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Elite Sprinting: Are Athletes Individually
Step-Frequency or Step-Length Reliant?
AKI I.T. SALO
1
, IAN N. BEZODIS
2
, ALAN M. BATTERHAM
3
, and DAVID G. KERWIN
2
1
Sport and Exercise Science, University of Bath, Bath, UNITED KINGDOM;
2
Cardiff School of Sport, University of
Wales Institute Cardiff, Cardiff, UNITED KINGDOM; and
3
Health and Social Care Institute, Teesside University,
Middlesbrough, UNITED KINGDOM
ABSTRACT
SALO, A. I. T., I. N. BEZODIS, A. M. BATTERHAM, and D. G. KERWIN. Elite Sprinting: Are Athletes Individually Step-Frequency
or Step-Length Reliant? Med. Sci. Sports Exerc., Vol. 43, No. 6, pp. 1055–1062, 2011. Purpose: The aim of this study was to investigate
the step characteristics among the very best 100-m sprinters in the world to understand whether the elite athletes are individually more
reliant on step frequency (SF) or step length (SL). Methods: A total of 52 male elite-level 100-m races were recorded from publicly
available television broadcasts, with 11 analyzed athletes performing in 10 or more races. For each run of each athlete, the average SF
and SL over the whole 100-m distance was analyzed. To determine any SF or SL reliance for an individual athlete, the 90% confidence
interval (CI) for the difference between the SF–time versus SL–time relationships was derived using a criterion nonparametric boot-
strapping technique. Results: Athletes performed these races with various combinations of SF and SL reliance. Athlete A10 yielded the
highest positive CI difference (SL reliance), with a value of 1.05 (CI = 0.50–1.53). The largest negative difference (SF reliance) occurred
for athlete A11 as j0.60, with the CI range of j1.20 to 0.03. Conclusions: Previous studies have generally identified only one of these
variables to be the main reason for faster running velocities. However, this study showed that there is a large variation of performance
patterns among the elite athletes and, overall, SF or SL reliance is a highly individual occurrence. It is proposed that athletes should
take this reliance into account in their training, with SF-reliant athletes needing to keep their neural system ready for fast leg turnover
and SL-reliant athletes requiring more concentration on maintaining strength levels. Key Words: ATHLETICS, BIOMECHANICS,
COACHING, INDIVIDUAL ANALYSIS, SINGLE SUBJECT, SPRINT RUNNING
An athlete’s running velocity is the product of step
frequency (SF) and step length (SL)—a step being
from one foot contact to the next contact of the
contralateral foot. The term stride is also used in the litera-
ture, which is equal to two consecutive steps. Although the
equation of velocity equals SF multiplied by SL is very
straightforward and simple in theory, athletes face problems
in practice because the relationship between SF and SL is
generally an inverse relationship at maximum effort. Thus,
an increase in one parameter could typically lead to a de-
crease in the other. This is due to the negative interaction
apparent in the production of these variables (11). Conse-
quently, this relationship has attracted attention in the bio-
mechanics literature.
Luhtanen and Komi (16) were among the first to com-
prehensively analyze the relationship between SF and SL
and presented the development of SF and SL in track ath-
letes when running velocity was increased from jogging at
3.9 mIs
j1
to sprinting at 9.3 mIs
j1
. However, this study is
not directly relevant to elite sprint athletes, who always need
to run at very high individual velocities in competition. In a
study of 28 sprint-related sportsmen (background, e.g., in
athletics, soccer, touch rugby), Hunter et al. (11) found that
at the group-level SL was significantly related to running
velocity, whereas SF was not. However, at the individual
level, the subjects performed with a significantly higher SF
in their fastest trial in comparison with their third fastest
trial. SL did not reveal significant differences in the indi-
vidual analysis (11). The authors offered a potential expla-
nation for these differences between individual and group
analysis by stating that SF may be the more important factor
in the short term, whereas longer steps may require the de-
velopment of strength and power during a longer period.
Hunter et al. (11) also offered further detailed explanations
of the technique issues that were behind the aforementioned
negative interaction between SL and SF. The sprinting ve-
locities, however, ranged from 7.44 to 8.80 mIs
j1
(11) and
were measured only 16 m into the sprint. Thus, whereas the
article provides general information about step character-
istics and can be helpful to developing athletes, it is not fully
Address for correspondence: Aki I.T. Salo, Ph.D., Sport and Exercise
Science, University of Bath, Bath, BA2 7AY, United Kingdom; E-mail:
A.Salo@bath.ac.uk.
Submitted for publication April 2010.
Accepted for publication October 2010.
0195-9131/11/4306-1055/0
MEDICINE & SCIENCE IN SPORTS & EXERCISE
Ò
Copyright Ó2011 by the American College of Sports Medicine
DOI: 10.1249/MSS.0b013e318201f6f8
1055
APPLIED SCIENCES
Copyright © 2011 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
applicable to elite sprinters, whose running velocities are
much higher.
To fully explore how elite athletes could fine-tune their
performances, it would be necessary to understand how they
perform in competition. Mann and Herman (17) analyzed
the first-, second-, and eighth-place finishers in the 1984
Olympic men’s 200-m final and highlighted the fact that
the major difference between the three athletes (especially
those in first and second) was SF. Interestingly, all three
athletes increased velocity, SF, and SL between the non-
fatigued (125-m mark) and fatigued (180-m mark) phases of
the sprint.
Ae et al. (1) analyzed the final of the men’s 100 m from
the 1991 World Championships in Athletics. One of the key
points highlighted by Ae et al. (1) in their conclusions was
that the gold medalist generally exhibited a shorter SL and
higher SF than the silver medalist, although this was not
consistent throughout the whole race. A similar type of
analysis over each 10 m was performed by Gajer et al. (8)
from the semifinals and final of the men’s 100 m at the 1996
French Championships. The six fastest (10.18 T0.05 s) and
six slowest athletes (10.52 T0.08 s) were divided into sep-
arate groups. SL was consistently higher in the faster group
and was significantly higher in 7 of 10 sections. On the other
hand, SF was higher in the slower group in all but the last
10-m section, although it was significantly higher in only 1
of 10 sections. The authors (8) drew the conclusion from
their results that SL was the more important factor at the
highest level. Recent competition analysis from the World
Championships in Helsinki 2005 (13) provided a similar
trend to that of Gajer et al. (8). Eighteen male sprinters from
the 100-m heats were divided into faster and slower groups
(nine athletes in each group; high-performance group =
10.12–10.32 s, lower performance group = 10.40–10.90 s).
In the full-stride phase (around 60 m), the longest SL was
significantly longer (PG0.003) by 0.12 T0.03 m for the
faster group than the slower group, whereas there were no
significant differences in SF.
Gajer et al. (8) also reanalyzed the data of Ae et al. (1) by
splitting the eight finalists into two groups: the first- to
fourth-place and fifth- to eighth-place finishers. The four
fastest athletes had a higher average SL in 9 of 10 intervals,
whereas the four slowest athletes had a higher average SF
in 7 of the intervals. This was presented by Gajer et al. (8) as
further evidence to support their own conclusions. Thus, at
the group level the finding was opposite to the conclusion of
Ae et al. (1) regarding the first- and second-place finishers.
It seems, however, that the results are very dependent on
the grouping. The grouping used by Gajer et al. (8) for the
data from Ae et al. (1) meant that the groups were equal in
number, each containing four athletes. When the finishing
times for the eight athletes were examined, a different
method of grouping could be justified. The first six finishers
all completed the race in times in a close range of 9.86–
9.96 s. The last two finishers were considerably slower,
finishing with times of 10.12 and 10.14 s. New calculations
reveal the opposite trend to that presented by Gajer et al. (8).
With the modified groupings based on the absolute level of
performance, the six fastest athletes recorded a higher SF in
9 of the 10 intervals, whereas the slowest two athletes had a
higher SL in 7 of the intervals. This change occurred be-
cause the fifth- and sixth-place athletes typically displayed
short SL and high SF values when compared with the other
six athletes. This example shows that an average group-
based analysis can actually mask important issues at the
individual level. Because of this problem, Dixon and Kerwin
(6) called for a multiple single-subject approach in studies
where important individual differences that are not visible
in general trends of a group analysis may be present. This
might be even more important for individual elite athletes
because any improvement in their performance may give
them an advantage over the competitors. Thus, when elite
sprinters try to improve their performance by seeking to cut
hundredths of a second from their race time, it is very im-
portant to understand the individual performance and step
characteristics issues rather than analyze them at the group
level. Recently, in track-and-field biomechanics, there have
been single-subject analyses published in sprinting (3) and
sprint hurdling (22).
It is clear from the results presented on elite athletes in a
competition situation that there is no consensus of opinion
over which factor, SF or SL, is the more important at this
level of competition. These are important findings, none-
theless, because they give a good insight into the perfor-
mance of the very best athletes in a competitive situation,
something that a laboratory- or training-based study is not
capable of doing. There is, however, a lack of consideration
for the possibility that individual athletes may adopt dif-
fering strategies from one another, about optimizing SF
and SL. Further insight could be realized if the same elite
athletes were analyzed over several runs. Such analysis is
clearly missing from the current biomechanics literature.
Thus, the aim of this study was to investigate the step
characteristics among the very best 100-m sprinters in the
world to understand whether the elite athletes are individu-
ally more reliant on SF or SL.
METHODS
A total of 52 male elite-level 100-m races were recorded
from publicly available television broadcasts. The com-
petitions included several Olympic, World, and European
Championships; International Association of Athletics Fed-
erations (IAAF) Grand Prix series competitions; European
Cups; and some National Championships. The summary of
competitions is in Table 1. Data were collected from semi-
finals and finals of the major championships and heats and
finals of individual Grand Prix series competitions. Offi-
cial race times were recorded from the IAAF Web site (12).
A similar approach of analyzing publicly available data
from sport competitions for research purposes has been
carried out by Stewart and Hopkins (23), who analyzed the
http://www.acsm-msse.org1056 Official Journal of the American College of Sports Medicine
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consistency of performance between swimming strokes, race
distances, and two competitions across 221 swimmers. In
the current study, athletes’ individual races were analyzed
if the athlete ran fully through the finish line. Thus, indi-
vidual races in which an athlete clearly eased off before the
finish line (e.g., in some heats or semifinals), sustained an
injury, or in any way was deemed not to perform normally,
were disregarded from the analysis. Consequently, the
worst individual race time analyzed was 10.39 s. It is clear
that not every analyzed athlete was involved in each com-
petition. All athletes who performed in 10 or more races
were taken for this analysis yielding a total of 11 athletes.
The number of races per athlete is listed in Table 2. Of
these 11 athletes, 9 ran under 10.00 s at least once in these
competitions.
For each run of each athlete, the average SL and SF over
the whole 100-m distance were analyzed as follows. The
total number of steps taken in the race by each of the athletes
of interest was counted by viewing the race in slow motion
on a normal television and using a video player (AG-7550;
Panasonic, Osaka, Japan), which yielded 50 video fields per
second. Because the athlete did not necessarily complete a
step exactly at 100 m, the displacement of the last step (S
LS
)
was defined. This was the overall displacement from the
start line to the toe of the ground foot in the step closest to
the finish line (either side). The displacement estimation was
based on using the track markings, the length of the foot
(approximately 0.3 m), and the expected values for SL.
Because the first step out from the starting blocks does not
cover as much ground as all subsequent steps and it clearly
takes the longest time, this step was disregarded from
the calculations. To do that, a pilot test was set up. Four
national-level athletes (who provided informed consent) were
videotaped with a high-speed video camera (Motionscope
500C; Redlake Imaging Corp., Alameda, CA) at 250 Hz to
estimate the length of the first step both as a distance and time
(from the start signal to the instance of the first contact). On
the basis of these four athletes’ performances over 16 runs
(4 each), a distance of 0.55 m and a time of 0.52 s were sub-
tracted from the calculations. Average SL throughout the race
was therefore calculated as follows:
SL
;
¼ðSLS 0:55 mÞ=ðnS1Þ;
where SL
;
is average step length, S
LS
is displacement of
the last step, and n
S
is number of full steps. The total number
of steps that were taken over the exact 100 m (n
S100
)was
then calculated as follows (this provides the last step as a
fraction):
nS100 ¼nSþ½ð100 m SLS Þ=SL
;
From this, the average SF for the race was calculated as
SF
;
¼ðnS100 1Þ=ðtr0:52 sÞ
where SF
;
is the average step frequency and t
r
is the official
race time.
Statistical analysis. All athletes were analyzed in-
dividually. SF, SL, and race time data were natural log-
transformed before analysis to normalize distributions and
stabilize variance. To determine any SF or SL reliance for
an individual athlete, the 90% confidence interval (CI)
for the difference between the SF–time versus SL–time
relationships was derived using a criterion nonparametric
bootstrapping technique (7) (Resampling Stats 4.0.7; Re-
sampling Stats, Inc., Arlington, VA). Briefly, for each set
of nraces for each individual athlete, 10,000 resamples
with replacement (of ncases) of the race time, SF and SL
variables were taken (maintaining case correspondence).
On each bootstrap resample, the SF–time and SL–time
correlations (Pearson r) were derived, and the difference
between these correlations was calculated and stored (SF
minus SL). The 90% CI (10) for the difference between the
SF and SL correlations was obtained using a simple per-
centile method, from the 5th and 95th percentiles of the
distribution of 10,000 differences. The threshold for a
practically important difference between SF and SL cor-
relations (in either direction) was set at a value of 0.1—a
‘‘small’’ effect size for the correlation coefficient (4). An
athlete was declared SF reliant if the lower limit of the 90%
CI was at or beyond the threshold of j0.1, with the upper
limit G+0.1 (precluding SL reliance). Conversely, an athlete
was declared SL reliant if the frequencyjlength correla-
tion difference was positive (favoring length), with the 90%
CI precluding frequency reliance (ej0.1). An effect was
deemed ‘‘unclear’’ if the 90% CI simultaneously extended
into regions, suggesting both SF and SL reliance; the athlete
could be SF reliant or SL reliant, or there could be a trivial
difference favoring neither step characteristic. In addition,
to investigate whether the elite athletes were more reliant on
TABLE 2. Number of races with the mean official race time (SD) for each individual
athlete in those races.
Athlete No. of Races Mean Time (s) SD (s)
A1 21 10.02 0.12
A2 27 10.08 0.09
A3 23 10.05 0.12
A4 20 10.12 0.12
A5 15 10.17 0.10
A6 15 10.12 0.13
A7 17 10.08 0.08
A8 16 10.16 0.08
A9 10 10.12 0.06
A10 14 10.17 0.12
A11 11 10.18 0.14
Mean 17 10.12
TABLE 1. Summary of analyzed competitions.
Final Semifinal
a
Olympic Games 1 2
World and European Championships and Commonwealth
Games
57
IAAF Golden League 10 2
IAAF Grand Prix 11 7
European Cup 2
National Championships 2 3
a
Semifinal column contains Golden League and Grand Prix heats because these
competitions do not have separate semifinals.
ELITE SPRINTING: STEP CHARACTERISTIC RELIANCE Medicine & Science in Sports & Exercise
d
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SF or SL or whether height influenced this reliance, three
further Pearson correlations were carried out: the point dif-
ference between the SF–time versus SL–time correlation
values from above was further correlated with the individual
mean race times as well as with the athletes’ personal best
times and heights, both of which were obtained from the
athletes’ biographical information on the IAAF Web pages
(12). These data were not natural log-transformed because
the point difference yielded negative values that cannot be
log-transformed. The 90% CI values were calculated, and
the threshold for a practically important difference was set
at 0.1 as above.
RESULTS
Table 3 provides the correlation coefficients for each
athlete between the independent variables and the race time.
The correlation values between SF and race time varied
between 0.16 and j0.79. Contrary to SF, all athletes yielded
a negative correlation between SL and race time. The range
of correlation values for SL varied from j0.16 to j0.89.
Figure 1 provides the difference between correlations
for SF–time and SL–time, together with its 90% CI. Athlete
A10 yielded the highest positive difference with a value of
1.05 (with the CI ranging from 0.50 to 1.53). The largest
negative difference occurred for athlete A11 as –0.60 with
the CI ranging from –1.20 to 0.03. The area of T0.1 to in-
dicate the smallest practically worthwhile difference be-
tween correlations is also shown in Figure 1.
Owing to the large variation shown in r-difference values,
three athletes’ data are specifically shown in Figure 2 to
illustrate the athletes’ times as a function of SL and SF. On
the basis of data in Figure 1, athlete A10 (Fig. 2, A and B)
had the largest SL reliance, athlete A4 (Fig. 2, C and D) did
not yield any reliance either on SF or on SL, and athlete A11
(Fig. 2, E and F) was the only athlete who was clearly SF
reliant. The minimum, maximum, and mean of average SL
and SF values for each athlete are presented in Table 4,
showing that the lowest range for the average SL was 0.06
m, whereas the largest range was 0.14 m. The respective
values for the average SF range were 0.07 and 0.30 Hz.
The SF–SL reliance (as correlation difference) did not
show a meaningful relationship with the athletes’ mean race
time (CI for r,j0.27 to 0.71), personal best times (j0.63 to
0.40), or height (j0.13 to 0.77).
DISCUSSION
This study was designed to increase our understanding of
the SF and SL characteristics of elite athletes in major com-
petitions. The main results showed that these characteristics
vary considerably between the athletes. Previous studies
have generally identified only one of these variables to be
the main reason for faster running velocities, and the results
have given a somewhat confusing picture. Kuitunen et al.
(15) showed that SF was the dominant factor when running
velocity increased from 70% to 100%. Higher SF also
seemed to be the major difference between three Olympic
200-m finalists (17). On the other hand, Gajer et al. (8)
found that better 100-m sprinters in their study had longer
SL than slower athletes, and Hunter et al. (11) showed that
the SL was significantly related to running velocity at the
group level (whereas SF was not). The results of Hunter
et al. (11), however, showed that, within individuals, SF was
higher in the fastest trials. None of these studies, however,
have looked at elite athletes across different races to deter-
mine how an individual athlete performs. From an elite
athlete’s point of view, the group-level data do not provide
appropriate information to improve individual performance.
For example, by executing an average performance of
100-m Olympic finalists, the athlete would not win the race.
In fact, often, if an athlete were to achieve the average per-
formance of all the finalists, that would not be sufficient to
even place that athlete on the podium. Thus, it is important
to look at each elite athlete individually. To the best of our
knowledge, this is the first study that has looked at step
TABLE 3. Correlation values for natural log-transformed SF and SL versus time.
Athlete SF vs Time Correlation SL vs Time Correlation
A1 j0.27 j0.38
A2 j0.57 j0.16
A3 j0.54 j0.31
A4 j0.39 j0.36
A5 0.11 j0.69
A6 j0.61 j0.65
A7 j0.12 j0.49
A8 j0.37 j0.58
A9 0.07 j0.80
A10 0.16 j0.89
A11 j0.79 j0.19
FIGURE 1—The rdifference (diamonds) with 90% CI (bars) for each
athlete A1 to A11. The area of T0.1 from zero in the middle demon-
strates the trivial (nonreliant) effect.
http://www.acsm-msse.org1058 Official Journal of the American College of Sports Medicine
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characteristics of elite athletes individually and longitudi-
nally across multiple competitive races. In addition, a novel
aspect of the current study is the use of a criterion boot-
strapping method, together with a criterion for practical
significance, to elucidate the within-athlete differences be-
tween SF and SL.
Average SF multiplied by average SL provides the aver-
age running velocity, which, in turn, has an inverse rela-
tionship with the race time. This means that both SF and SL
are inversely linked with the race time and strongly related
to each other. This collinearity between SF and SL makes it
impossible to properly separate the independent influence of
these predictors on race time, if both are entered together as
predictors in a multiple regression model. Therefore, a novel
approach was sought to understand any reliance on partic-
ular step characteristics by athletes. Consequently, it was
decided that the most appropriate approach was to adopt
a bootstrapping technique to calculate the 90% CI for the
difference between SF–time versus SL–time correlations to
inform how practically meaningful this effect was. The in-
terpretation of the results in Figure 1 follows the recom-
mendations by Batterham and Hopkins (2). The effect is
FIGURE 2—Three athletes’ race times as a function of SF and SL: athlete A10 (A and B) showed SL reliance, athlete A4 (C and D) did not yield either
reliance, and athlete A11 (E and F) was SF-reliant. Please note that yaxes have been inverted because quicker times demonstrate improved perfor-
mance. Points on the figures with trend lines are from the original data; rvalues are from the log-transformed data (Table 3). Because of inverted
yaxes, the signs of rvalues do not match the visual impression.
ELITE SPRINTING: STEP CHARACTERISTIC RELIANCE Medicine & Science in Sports & Exercise
d
1059
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considered reliant if the 90% CI is fully on either the SF or
SL side or if one end reaches only to the area of a trivial
effect in the middle. If the CI extends to include both fre-
quency and length reliance, then the effect is considered
unclear.
Overall, the results in Figure 1 revealed that there is a
large variation of performance patterns among the elite ath-
letes. There were clearly athletes at the highest elite level
of 100-m sprinting who were SL reliant (athletes A10, A9,
and A5), whereas only athlete A11 was clearly SF reliant.
All other athletes did not have clear reliance on either side,
although there were trends implying that, for example, ath-
lete A7 was most likely to be SL reliant and athlete A2 was
most likely to be SF reliant. When looking at the results in
further detail, athlete A10 yielded a 90% CI (0.50–1.53),
which did not even cross over the T0.1 trivial effect region
(Fig. 1). Thus, athlete A10 performed best when he was able
to produce long steps (within his own range; Fig. 2B). Such
reliance of SL meant that if the athlete was not able to pro-
duce long steps, he could not compensate the performance
enough with high SF to produce fast 100-m times. On the
contrary, athlete A11 performed his best times when he was
capable of producing high step frequencies (within his own
range; Fig. 2E). The 90% CI (j1.20 to 0.03) crosses over
into the trivial effect region from the SF reliance but does
not reach to a SL-reliant effect (Fig. 1). This meant that, if
the athlete could not produce high step frequencies (e.g., if
the nervous system was not ready to fire quickly enough to
have a fast turnover of the steps), the SL had not compen-
sated the running velocity enough. The athletes whose 90%
CI reached over all three different zones in Figure 1 were
such that they produced the best times sometimes with
slightly higher SL (and lower SF) and sometimes with
slightly higher SF (and lower SL; see an example of athlete
A4 in Fig. 2, C and D).
When looking at the individual SL and SF values within
athletes and across the races, the three examples in Figure 2
provided a very similar range of values. SL range was 0.08
m for A11, 0.11 m for A10, and 0.13 m for A4. The re-
spective SF ranges were 0.22, 0.11, and 0.30 Hz. Athlete A4
had the largest range in both SL and SF from all athletes
(Table 4). As the range of values on SF and SL were quite
similar for all athletes, it reinforced that SF or SL reliance
occurred within the normal range of that variable in indi-
vidual athletes, and it was not due to some clear outliers in
occasional runs.
The average within-athlete SF in this study ranged from
4.43 to 5.19 Hz, whereas the average SL ranged from 2.01 to
2.34 m (Table 4). It is clear that the average SL over the full
100 m in this study were less than those found in the max-
imum velocity phase in the literature because data in the
current study also contain steps at the start of the run, which
are shorter than later in the run. Ae et al. (1) reported SL
from 2.29 up to 2.71 m for the World Championships’ fi-
nalists in the maximum velocity phase. SL values reported
by Gajer et al. (8) fell within the range provided by Ae et al.
(1). SF values in the current study match more closely to
those at maximum velocity because SF does not alter largely
during the race. This is due to the fact that when early con-
tact phases are generally longer, the flight phases are shorter.
This ratio gradually changes; however, the total step time
(and thus frequency) does not drastically change, as visible
in the data of the first four steps out of the blocks in a study
by Salo et al. (21). Step frequencies in the maximum ve-
locity phase provided by Ae et al. (1) and Gajer et al. (8)
generally matched the range seen in the current study.
At the group level, SF–SL reliance did not yield mean-
ingful relationships with athletes’ mean race times (CI for
r,j0.27 to 0.71) or the personal best times (j0.63 to 0.40).
This means that, for example, SL-reliant athletes were not
any faster than SF-reliant athletes. Thus, it is possible to
reach the absolute top level of sprinting in the world (run
under 10.00 s) with widely varying patterns of SF and SL
reliance. The results also showed an unclear (trivial) effect
(i.e., there was no relationship) between the height of the
athletes and SF–SL reliance (CI for r,j0.13 to 0.77). This
means that taller athletes within this group were not SL re-
liant (against the general perception) or that shorter athletes
were not SF reliant and vice versa. Overall, these three re-
sults support the idea that either SF or SL reliance is a highly
individual occurrence.
The wind has been shown to influence the finishing time
in sprinting. For example, the theoretical calculation by
Ward-Smith (24) showed that a 2-mIs
j1
following wind
improves a 100-m result at the elite level (10.00-s runner) by
0.10 s, whereas the same head wind would slow the runner
down by 0.13 s. However, the situation in the current study
was different from that of an individual race because data
were collected during a long period and across numerous
races. It is clear that athletes train and target some major
competitions, and thus, they potentially run faster in these
races regardless of the wind speed in comparison with races
perhaps earlier in a season. There were no clear trends that
the faster times were set with better wind conditions. In
addition, regardless of the wind, the race time was per-
formed with that specific SF and SL combination found in
the analysis, and it is this specific SF–SL pattern (reliance)
that is the interest in the current study.
TABLE 4. Minimum (Min), maximum (Max), and mean of average SL and SF values for
each individual athlete.
SL (m) SF (Hz)
Athlete Min Max Mean Min Max Mean
A1 2.14 2.27 2.20 4.62 4.91 4.75
A2 2.18 2.28 2.23 4.50 4.77 4.66
A3 2.01 2.11 2.07 4.91 5.19 5.05
A4 2.07 2.21 2.14 4.64 4.94 4.83
A5 2.12 2.23 2.17 4.66 4.82 4.74
A6 2.20 2.28 2.24 4.54 4.73 4.63
A7 2.13 2.24 2.18 4.67 4.86 4.76
A8 2.28 2.34 2.30 4.43 4.54 4.47
A9 2.28 2.34 2.31 4.44 4.51 4.48
A10 2.13 2.24 2.19 4.65 4.76 4.71
A11 2.14 2.22 2.18 4.62 4.84 4.73
Mean 2.15 2.25 2.20 4.61 4.81 4.71
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Because the SF or SL reliance varied considerably between
the athletes, it is proposed here that this should be taken
into account in their training, especially in the preparations
for the most important competitions. The effect of different
types of training on athletes’ performance is difficult to prove
owing to two factors: first, there is an inherent problem in
getting elite athletes to participate in training studies (14),
and second, it is practically impossible to isolate the training
influence of one specific type of exercise or mode of exer-
cise. However, some indirect conclusions can be drawn from
the literature and theory of specificity in training.
On the basis of animal research, Heglund and Taylor (9)
concluded that the increased stride length in various animals
primarily required higher average muscle force production
pointing toward the association between muscle strength and
stride (step) length. Studying humans’ sprinting perfor-
mance, Weyand et al. (25) concluded that the faster running
speeds were achieved by greater vertical ground reaction
forces rather than more rapid leg movements. The higher
average force production during the contact (i.e., strength)
resulted in considerably higher stride lengths. The regression
analysis showed that a 1.8-fold increase in top running speed
was achieved with 1.69 times longer strides (and with an
average vertical force production that was 0.5 times body
weight larger). It is acknowledged that, in the same study,
higher stride frequency was also associated with increased
force production. This was because a higher vertical force
production allowed athletes to produce the required impulse
in a shorter contact time. However, the regression analysis
showed only a 1.16-times increase in stride frequency across
the same range of top speeds as above. On the other hand,
Mero and Komi (18) found that only well-trained athletes
(as opposed to less-trained athletes) were able to increase
SF when towed to supramaximal velocities. Ross et al. (20)
concluded that this ability to increase SF may have been
caused by neural adaptations of training. Furthermore,
Heglund and Taylor (9) stated that higher stride frequencies
in animals require faster production of cross-bridges owing
to faster force generation demands pointing toward the as-
sociation between SF and neural conditioning.
Hunter et al. (11) hypothesized, based on their results and
the literature, that developing longer SL requires long-term
development of strength and power, especially to increase
horizontal ground reaction impulse. Cronin et al. (5) studied
how two types of resistive training (sled towing and
weighted vest) acutely influenced step characteristics over
the first 30 m in comparison with unresisted sprinting. Be-
cause relative strength due to additional weights was re-
duced, the decrease in performance was mainly due to lower
SL with only small decreases in SF. Moir et al. (19) had a
slightly different approach when the authors studied the in-
fluence of 8 wk of resistance training on step characteristics.
Although these were analyzed only over the first three steps
after the start, and thus may not be fully applicable to the
current article, the results gave indications of how such
training affects these step variables. Increased maximum and
explosive strength was associated with increased SL and
reduced SF over these first three steps (19).
Thus, overall, it is reasonable to conclude that SL is re-
lated more to increased force production, and SF is associ-
ated with faster force production during the contact and
quick leg turnover requiring neural adaptations. Higher SF
requires cross bridges within the muscles to be built at high
rates, and thus, these need a high rate of neural activation.
Consequently, it is proposed that the SF-reliant athletes are
required to concentrate on neural activation in their final
preparations for the major races and have a nervous system
ready such that they can produce the quick turnover of the
legs. On the other hand, the SL-reliant athletes need to keep
their strength levels up throughout the season and have the
required flexibility in the hip area to produce long steps.
Naturally, athletes cannot totally forget the nonreliant vari-
able because any disproportionate reductions in one variable
cannot be generally compensated for by the other variable.
The athletes who cross over the T0.1 trivial effect region
should perhaps focus equally on SL and SF in their training.
It is also good to remember that the current study was based
on the average step variables over the whole 100 m, whereas
SL, especially, varies throughout the race. It is estimated that
the accuracy of our measurements is about 0.01 m for the SL
and 0.06 Hz for the SF. The final caution is that this article
was able to provide only results based on how people have
performed not how an ideal performance could be created.
Thus, to further understand how SF and SL influence each
other and interact to produce the velocity of the individual
athlete, these same variables should be analyzed individually
at the maximum velocity phase and longitudinally in train-
ing. Some questions about the SF, SL, and velocity rela-
tionships could probably be best answered by adopting a
modeling approach.
CONCLUSIONS
This study analyzed SL and SF of world elite male 100-m
sprinters over multiple competitions. Because group-level
analysis could mask personal differences, this study con-
centrated on analyzing each athlete separately. Individually,
some athletes’ performances were more reliant on SL, one
athlete was clearly SF reliant and some athletes used com-
binations that showed a reliance on neither. It is proposed
that athletes should take this reliance into account in their
training, with SF-reliant athletes needing to keep their neural
system ready for fast leg turnover and SL-reliant athletes
requiring more concentration on maintaining strength levels.
This study has been partly funded by UK Athletics Ltd. and the
Leverhulme Trust, United Kingdom.
The authors thank the BBC Sports Library and its staff for access
to broadcast archives, and the Research Institute for Olympic
Sports, Finland, and their staff are thanked for their assistance in
the pilot test for the ‘‘first-step’’ evaluations.
The authors declare that they have no conflict of interest and that
the results of the present study do not constitute endorsement by
the American College of Sports Medicine.
ELITE SPRINTING: STEP CHARACTERISTIC RELIANCE Medicine & Science in Sports & Exercise
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1061
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