s run-up if there is a change in weather con-
ditions during competition (Competition Rules,
2014–2015,2013). This type of environmental con-
straint emphasises the importance of attunement to
potential variability in performance conditions when
preparing for competition by elite athletes.
Task constraints are more specific to performance
contexts than environmental constraints (Davids,
Button, & Bennett, 2008) and include the rules of a
sport. In long jumping, the key rule is the requirement
to keep the front foot behind the take-off line to reg-
ister a legal jump, constraining run-up strategies.
Research on the run-up approach in long jumping
(e.g. Lee, Lishman, & Thomson, 1982; Montagne
et al., 2000) has demonstrated that the presence of
2C. Mccosker et al.

the take-off board, in comparison to jumping con-
ditions with no take-off board, led to changes in
foot placement throughout the entire run-up as well
as lower levels of footfall variability (Maraj, 1999).
The need to intercept an object or surface, such as a
20 cm wide take-off board, when completing a task
nested at the end of a run-up (i.e. jumping) has impor-
tant implications for training design. Gait regulation
strategies in run-ups with the absence of a nested
jumping task show few similarities with performance
in tasks requiring a jump at the end (Bradshaw &
Aisbett, 2006; Glize & Laurent, 1997).
Identifying interacting constraints that shape
exploration and utilisation of affordances (opportu-
nities for action) in competition provides practitioners
with a better understanding of the performance
environment, thereby enhancing their capacity to
design more effective practice tasks. Ecological
dynamics proposes how training environments could
be designed to provide athletes with opportunities to
attune and calibrate their intentions, perceptions and
actions in the landscape of affordances representative
of competitive performance (Pinder et al., 2011).
Such learning designs can enhance athlete adaptation
to the dynamics of a competitive performance environ-
ment, ready to self-regulate their behaviours as a com-
petitive event unfolds. Currently, there is limited
research investigating the constraints of competition
in long jumping and there is a need for a more in-
depth analysis of performance in elite long jump com-
petitions. Consequently, this study aimed to investigate
how performance analysis, under the framework of
ecological dynamics, can lead to the identification of
more contextual information for the design of practice
environments. These sources of information could
better reflect the intertwined interactions that emerge
in between athlete intentions, perceptions and actions
in adapting to changing event conditions. Elite level
long jumping will be used as the exemplar, with
key individual, environment and task constraints ident-
ified through the statistical analysis of elite long jump
competitions held between 1999 and 2016. These
competitions will include Olympic Games, World
Championships and Diamond League competitions.
Methods
Results from 108 (men = 56; women = 52) elite level
long jump competitions were obtained from publicly
available online databases (www.iaaf.org.au &
www.diamondleague.com). These competitions
included Diamond League competitions staged
between 2011 and 2016 (men = 42; women = 39)
and World Championships (men = 9; women = 8)
and Olympic Games (men 5; women = 5)
competitions between 1999 and 2016. These events
covered a total of 244 athletes (male = 140; female
= 104) with 5392 jumps (male = 2785; women =
2607) available for analysis. Two jumps under 2 m
were excluded as outliers in the men’s dataset as
they were not reflective of a genuine attempt at a
jump at that performance level. Only performances
of athletes in competitions where all wind (m/s) and
horizontal jump distance (m) data were available,
were included in the analysis.
Candidate variables that may potentially impact on
performance were selected using an ecological
dynamics rationale and the experiential knowledge of
elite long jumping coaches identified in previous
research (e.g. Greenwood, Davids, & Renshaw, 2012)
(Table I). For example, wind was selected as a candi-
date environmental variable, since mathematical mod-
elling has suggested that a 1 m/s increase or decrease
in wind velocity has a 0.08–0.12 m impact on jump dis-
tance in long jump (de Mestre, 1991;Ward-Smith,
1985). The conceptualisation that each jump forms a
part of a complex system, formed by a series of con-
nected events (Renshaw & Gorman, 2015), supports
the inclusion in the analysis of performance variables
including previous round foul, Round 1 foul, distance
of Round 1 jump, medal position after previous foul,
Top 8 previous round and previous round jump dis-
tance. It was predicted that these variables might
impact the intentions or strategy implemented by ath-
letes throughout a competitive event, and subsequent
movement (re)organisation, depending on their com-
petitive needs at a specific point in time (Bradshaw &
Sparrow, 2000; Maraj et al., 1998).
To determine the effects of competition on jump
distance, descriptive statistics were calculated for
each competition type with median jump distance
values compared using a Kruskal–Wallis test with
a Bonferroni correction for multiple comparisons
(p< .001). Effects of year of performance on jump
distance was calculated using multiple linear
regression (p<.001) and effects of round on jump
distance was determined using analysis of variance.
Post-hoc procedures (Tukey’s HSD) determined
where differences existed if statistically significant
differences were found.
To determine the variables that best predicted
horizontal distance jumped, a linear mixed model
with main effects, interactions and random intercepts
was constructed. Univariate tests were first con-
ducted to determine variables of significance. Vari-
ables tested for statistical significance appear in
Table I (excluding ‘Previous round jump distance’).
These variables were explored in order of significance
to determine the most parsimonious model explain-
ing the most variability and were assessed using
Akaike’s information criterion. Two-way interactions
How performance analysis of elite long jumping can inform representative training design 3

only were considered for the purposes of the analysis.
The statistical significance level was set at p= .05.
Descriptive statistics were calculated on jump
classification (legal and foul jumps) with the effects
of competition, round and time (years), on foul
jumps made, determined using chi-square test for
association and effect sizes. To determine variables
which best predicted foul jumps, binary logistic
regression was used. Variables included in the
regression calculation were identical to those used
in predicting jump distance with the addition of
‘Previous round jump distance’.
Results
Table II provides descriptive statistics for jump dis-
tance and jump classification across all competitions
Table I. Competition variables and definitions.
Competition variables
Constraint
classification Definition
Round Task Each competition consists of six rounds
Wind Environment Measured in metres per second. Readings must be under 2 m per second for jump to be
valid for team selection and records
Competition ID Environment Three competitions used for analysis (1) Diamond League or DL (2) World
Championships or WC and (3) Olympic Games or OG
Previous round foul Individual Previous round was classified as a foul
Round 1 foul Individual Round 1 jump was classified as a foul
Distance of Round 1 jump Individual Round 1 jump distance measured in metres
Medal position after
previous round
Individual Athlete enters round in either first, second or third position
Top 8 previous round Individual Athlete is in a Top 8 position entering the round. After the completion of Round 3,
athletes in the Top 8 positions are permitted 3 more jumps
Previous round jump
distance
Individual Previous round jump distance measured in metres
Table III. Jump distance and classification by round –men and women.
Round
Men’s competitions Women’s competitions
Total
jumps
analysed
Jump distance (m) Jump classification Total
jumps
analysed
Jump distance (m) Jump classification
Mean (±SD) Legal (%) Foul (%) Mean (±SD) Legal (%) Foul (%)
1 559 7.73
(±0.44)
406
(72.63%)
153
(27.37%)
509 6.45
(±0.33)
381
(74.85%)
128
(25.15%)
2 557 7.83
(±0.37)
378
(67.86%)
179
(32.14%)
506 6.49
(±0.30)
355
(70.16%)
151
(29.84%)
3 543 7.83
(±0.39)
383
(70.53%)
160
(29.47%)
501 6.47
(±0.35)
373
(74.45%)
128
(25.55%)
4 380 7.87
(±0.35)
269
(70.79%)
111
(29.21%)
374 6.50
(±0.34)
247
(66.04%)
127
(33.96%)
5 369 7.82
(±0.46)
252
(68.29%)
117
(31.71%)
361 6.49
(±0.41)
234
(64.82%)
127
(35.18%)
6 375 7.85
(±0.41)
249
(66.40%)
126
(33.60%)
356 6.49
(±0.39)
256
(71.91%)
100
(28.09%)
Table II. Jump distance and classification –men and women.
Jump distance Jump classification
Total jumps analysed Mean (±SD) Median (IQR) Legal (%) Foul (%)
Male 2783 7.81 (±0.40) 7.88 m (0.34) 1937 (69.90%) 846 (30.40%)
Female 2607 6.48 (±0.35) 6.52 m (0.33) 1846 (70.81%) 761 (29.19%)
4C. Mccosker et al.

for both men’s and women’s competitions. Multiple
linear regression showed a statistically significant
effect of the year of the competition (p< .001) with
mean distance jumped decreasing by 1.2 cm per
year for both men and women. The frequencies of
foul jumps showed a significant annual effect in
women’s competitions only, but the effect size was
small (χ
2
= 25.6, p= .019, ϕ= 0.099).
Table III provides descriptive statistics of the
effects of round on distance jumped and foul jumps
recorded for male and female competitions. Analysis
of variance demonstrated a significant effect of round
(F(5, 1931) = 5.425, p= .003) on distance jumped
for male competitions only. Post-hoc testing indi-
cated significant differences in distances jumped
between Rounds 1 and 2 (p= .005), Rounds 1 and
3(p= .008), Rounds 1 and 4 (p= .000) and
Rounds 1 and 6 (p= .004). Overall, the number of
foul jumps was significantly different between
rounds (χ
2
= 17.9, p= .003) for female competitions
only, with a small effect size (ϕ= 0.083). For both
men and women, the total percentage of fouls was
higher in the last three rounds (men: 31.49% and
women: 32.45%), compared to the first three
rounds (men: 29.66% and women: 26.85%).
Data on the effects of competition on jump dis-
tance and classification for both male and female
competitions are provided in Table IV. For men,
median (non-normal distribution) jump distance
for Diamond League (7.82 m) was significantly
(p< .001) shorter than World Championships
(7.99 cm) and Olympic Games (8.03 cm). In the
female competitions, median distances (p< .001)
and overall number of foul jumps were significantly
different between competition types (Pearson’s chi-
square = 10.87, p= .004, ϕ= 0.065).
In determining the best predictors of jump distance
in male competitions, the main effects model showed
a significant difference of competition type between
Olympic Games and both Diamond Leagues and
World Championships. Estimated marginal means
revealed a larger statistical effect for Diamond
Leagues with mean jump distance value 16.8 cm
(SE = 0.64) less than that observed in Olympic
Games with World Championships found to be
8.6 cm (SE = 0.70) less. Of the other variables, the
largest effect on jump distance was found to be
Round 1 jump distance (coefficient = 0.374).
Effects of wind (1 m/s increase in tailwind or
reduction in headwind) increased jump distance by
4.2 cm. In the interactions model, ‘in medal position
after last round’with competition type, was signifi-
cantly different between the Olympic Games and
Diamond Leagues (p= .006) only. Estimated mar-
ginal means suggested that a jump into a medal pos-
ition increased the value of the subsequent round
Table IV. Jump distance and classification by competition –men and women.
Competition
Men’s competitions Women’s competitions
Total jumps analysed
Jump distance (m) Jump classification
Total jumps analysed
Jump distance (m) Jump classification
Mean (±SD) Median Legal (%) Foul (%) Mean (±SD) Median Legal (%) Foul (%)
Diamond League 1901 7.78
(±0.35)
7.82 1337
(70.33%)
564
(29.67%)
1833 6.44
(±0.35)
6.48 1331
(72.61%)
502
(27.39%)
World Championships 586 7.83
(±0.37)
7.99 393
(67.07%)
193
(32.93%)
477 6.57
(±0.30)
6.60 324
(67.92%)
153
(32.08%)
Olympic Games 296 7.83
(±0.39)
8.03 207
(69.93%)
89
(30.07%)
297 6.62
(±0.38)
6.67 191
(64.31%)
106
(35.69%)
How performance analysis of elite long jumping can inform representative training design 5

jump distance. Interactions of ‘Distance of Round 1
jump’with competition type were also significantly
different between the Olympic Games and the
World Championships (p< .001).
For the women’s competitions, a statistically signifi-
cant difference was found between jump distance
observed in Diamond Leagues and Olympic Games,
with Diamond Leagues values being 12.8 cm shorter
(SE = 0.035) than Olympic Games, based on the esti-
mated marginal means. Other variables found to be of
significance in the main effects model were ‘Round 1
jump distance’(coefficient = 0.219), ‘Medal position
after previous round’(coefficient = 0.113), and the
effect of wind (5 cm increase in jump distance for
1 m/s increase in tailwind or reduction in headwind).
No variables within the interactions model were
significant.
In determining the best predictors of foul jumps,
no factor or covariate was predictive of a foul jump
in male competitions. Despite this observation, two
factors in the current model appear to increase the
odds of a given jump being a foul, albeit not statisti-
cally significantly. If a Round 1 jump was a foul,
then the odds of the next jump being a foul increased
by a factor of 1.67 –regardless of the round.
Additionally, if the previous jump had been a foul,
the odds of the next jump resulting in a foul, was
1.56 higher than if it had not been a foul. For
female competitions, initial investigation showed
that practically every factor measured was a signifi-
cant predictor of foul jumps, but the final, most par-
simonious model contained three terms: round,
distance of first jump and previous jump being a
foul. The odds of foul jumps (compared to Round
1) are significantly increased in Round 4 (OR =
1.615) and Round 5 (OR = 1.530). For distance of
first-round jump, a unit increase (metre) in distance
increased the odds of the next jump being a foul by
a factor of 1.89. Thus, if an athlete made a first
jump of 6.50 m, the odds of any remaining jump in
the competition being a foul were increased by a
factor of 1.89, compared to a competitor who made
a first jump of 5.50 m. Furthermore, if an athlete
recorded a foul in the previous round, then the
odds of recording a second foul in succession were
increased by a factor of 1.50.
Discussion
In this study, we sought to identify how the analysis of
competition data, framed by concepts from ecologi-
cal dynamics, can provide a more nuanced under-
standing of long jump performance. This
relationship between performance analysis and key
tenets of the theory of ecological dynamics could
assist practitioners in designing more effective train-
ing environments to reflect the intertwined inter-
actions between intentions, perceptions and actions
of athletes in performance. Analysis of competitive
performance data of elite male and female long
jumpers revealed that elite long jumping is defined
by a mean jump distance of 7.81 m for men and
6.48 m for women. Interestingly, mean jump dis-
tance decreased by 1.2 cm per year for both men
and women. In classifying jump outcomes, the per-
centage of jumps deemed fouls was 30.40% and
29.19%, respectively, for men and women. The stag-
nation of long jump performance over time raises
important questions, given advances in technology
and sports sciences (e.g. Balague, Torrents, Hris-
tovski, & Kelso, 2016; Pluijms, Canal-Bruland,
Kats, & Savelsbergh, 2013) and potentially point to
the need to carefully consider training designs to
enhance performance.
Findings revealed how continuous interactions of
individual, task and environmental constraints influ-
enced elite long jumping performance. The personal
constraint of an athlete’s (tactically defined) inten-
tions continuously shape perception–action coup-
lings during competition. It is these intentions,
embedded within specific performances, that frame
the interactions of athletes with task and environ-
mental constraints to facilitate adaptive behaviours
(Araújo et al., 2019). For example, the lowest value
for mean jump distance and lowest percentage of
fouls found in Round 1 suggests athlete intentionality
on the first jump could be to record a ‘safe’jump.
Round 1 jumps were also significantly shorter than
jumps in Rounds 2, 3, 4 and 6 in the men’scompe-
titions. The notions of a ‘safe’jump could be inter-
preted as an athlete’s deliberate adaptation of
perception–action couplings (i.e. decrease in run-up
velocity) to intentionally match his or her specific
needs to the competition demands at specific points
in time (Araújo et al., 2019; Maraj et al., 1998). The
importance of the first round was also highlighted by
its role in predicting jump distance and fouls in
future rounds across the competition. This relation-
ship between jump performances demonstrates that
each jump is connected and forms an event (Gibson,
1979) influencing emergent jump performance
(Renshaw & Gorman, 2015). The outcome of
Round 1 is, therefore, likely to impact the athlete’s
intentions in subsequent rounds, depending on the
needs of the athlete at that particular point in the com-
petition. Intentions, and hence perception–action
couplings, will be strongly influenced by an athlete’s
own goals, competitors’performances and ultimately
the rules of the sport (only the Top 8 athletes at the
end of Round 3, get three further jumps). For
example, after a Round 1 foul, an athlete may place
6C. Mccosker et al.

more emphasis on making a ‘safe’jump (i.e. speed/
accuracy trade-off) in Round 2 in order to increase
the chances of making a legal jump that enables him/
her to receive three additional jumps after Round
3. This conceptualisation of emergent behaviours in
long jump is an important development in better
understanding performance as a series of complex
interconnected events rather than seeing training as a
series of isolated jumps, with important implications
for training design.
The environmental constraint of wind was ident-
ified as a key influence on long jump competitive per-
formance. A 1 m/s increase in tailwind (or decrease in
headwind) increased jump distance for both women
(by 5.0 cm) and men (by 4.2 cm). Previous research
has attempted to determine the aerodynamic effects
of wind on jump performance (de Mestre, 1991;
Ward-Smith, 1985) using mathematical modelling.
However, to date, no research has reported in compe-
tition data. Evidence on the impact of wind as an
environmental constraint on jump performance high-
lights the relevance of training designs which include
experiences in variable wind conditions.
As expected, a major task constraint is rule-based:
that a ‘no-jump’is recorded unless the take-off foot
is behind the foul line. Satisfying this influential con-
straint shapes athletes’behaviours and actions in
seeking to intercept the take-off board with the front
foot. Foul jumps (at any time in a competition) were
seen to increase the odds of subsequent fouls later in
the competition. With almost a third (men: 30.40%
and women: 29.19%) of jumps being classified as
fouls, each athlete’s tactical behaviours are influenced
at any point in competition by these ‘no’jumps. For
example, a foul jump in Round 1 increases pressure
on an athlete to accurately hit the take-off board in
Rounds 2 and 3, whilst also needing to jump for dis-
tance to qualify for the final three jumps. This increase
in psychological and emotional demands, along with
the known implications for run-up velocity and foot
placement error on the take-off board when jumping
for distance, defines how interactions between differ-
ent constraints impact behaviour in elite long jump
performance.
The findings of the current study have important
implications for the design of representative training
environments. Long jump coach education resources
(e.g. Brown, 2013) typically fail to consider how
competition behaviours can be invited through the
design of training environments. Simulating con-
ditions of competitive performance allows prac-
titioners to model environmental and task
constraints to shape intentions, perceptions and
actions influencing performance in elite long
jumping. Our analyses of elite competition revealed
that the most influential interactions were between:
athlete intentionality, effect of wind (direction and
speed) and rules of the sport.
Identification of athlete intentions in the form of
competition strategies highlights the need for training
to focus on adaptations needed to achieve specific
outcome goals, with athletes training in a series of
connected jumps that replicate the demands of com-
petition. This form of ‘within-session periodisation’
can be achieved by the creation of specific ‘vignettes’
for athletes, that seek to simulate the physical,
emotional and psychological demands of competitive
performance environments (Headrick et al., 2015).
An exemplar scenario could focus on the context
when an athlete has fouled in the first two rounds
and must record a jump of sufficient distance in
Round 3 to qualify for a further three jumps. In
this way, the reduction of emphasis on constant rep-
etition in some practice sessions can have a func-
tional value of highlighting focus on a single
performance trial, which simulates competition con-
ditions. In this way, practice task design could
involve ‘repetition without repetition’as advocated
by Bernstein (1967), for example, challenging ath-
letes to calibrate their actions (Van Der Kamp &
Renshaw, 2015) to exploit variable wind speeds and
direction. Asking athletes to complete the run-up
and jump in variable wind speeds and direction
during training will facilitate their attunement to vari-
able weather conditions and adaptation of movement
patterns accordingly. Exploitation of this environ-
mental constraint in training will promote ‘dexterity’
(Bernstein, 1967) in athletes and simulate the level of
uncertainty that exists in competitive performance.
The high percentage of fouls across all competitions
for both men and women, suggests that there may be
a failure to give due emphasis to the importance of
legal jumps in practice conditions (e.g. Brown,
2013). Whilst allowing fouls in training may increase
trial repetition (practice volume) and reduce per-
formance complexity, this approach fails to simulate
the individual-environment relationships that perfor-
mers forge in the competition environment (Davids
& Araújo, 2010; Renshaw, Chow, Davids, &
Hammond, 2010). Coaches need to recognise the
take-off board as a key affordance that athletes
must attune to in order to enable the development
of functional perception–action couplings required
in competition.
Conclusions
In summary, the theoretical framework of ecological
dynamics suggests that a more nuanced understand-
ing of the complexities of long jump performance
could facilitate the design of more representative
How performance analysis of elite long jumping can inform representative training design 7

practice environments by practitioners. We have con-
sidered how more contextual information from com-
petitive environments can enhance practice designs,
following recent conceptualisation of the use of
‘gold standard’data in understanding sports per-
formance constraints (Anderson, 2018). Results
from this study revealed three key constraints that
shape performance behaviours in both male and
female elite long jumping: (i) athlete intentionality,
(ii) wind effects on run-up and jump phases, and
(iii), adhering to rules of the sport. The integrated
manipulation of these key constraints in training
can provide opportunities for athletes to adapt to
major physical and emotional demands of perform-
ance environments. The use of ecological dynamics
to guide the analysis of competition data shows how
performance analysis can be enhanced to enrich the
understanding of athlete interactions during compe-
tition. Recognising the conditioned coupling
evident in dynamic performance environments is a
critical advancement in understanding movement
behaviours in individual sports.
Our findings suggested the need to move beyond
reductionist approaches to studying long jumping,
currently provided by isolated biomechanical analy-
sis of single jumping events (Mendoza, Nixdorf,
Isele, & Gunther, 2009). Future work needs to
embrace the complexity of competitive long
jumping and adopt a more inter-disciplinary
approach to performance analysis in context.
Future research could also further our understand-
ing of influential constraints on long jump perform-
ance through accessing the experiential knowledge
of expert coaches and athletes. Integrating experien-
tial knowledge with theoretical concepts and
research data would enhance understanding of inter-
acting constraints impacting long jump perform-
ance. It would also provide a basis for analysing
how key long jumping performance variables (such
as in the run-up) may be shaped by competitive per-
formance contexts. This integrated approach would
reveal informational constraints that regulate
athlete intentions, and perception–action couplings
during run-ups in sport tasks like long jumping,
cricket bowling and gymnastics vaulting (Green-
wood, Davids, & Renshaw, 2014).
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
The authors received no financial support for the research, author-
ship, and/or publication of this article.
ORCID
Chris Mccosker http://orcid.org/0000-0002-7160-
2987
Ian Renshaw http://orcid.org/0000-0003-3694-
9915
Keith Davids http://orcid.org/0000-0003-1398-
6123
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