RESEARCH ARTICLE
Sprint mechanical variables in elite athletes:
Are force-velocity profiles sport specific or
individual?
Thomas A. Haugen1*, Felix Breitscha¨del1,2, Stephen Seiler3
1 Norwegian Olympic Federation, Oslo, Norway, 2 Norwegian University of Science and Technology,
Trondheim, Norway, 3 Faculty of Health and Sport Sciences, University of Agder, Kristiansand, Norway
* thomas.haugen@olympiatoppen.no
Abstract
Purpose
The main aim of this investigation was to quantify differences in sprint mechanical variables
across sports and within each sport. Secondary aims were to quantify sex differences and
relationships among the variables.
Methods
In this cross-sectional study of elite athletes, 235 women (23 ± 5 y and 65 ± 7 kg) and 431
men (23 ± 4 y and 80 ± 12 kg) from 23 different sports (including 128 medalists from World
Championships and/or Olympic Games) were tested in a 40-m sprint at the Norwegian
Olympic Training Center between 1995 and 2018. These were pre-existing data from quar-
terly or semi-annual testing that the athletes performed for training purposes. Anthropomet-
ric and speed-time sprint data were used to calculate the theoretical maximal velocity,
horizontal force, horizontal power, slope of the force-velocity relationship, maximal ratio of
force, and index of force application technique.
Results
Substantial differences in mechanical profiles were observed across sports. Athletes in
sports in which sprinting ability is an important predictor of success (e.g., athletics sprinting,
jumping and bobsleigh) produced the highest values for most variables, whereas athletes in
sports in which sprinting ability is not as important tended to produce substantially lower val-
ues. The sex differences ranged from small to large, depending on variable of interest.
Although most of the variables were strongly associated with 10- and 40-m sprint time, con-
siderable individual differences in sprint mechanical variables were observed among equally
performing athletes.
Conclusions
Our data from a large sample of elite athletes tested under identical conditions provides a
holistic picture of the force-velocity-power profile continuum in athletes. The data indicate
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
1 / 14
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Haugen TA, Breitscha¨del F, Seiler S
(2019) Sprint mechanical variables in elite athletes:
Are force-velocity profiles sport specific or
individual? PLoS ONE 14(7): e0215551. https://doi.
org/10.1371/journal.pone.0215551
Editor: Leonardo A. Peyre´-Tartaruga, Universidade
Federal do Rio Grande do Sul, BRAZIL
Received: January 3, 2019
Accepted: April 3, 2019
Published: July 24, 2019
Copyright: © 2019 Haugen et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data underlying
this study are deposited at the UiA Open Research
Data repository with the following persistent ID:
https://dataverse.no/dataset.xhtml?persistentId=
doi:10.18710/PJONBM. Researchers interested in
verification/replication of the present study can use
this data source and are also kindly requested to
contact the senior author, Stephen Seiler at
Stephen.Seiler@uia.no, or Monica Klungland
Torstveit at monica.k.torstveit@uia.no. The data
underlying this study are owned by the Norwegian
Olympic Federation (NOF). Because there are some
unique athletes in the dataset, and their results may
that sprint mechanical variables are more individual than sport specific. The values pre-
sented in this study could be used by coaches to develop interventions that optimize the
training stimulus to the individual athlete.
Introduction
Running a short distance as fast as possible is a core capacity in many sports. For a sprinter
competing in athletics, 100 m and 200 m, this capability alone defines them as performers. In
bobsleigh, athletes are required to sprint while moving an external mass. Sprinting capacity is
also crucial in most team sports, as the ability to either create or close small gaps can be deci-
sive in goal scoring situations. Even in typical endurance sports, explosive acceleration ability
(in the context of their slow-twitch dominant peers) can be a medal-winning advantage at the
finish of a close race. Accordingly, numerous sprint training studies across a wide range of
sports have been performed over the years. Sprinting under assisted, resisted and normal con-
ditions, maximal and explosive strength training, plyometric training and high-intensity run-
ning have all been investigated in different combinations [1–3]. Although the principle of
specificity is clearly present, no training methods have so far emerged as superior. Individual
predispositions must therefore be considered when prescribing training programs.
Considerable effort has been made over the years to quantify the underlying variables for
sprint performance. Seminal works by Fenn & Marsh [4] described the force-velocity (Fv) rela-
tionship in isolated frog and cat muscles, a relationship that later was confirmed in humans by
Wilkie [5]. Advances in technology have allowed scientists to explore fundamental aspects of
sprinting skills more closely, and presently, the physiology and mechanics of sprint running
are typically interrogated through macroscopic mechanical variables [6–8]. Samozino et al. [9]
have recently developed a simple and practical method for profiling the mechanical capabilities
of the neuromuscular system using an inverse dynamic approach applied to the centre-of-
mass movement, calculating the step-averaged ground reaction forces in runners’ sagittal
plane of motion during accelerated sprinting from only anthropometric and spatiotemporal
data. Theoretical maximal velocity (v0), horizontal force (F0), horizontal power (Pmax) and
force-velocity profile (i.e., the slope of the force-velocity relationship; SFV) can be calculated
from the speed-time curve. Other indices, such as ratio of force (RF) and index of force appli-
cation technique (DRF) can also be computed from the same method. RF is a ratio of the step-
averaged horizontal component of the ground reaction force to the corresponding resultant
force, while DRF expresses the athlete’s ability to maintain a net horizontal force production
despite increasing velocity throughout accelerated sprinting [6]. These variables are determi-
nant factors for sprint performance, in line with the laws of motion, and provide insights into
individual biomechanical limitations [6–8, 10].
A promising application of force-velocity profiling is in the design of individualized sprint
training programs. An effective sprint training program should target the main factors that
limit the athlete’s performance [11–12]. To help tailor the training program to the individual,
the coach could compare test values from the individual to test values that are typical for the
sport. An athlete with a velocity value that is low for the sport could then be prescribed more
maximal velocity sprinting, whereas an athlete with a horizontal force value that is low for the
sport could be prescribed more horizontal strength training [11]. Currently, sprint force-veloc-
ity profile data are only available from athletes in a few selected sports; previous studies only
analyzed specialist sprinters or athletes from selected team sports [8, 10, 13–19]. It is unclear
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
2 / 14
identify them, NOF has determined that the
underlying data cannot be shared. Data are
therefore available only on request to Stephen.
seiler@uia.no for researchers who meet the criteria
for access to confidential data. The authors confirm
that data would be made available to researchers
interested in replication/verification of the present
study.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
whether previously measured sprint force-velocity profiles are specific to the sport or specific
to the athlete. To individualize a training program for an athlete in a given sport, the coach
requires a holistic picture of the force-velocity profile continuum in athletes. Therefore, the
aim of the present study was to quantify differences in sprint mechanical variables across
many sports and within each sport. Secondary aims were to quantify sex differences and rela-
tionships among the variables.
Materials and methods
Participants
The Norwegian Olympic training centre is a standardized testing facility used by a large num-
ber of elite athletes from many sports. A database of results from 40-m sprint tests was col-
lected from 1995 to 2018, and this database provides a foundation for exploring sprint
performance and mechanical properties in athletes. In this cross-sectional study we analyzed
sprint tests by 666 athletes from 23 sports. All participants were Norwegian national team ath-
letes, i.e., represented Norway in international senior competitions, and 128 of the athletes
were medalists from the World Championships and/or Olympic Games.
Ethics statement
This study was based on pre-existing data from quarterly or semi-annual testing that the ath-
letes performed for training purposes, and thus no informed consent was obtained [20]. All
data were anonymized to comply with the General Data Protection Regulations of the Euro-
pean Union. The study was reviewed by the Norwegian Data Protection Authority and
approved by the ethics committee at the Faculty of Health and Sport Sciences, University of
Agder (reference number 19/00068).
Procedures and data handling
All included players were tested in the time period 1995–2018. Tests were performed on a ded-
icated indoor 40-m track with 8 mm Mondotrack FTS surface (Mondo, Conshohocken, USA)
at the Norwegian Olympic training center in Oslo. A standard warm-up program was com-
pleted prior to sprint testing, beginning with a 10–15 min easy jog, followed by 5–6 minutes of
sprint specific drill exercises, 2–4 strides with increasing speed and 1–2 trial starts. During test-
ing, athletes assumed the starting position and started running on their own initiative after
being cleared to start by the test leader. New trials were performed every 3–5 min until a per-
formance plateau was observed. In practice, 80% of all players achieved their best performance
within two trials. Body mass was assessed immediately prior to or after the sprint test on a sta-
tionary force platform (AMTI, model OR6-5-1). Data from a single athlete was only included
in one category for each analysis. That category was the player’s affiliation on the day of his/
her best sprint test result. A purpose-built excel spreadsheet developed by Morin & Samozino
[21] formed basis for calculations of F0, v0, Pmax, SFV, RFmax and DRF. These calculations were
based on best individual sprint test, associated split times and body mass. Temperature and
atmospheric pressure were set to 760 mm Hg and 20 ˚C.
Two different timing system setups were used. In the time epoch 1995–2011, a 60x60 cm
start pad was placed under the track at the start line. The clock was initiated when the front
foot stepped off the pad. Split times were recorded with split-beamed timing gates for each
10th m of the sprints. The transmitters where placed 140 cm above ground level, while the
reflectors were placed 130 and 150 cm above the floor. Both beams had to be interrupted to
trigger each timing gate. The timing setup has been assessed for accuracy and reliability [22].
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
3 / 14
In January 2011, the timing system was upgraded. The split-beamed timing gates were
replaced by dual-beamed timing gates, while the start pad was replaced with a single-beamed
timing gate located 60 cm in front of the start line and 50 cm above ground level. Rigorous
pilot testing was performed before deciding the exact location of the timing gate at start to pro-
vide comparable times with the previous setup. Simultaneous comparisons (n = 50) of the old
and new timing setup revealed no differences in 40-m sprint times (mean ± SD: 0.00 ± 0.02 s).
The dual-beamed timing system has also been assessed for accuracy and reliability [23]. Over-
all, typical error (coefficient of variation; CV) was 0.6–2.4% for sprint times, ~1.5% for v0 and
RFmax, and 3.5–5.1% for F0, Pmax, SFV and DRF for both timing setups.
To ensure valid sprint mechanical values when using split times as input in the method pro-
posed by Samozino et al. [9], it is crucial that i) the entire acceleration phase is captured, and
ii) time initiation (the time 0) is very close to the first rise of the force production onto the
ground [24]. For the current procedures, the body’s center-of-mass was ~ 0.5 m in front of the
start line, and possessed a considerable forward momentum, at time triggering. Hence, based
on available correction factors [22, 25], 0.5 s was added to all sprint times for converting to
“first movement” triggering. All sprint times presented are comparable to starts from blocks
and audio signal with reaction time subtracted from the total time.
Statistical analysis
Data are reported as mean ± SD. Pearson’s R (±90%CL) was used to examine the relationship
across variables (after log transformation of physiological data). Correlation values were inter-
preted categorically according to the scale outlined by Hopkins et al. [26], meaning that 0.10,
0.30, 0.50, 0.70, 0.90 and 1.0 were thresholds for small, moderate, large, very large, extremely
large and perfect, respectively. Magnitudes of differences across category means were assessed
by standardization (mean difference divided by the harmonic mean of the SD of the compared
groups). The thresholds for assessing the observed difference in means were 0.2, 0.6, 1.2, 2.0
and 4.0 for small, moderate, large, very large and extremely large, respectively [26]. To make
inferences about true values of effects, non-clinical magnitude-based inference rather than
null-hypothesis significance testing was used [26, 27]. Magnitudes were evaluated mechanisti-
cally: if the confidence interval overlapped substantial positive and negative values, the effect
was deemed unclear; otherwise effects were deemed clear and shown with the probability that
the true effect was substantial using the following scale: 25–75%, possibly; 75–95%, likely; 95–
99.5%, very likely; > 99.5%, most likely [26, 27]. A purpose-built excel spreadsheet for combin-
ing outcomes from several subject groups was used to calculate effect magnitudes, confidence
limits (CL) and inferences [28]. Women’s performance was defined as 100% for comparisons
between male and female athletes (all sport disciplines pooled together).
Results
To keep the results within reasonable limits, only a summary of the results is presented in this
section. However, additional comparisons across category means can be performed by insert-
ing data from the supplementary file into Hopkins’ spreadsheet [28].
Table 1 shows age, body mass and sprint performance across the analyzed sports. Overall,
athletics sprinting (hereafter referred to as “sprinting” or “sprinters”) produced the fastest
sprint times over both the shortest (10 m) and longest (40 m) distances among males, clearly
ahead of bobsleigh (mean difference, ±90%CL: 0.02, ±0.03 and 0.09, ±0.09 s; possibly and
likely; moderate), athletics jumping (hereafter referred to as “jumping” or “jumpers”) (0.07,
±0.03 and 0.15, ±0.09 s; most likely and very likely; large), soccer (0.11, ±0.02 and 0.38, ±0.07 s;
most likely; very large) and all other sports (0.13, ±0.03 to 0.24, ±0.03 and 0.45, ±0.09 to 0.88,
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
4 / 14
±0.09 s; most likely; very large to extremely large). Sprinters also displayed the fastest sprint
times over the same distances among women, clearly ahead of jumpers (0.10, ±0.04 and 0.28,
±0.15 s; most likely and very likely; very large), handball (0.15, ±0.03 and 0.52, ±0.11 s; most
likely; very large), athletics throwing (0.16, ±0.04 and 0.57, ±0.13 s; most likely; very large) and
Table 1. Mean values (± SD) of age, body mass and sprint performance in Norwegian national team athletes (n = 666).
Discipline (sex)
N
Age
BM
10 m
20 m
30 m
40 m
(y)
(kg)
(s)
(s)
(s)
(s)
Alpine skiing (W)
10
22.6 ± 3.3
66.0 ± 4.2
2.19 ± 0.05
3.62 ± 0.10
4.95 ± 0.15
6.29 ± 0.19
Alpine skiing (M)
13
24.7 ± 3.8
84.4 ± 3.7
2.04 ± 0.05
3.30 ± 0.08
4.48 ± 0.10
5.64 ± 0.13
Athletics jumping (W)
8
20.4 ± 4.9
60.4 ± 2.6
2.10 ± 0.06
3.40 ± 0.10
4.60 ± 0.15
5.79 ± 0.19
Athletics jumping (M)
9
21.8 ± 3.6
77.4 ± 6.8
1.97 ± 0.05
3.16 ± 0.06
4.23 ± 0.09
5.28 ± 0.11
Athletics sprinting (W)
5
19.2 ± 3.0
58.4 ± 2.1
2.00 ± 0.02
3.24 ± 0.04
4.39 ± 0.05
5.51 ± 0.09
Athletics sprinting (M)
8
20.7 ± 3.2
71.8 ± 3.8
1.90 ± 0.03
3.05 ± 0.05
4.09 ± 0.08
5.13 ± 0.11
Athletics throwing (W)
10
20.3 ± 3.6
75.0 ± 6.6
2.16 ± 0.06
3.53 ± 0.09
4.81 ± 0.14
6.08 ± 0.20
Athletics throwing (M)
14
22.4 ± 4.7
100.1 ± 8.8
2.03 ± 0.07
3.30 ± 0.11
4.45 ± 0.15
5.58 ± 0.20
Bandy (W)
13
23.0 ± 5.4
64.1 ± 8.6
2.28 ± 0.07
3.76 ± 0.12
5.18 ± 0.16
6.63 ± 0.21
Bandy (M)
23
19.3 ± 2.7
77.4 ± 8.3
2.09 ± 0.06
3.39 ± 0.09
4.60 ± 0.12
5.80 ± 0.16
Basketball (M)
10
22.6 ± 3.3
86.2 ± 9.5
2.06 ± 0.07
3.36 ± 0.12
4.55 ± 0.17
5.74 ± 0.22
Beach-/volleyball (M)
23
24.9 ± 4.7
87.7 ± 7.7
2.04 ± 0.05
3.34 ± 0.09
4.56 ± 0.13
5.78 ± 0.16
Bobsleigh (M)
9
26.7 ± 1.9
92.8 ± 4.4
1.92 ± 0.03
3.10 ± 0.06
4.17 ± 0.07
5.22 ± 0.09
Combat sports (W)
17
23.7 ± 6.0
60.6 ± 5.7
2.30 ± 0.07
3.78 ± 0.12
5.20 ± 0.19
6.62 ± 0.26
Combat sports (M)
32
22.5 ± 4.2
74.8 ± 11.1
2.08 ± 0.07
3.38 ± 0.11
4.59 ± 0.16
5.80 ± 0.23
Cross-country skiing (W)
8
20.0 ± 3.4
59.9 ± 5.1
2.27 ± 0.10
3.73 ± 0.19
5.12 ± 0.28
6.51 ± 0.37
Cross-country skiing (M)
15
21.9 ± 3.4
74.2 ± 4.4
2.11 ± 0.10
3.44 ± 0.16
4.68 ± 0.23
5.88 ± 0.31
Fencing (W)
5
18.9 ± 1.0
64.6 ± 4.0
2.34 ± 0.06
3.86 ± 0.11
5.30 ± 0.18
6.74 ± 0.25
Fencing (M)
10
21.9 ± 2.0
77.1 ± 6.6
2.14 ± 0.04
3.50 ± 0.07
4.76 ± 0.10
6.01 ± 0.13
Handball (W)
32
25.8 ± 4.6
72.8 ± 6.1
2.15 ± 0.07
3.50 ± 0.13
4.77 ± 0.18
6.03 ± 0.24
Handball (M)
18
23.9 ± 3.6
92.7 ± 9.0
2.03 ± 0.04
3.27 ± 0.07
4.43 ± 0.10
5.58 ± 0.14
Ice hockey (M)
34
24.8 ± 4.6
88.7 ± 7.4
2.03 ± 0.06
3.30 ± 0.10
4.46 ± 0.14
5.62 ± 0.19
Mogul skiing (W)
5
19.4 ± 1.9
64.0 ± 9.1
2.18 ± 0.04
3.57 ± 0.10
4.88 ± 0.16
6.19 ± 0.22
Mogul skiing (M)
14
21.2 ± 3.1
72.9 ± 6.3
2.05 ± 0.04
3.32 ± 0.05
4.52 ± 0.09
5.67 ± 0.12
Nordic combined (M)
22
23.5 ± 4.2
69.6 ± 4.0
2.04 ± 0.05
3.33 ± 0.08
4.51 ± 0.11
5.69 ± 0.16
Ski jumping (W)
11
18.4 ± 4.1
56.3 ± 3.1
2.23 ± 0.04
3.68 ± 0.06
5.05 ± 0.11
6.45 ± 0.15
Ski jumping (M)
28
21.2 ± 3.5
64.3 ± 5.0
2.05 ± 0.07
3.34 ± 0.12
4.55 ± 0.18
5.75 ± 0.25
Snowboard (W)
5
21.7 ± 4.6
59.4 ± 3.4
2.24 ± 0.06
3.73 ± 0.11
5.13 ± 0.17
6.61 ± 0.24
Snowboard (M)
9
21.3 ± 3.1
78.5 ± 7.7
2.05 ± 0.05
3.34 ± 0.09
4.55 ± 0.11
5.76 ± 0.16
Soccer (W)
93
23.8 ± 3.9
64.0 ± 4.9
2.17 ± 0.06
3.55 ± 0.11
4.84 ± 0.16
6.12 ± 0.22
Soccer (M)
57
25.4 ± 4.3
78.7 ± 5.8
2.01 ± 0.05
3.24 ± 0.08
4.39 ± 0.12
5.51 ± 0.16
Speed skating (W)
12
21.4 ± 4.0
68.5 ± 5.8
2.20 ± 0.05
3.60 ± 0.08
4.92 ± 0.12
6.25 ± 0.17
Speed skating (M)
22
22.3 ± 3.7
78.6 ± 8.0
2.10 ± 0.06
3.39 ± 0.12
4.59 ± 0.17
5.78 ± 0.23
Table tennis (M)
13
21.1 ± 4.1
69.5 ± 8.8
2.12 ± 0.06
3.46 ± 0.13
4.71 ± 0.19
5.95 ± 0.27
Telemark skiing (W)
5
18.6 ± 1.7
62.0 ± 1.9
2.26 ± 0.13
3.72 ± 0.23
5.10 ± 0.35
6.49 ± 0.49
Telemark skiing (M)
13
23.3 ± 2.6
82.7 ± 6.3
2.08 ± 0.06
3.39 ± 0.08
4.60 ± 0.11
5.80 ± 0.14
Tennis (W)
7
17.5 ± 1.7
65.6 ± 3.3
2.25 ± 0.07
3.70 ± 0.12
5.08 ± 0.18
6.48 ± 0.24
Tennis (M)
11
20.8 ± 3.3
75.3 ± 4.8
2.07 ± 0.05
3.37 ± 0.07
4.57 ± 0.10
5.78 ± 0.13
Weight-/powerlifting (M)
13
20.6 ± 4.2
87.9 ± 22.2
2.12 ± 0.07
3.45 ± 0.10
4.71 ± 0.17
5.96 ± 0.22
W = women, M = men, BM = body mass.
https://doi.org/10.1371/journal.pone.0215551.t001
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
5 / 14
all other sports (0.17, ±0.02 to 0.34, ±0.05 and 0.61, ±0.13 to 1.23, ±0.25 s; most likely; very
large to extremely large). The mean sex difference increased from 6.4% for 10-m sprints to
9.3% for 40-m sprints.
Fig 1A shows F0 across sports. Bobsleigh and sprinting displayed the greatest F0 values
among men (unclear difference between them), clearly ahead of volleyball/beach volleyball
(0.4, ±0.3 to 0.5, ±0.3 Nkg-1; likely to very likely; moderate), snowboard (0.4, ±0.4 to 0.5, ±0.4
Nkg-1; likely to very likely; moderate), soccer (0.5, ±0.3 to 0.6, ±0.3 Nkg-1; very likely to most
likely; moderate to large) and all other sports (0.5, ±0.3 to 1.3, ±0.3; very likely to most likely;
moderate to very large). In women, sprinters exhibited the highest F0 values, clearly ahead of
jumping (0.7, ±0.4 Nkg-1; most likely; very large), handball (0.8, ±0.3 Nkg-1; most likely; very
large), snowboard (0.9, ±0.4 Nkg-1; very likely; very large), alpine skiing (0.9, ±0.3 Nkg-1;
most likely; very large) and all other sports (0.9, ±0.3 Nkg-1 to 1.7, ±0.3 Nkg-1; most likely;
very large to extremely large). The sex difference for F0 was 9.3, ±1.2% (most likely; moderate).
Fig 1B shows v0 across sports. Sprinters showed the highest values among men, clearly
ahead of jumpers (0.4, ±0.4 ms-1; likely; moderate), bobsleigh (0.5, ±0.3 ms-1; very likely;
large), soccer (1.1, ±0.3 ms-1; most likely; very large) and all other male sports (1.2, ±0.3 to 2.1,
±0.3 ms-1; most likely; very large to extremely large). Sprinters also displayed superior values
among women, clearly better than jumpers (0.4, ±0.3 ms-1; very likely; moderate), handball
(1.0, ±0.2 ms-1; most likely; very large), athletics throwing (1.1, ±0.2 ms-1; most likely; very
large) and all other female sports (1.1, ±0.2 to 2.0, ±0.4 ms-1; most likely; very large to
extremely large). The sex difference for v0 was 11.9, ±1.1% (most likely; large).
Fig 2A shows Pmax across sports. In men, sprinters obtained the highest values, clearly
ahead of bobsleigh (0.9, ±1.0 Wkg-1; likely; moderate), jumping (2.2, ±1.1 Wkg-1; most likely;
large), soccer (3.7, ±0.8 Wkg-1; most likely; very large) and all other male sports (4.2, ±0.9 to
7.2, ±0.9 Wkg-1; most likely; very large to extremely large). Sprinting athletes also displayed
the highest Pmax values among women, clearly ahead of jumping (2.4, ±1.1 Wkg-1; most likely;
very large), handball (3.6, ±0.8 Wkg-1; most likely; very large), throwers (4.0, ±0.9 Wkg-1;
most likely; extremely large) and all other female sports (4.2, ±0.7 to 7.3, ±0.8 Wkg-1; most
likely; extremely large). The sex difference for Pmax was 21.9, ±1.1% (most likely; large).
Fig 2B shows SFV across sports. Jumpers produced the highest values (i.e., most velocity-ori-
ented) among the males, ahead of sprinting specialists (0.02, ±0.04 Ns-1m-1kg-1; unclear;
small), speed skating (0.03, ±0.04 Ns-1m-1kg-1; possibly; small) and all other male sports
(0.06, ±0.04 to 0.16, ±0.03 Ns-1m-1kg-1; very likely to most likely; moderate to very large. At
the other end, volleyball/beach volleyball and snowboard were the most force-oriented disci-
plines (unclear difference between them), showing clearly lower SFV values than weight-/
powerlifting (0.03, ±0.04 to 0.04, ±0.04 Ns-1m-1kg-1; likely: small to moderate) and all other
male sports (0.03, ±0.04 to 0.16, ±0.03 Ns-1m-1kg-1; likely to most likely; small to very large).
Among female athletes, jumpers displayed the most velocity-based SFV values, clearly higher
than athletic sprinting (0.04, ±0.05 Ns-1m-1kg-1; likely; moderate) and all other female sports
(0.05, ±0.04 to 0.16, ±0.08 Ns-1m-1kg-1; likely to most likely; moderate to very large). Snow-
board was the most force-oriented group, ahead of bandy (0.05, ±0.07 Ns-1m-1kg-1; unclear;
moderate), ski jumping (0.05, ±0.08 Ns-1m-1kg-1; unclear; moderate) and all other female
sports (0.06, ±0.07 to 0.16, ±0.08 Ns-1m-1kg-1; likely to most likely; moderate to very large).
The sex difference for SFV was 2.4, ±0.7% (most likely; small).
Fig 3A shows RFmax across sports. Sprinters produced the highest percentage values among
men, ahead of bobsleigh (0.4, ±0.8%; unclear; small), jumping (1.4, ±0.8%; very likely; large)
and all other male sports (2.1, ±0.7 to 4.6, ±0.7%; most likely; large to extremely large). Sprint-
ers also displayed the highest values among women, clearly ahead of jumpers (1.7, ±1.1%; very
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
6 / 14
Fig 1. Maximal horizontal force (F0) (Panel A) and theoretical maximal velocity (v0) (Panel B) across sports. The sports are
ranked according to mean values for men.
https://doi.org/10.1371/journal.pone.0215551.g001
Fig 2. Maximal horizontal power (Pmax) (Panel A) and force-velocity slope (SFV) (Panel B) across sports. The sports are ranked
according to mean values for men.
https://doi.org/10.1371/journal.pone.0215551.g002
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
7 / 14
likely; large) and all other sports (2.9, ±0.9 to 5.9, ±1.2%; most likely; very large to extremely
large).
Fig 3B shows DRF across sports. Jumpers displayed the highest values, ahead of sprinters
(0.2, ±0.4%; unclear; small), speed skating (0.4, ±0.4%; likely; moderate) and all other male
sports (0.7, ±0.4 to 1.5, ±0.3%; very likely to most likely; moderate to very large). Among
females, jumpers also obtained the highest values, clearly ahead of sprinters (0.2, ±0.4%;
unclear; small), handball (0.5, ±0.3%; very likely; moderate) and all other female sports (0.5,
±0.3 to 1.5, ±0.7%; very likely to most likely; moderate to very large).
Table 2 shows correlations (±90%CL) across the analyzed variables. The correlations
between sprint mechanical variables and sprint times ranged from trivial to perfect. The corre-
lations between sprint performance and SFV/DRF increased with increasing sprint distance.
Fig 3. Maximal ratio of force (RFmax) (Panel A) and index of force application technique (DRF) (Panel B) across sports. The
sports are ranked according to mean values for men.
https://doi.org/10.1371/journal.pone.0215551.g003
Table 2. Correlations (±90%CL) across analyzed variables.
10-m time
40-m time
F0
v0
Pmax
SFV
RFmax
40-m time
0.96, ±0.01
F0
-0.89, ±0.01
-0.73, ±0.03
v0
-0.87, ±0.02
-0.97, ±0.01
0.57, ±0.04
Pmax
-1.00, ±0.01
-0.97, ±0.01
0.88, ±0.02
0.89, ±0.01
SFV
-0.02, ±0.06
-0.30, ±0.06
-0.43, ±0.05
0.50, ±0.05
0.05, ±0.06
RFmax
-1.00, ±0.01
-0.96, ±0.01
0.89, ±0.01
0.88, ±0.02
1.00, ±0.01
0.03, ±0.06
DRF
-0.15, ±0.06
-0.42, ±0.05
-0.30, ±0.06
0.62, ±0.04
0.19, ±0.06
0.99, ±0.01
0.17, ±0.06
F0 = maximal horizontal force (relative to body mass), v0 = theoretical maximal velocity, Pmax = maximal horizontal power (relative to body mass), SFV = force-velocity
slope (relative to body mass), RFmax = ratio of force, DRF = index of force application technique.
https://doi.org/10.1371/journal.pone.0215551.t002
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
8 / 14
Discussion
To our knowledge, this is the first study to explore and compare underlying physiological and
mechanical variables of sprint performance across a wide range of sports. Up to very large and
even extremely large differences in sprint mechanical variables were observed across sports.
Overall, sports in which sprinting ability is an important predictor of success scored the high-
est values for most variables, while sports involving other locomotion modalities than running
tended to produce substantially lower values. The current data from a large sample of elite ath-
letes tested under identical conditions provides a holistic picture of the Fv profile continuum
in sprinting athletes. In the following paragraphs, we will discuss each of the analysed variables
more in detail.
F0 in the present sample was in the range 7–10 Nkg-1 for men and 6–9 Nkg-1 for women,
corresponding to a mean sex difference of 9.3%. Athletic sprinters and bobsleigh contestants
were at the upper end of the scale. Previously published studies have shown that world-class
male and female sprinters can reach 11 and 10 Nkg-1, respectively [10], representing the cur-
rent upper limits for horizontal force production relative to body mass during accelerated
sprinting. However, F0, calculated with the simple method outlined by Samozino et al. [9], is
larger during resisted sprinting compared to unloaded sprints [14, 15, 29]. Hence, F0 derived
from normal sprinting appears not to be a true F0, as the resistance in overcoming body mass
inertia appears insufficient for maximal horizontal force-capacity generation. For practition-
ers, the importance of F0 is perfectly illustrated by the fact that bobsleigh athletes displayed
slightly higher F0-values (and clearly higher body mass) than sprinters despite slightly poorer
sprint times across all time splits. Thus, F0 is a particularly crucial measure for athletes who
perform brief sprints while moving an external mass. The shorter the distance considered, the
higher the correlation between sprint performance and F0 (see Table 2).
Cross et al. [17] reported 8.5 ± 1.3 and 8.8 ± 0.4 Nkg-1 for elite rugby union forwards and
backs, and 8.1 ± 0.8 and 8.2 ± 1.0 Nkg-1 for corresponding rugby league players. These F0-val-
ues are on par with the present male soccer players. Interestingly, although rugby players are
generally heavier than soccer players, they do not produce higher F0 when normalized for
body mass. As an athlete gets heavier, the energy cost of accelerating that mass also increases,
as does the aerodynamic drag associated with pushing that wider frontal area through the air
[30]. “Bigger” is therefore not necessarily better for sprinting, at least when there is no external
mass to push. Moreover, volleyball/beach volleyball were among the best sports in terms of F0
scores, while weight-/powerlifters produced clearly lower values, despite no substantial group
mean differences in body mass. This confirms previous findings that vertically-oriented and
heavy strength training of the lower limbs does not necessarily translate to higher horizontal
force production during accelerated sprinting [31].
The correlation between v0 and sprint performance was very large for 10-m sprint, and the
correlation values increased with increasing sprint distance (see Table 2). V0 was in the range
7.5–11 ms-1 for men and 6–9.5 ms-1 for women, equivalent to a mean sex difference of 11.9%.
Not surprisingly, sprinters obtained the highest scores. For comparison, the world’s fastest
male and female track sprinters reach peak velocities of ~12 and ~11 ms-1, respectively [10,
32]. The fastest male and female team sport athletes in our material approached/exceeded 10
and 9 ms-1, respectively, in line with previous reports [33–35]. Many practitioners would
argue that elite wide receivers, running backs and/or cornerbacks in American football are
even faster, but no studies to date have presented comparable data.
Metabolic energy turnover and efficient transfer to external power output underlies success-
ful performance in many sports. We observed perfect or extremely large correlations between
Pmax and sprint performance, depending on distance (Table 2). Sprint time improvement is
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
9 / 14
not a linear function of power increase. Indeed, the change in velocity (Δv) is related to the
cube of the change in power (ΔP3), such that a 5% increase in velocity would require a nearly
16% increase in power [36]. The present data are consistent with this relationship. We
observed Pmax values in the range 13–25 Wkg-1 for men and 11–21 Wkg-1 for women. The
mean sex difference observed (21.9%), based on pooled data of all disciplines, corresponds well
with data for elite sprinters. Slawinski et al. [10] reported that Pmax in male and female world-
class sprinters was 30.3 ± 2.5 and 24.5 ± 4.2 Wkg-1, respectively, typically attained after ~1 s of
sprinting. The highest individual values were 36.1 Wkg-1 and 29.3 Wkg-1, representing cur-
rent upper limits in humans [37]. Athletes achieve two-to-three times higher Wkg-1 during
countermovement jump (CMJ) compared to sprinting [37]. However, because it is not possible
to assess a generic anaerobic maximal power, each anaerobic power test must be treated sepa-
rately, and comparisons of power values across modalities are meaningless.
SFV reflects the athlete’s individual balance between force and velocity capabilities. Morin &
Samozino [11] have suggested that athletes with velocity values lower for the sport could be
prescribed more maximal velocity sprinting, whereas athletes with horizontal force values
lower for the sport could be prescribed more horizontal strength training. The present results
show that SFV ranged from -0.75 to -1.10 Ns-1m-1kg-1 for men and -0.80 to -1.15 Ns-1m-
1kg-1 for women, corresponding to a sex difference of 2.4%. The group mean values of
national-level sprinters were similar to the SFV values observed in world-class sprinters [10]. In
the current study, women displayed lower slope values than men in all the analyzed sports
where both sexes were represented. If we follow the approach proposed by Morin & Samozino
[11], men should generally perform more force-oriented training than women. To our knowl-
edge, no previous studies have recommended differentiated sprint-training programs accord-
ing to sex. While young and untrained individuals tend to show improvements irrespective of
training methods [38], well-trained senior athletes only achieve annual improvements smaller
than typical variation [39].
Sprinting distance must also be considered if training prescription should be based on SFV
orientation. The correlation between sprint performance and SFV increased (towards more
velocity-oriented FV-profile) with increasing sprint distance (Table 2). This suggests that the
longer the sprint distance, the more velocity-oriented training should be prescribed. Force-ori-
ented sprint training (e.g., resisted sprints) is likely more appropriate for sports where the ath-
letes are required to perform brief sprints while moving an external mass (e.g., bobsleigh).
However, macroscopic Fv profiles derived from the simple method provide limited informa-
tion about the Fv relationship of the individual muscles involved. The fascicle shortening
velocity of the different muscles engaged do not necessarily change with increasing running
velocity, and this inconsistent relationship is explained by an augmented contribution from
elastic properties with increasing running velocity [40, 41]. Hence, running velocity is not a
proxy for muscle contraction velocity, and one cannot use Fv profiles derived from sprint tests
to determine training prescriptions for muscles in isolation.
RFmax reflects the proportion of the total force production that is directed in the forward
direction of motion at sprint start [9]. We observed a perfect correlation between RFmax and
Pmax (r = 1.0, Table 2). This is mechanically sound, as Pmax in the simple model corresponds to
the peak of the power curve (i.e., the maximal product of horizontal force and velocity), while
vertical force corresponds to body mass when averaged over one step. Hence, Pmax and RFmax
are two measures of the same capability. Within our material, RFmax ranged from 41 to 52% in
men and 37 to 48% in women. Rabita et al. [8] reported 71.6 ± 2.6% in male world-class sprint-
ers, but these values are not directly comparable due to methodological differences. In the
Rabita et al. paper [8], RF was computed from force platform data, where the y-intercept of the
extrapolated linear RF-velocity curve was defined as RFmax, that is; the theoretical maximal
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
10 / 14
contribution of anteroposterior force to the total force produced over one contact phase at
zero velocity. For the simple method, where mechanical variables are calculated from anthro-
pometric and spatiotemporal data, RFmax does not correspond to the extrapolated value at
zero velocity, but to the value at 0.5 s (corresponding to the RF-value approximately at the first
step). Based on publicly available split time and anthropometric data of world-class sprinters
[42], we calculated RFmax values at 0.5 s as high as 56–57% in men and 52–53% in women,
using the simple model. However, the maximal possible value of RFmax (100%) is not optimal
for sprinting because a certain amount of vertical force is required to work against gravity.
DRF expresses the athletes’ ability to maintain a net horizontal force production despite
increasing running velocity. The more negative the slope, the faster the loss of net horizontal
force during acceleration, and vice versa. In the present dataset, the values ranged from -7 to
-10.5% among the men and -7.5 to -11% among the women. For comparisons, Rabita et al. [8]
reported −6.4 ± 0.3% for male world-class sprinters. In practical terms, DRF reflects the dis-
tance over which athletes are able to accelerate (i.e., distance to peak velocity). Previous
research has shown that the duration of the acceleration phase varies as a function of athlete
performance level. Team sport athletes [33, 34], students [43] and prepubescent children [44]
typically achieve peak velocity at ~ 25–30 m of maximal linear sprinting. National and interna-
tional 100-m track sprinters attain peak velocity after 40–50 and 50–80 m of sprinting, respec-
tively, but men peak ~20% further in distance than women [10, 32, 45]. The nearly perfect
correlation between DRF and SFV (r = 0.99, Table 2) is logical and expected, as the most veloc-
ity-oriented athletes are able to accelerate over a longer distance than their more force-ori-
ented counterparts.
Conclusion
In the present study, substantial differences in sprint mechanical properties were observed
across sports. Based on these findings, some may argue that the chronic practice of an activity
induces different Fv profiles in sprint running over time. However, the large spread within
each discipline, in addition to the large overlap across sports, indicate that such variables are
more individual than sport specific. Most sprint mechanical variables are strongly correlated
with sprint performance level, in line with the laws of motion. Indeed, when split times and
anthropometric data form basis for calculations of multiple variables, it is reasonable to expect
high correlations among them. Based on these considerations, practitioners may question the
practical relevance of such variables, as they are entwined, and in some cases, mere ‘different
explanations of the same story’. However, while split time data provide a basis for convenient
analysis on the field, sprint mechanical variables may provide deeper insights into individual
biomechanical limitations. The values presented here can be used by practitioners to develop
individual training interventions.
Supporting information
S1 File. Excel File containing sample size, group means and SD for all variables across
sports.
(DOCX)
Author Contributions
Conceptualization: Thomas A. Haugen, Stephen Seiler.
Data curation: Thomas A. Haugen, Felix Breitscha¨del.
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
11 / 14
Formal analysis: Thomas A. Haugen, Felix Breitscha¨del.
Investigation: Thomas A. Haugen, Felix Breitscha¨del, Stephen Seiler.
Methodology: Thomas A. Haugen, Felix Breitscha¨del, Stephen Seiler.
Project administration: Thomas A. Haugen, Stephen Seiler.
Software: Felix Breitscha¨del.
Supervision: Thomas A. Haugen, Stephen Seiler.
Writing – original draft: Thomas A. Haugen, Stephen Seiler.
Writing – review & editing: Felix Breitscha¨del, Stephen Seiler.
References
1.
Rumpf MC, Lockie RG, Cronin JB, Jalilvand F. Effect of different sprint training methods on sprint per-
formance over various distances: A brief review. J Strength Cond Res. 2016; 30(6):1767–85. https://
doi.org/10.1519/JSC.0000000000001245 PMID: 26492101
2.
Haugen T, Tønnessen E, Hisdal J, Seiler S. The role and development of sprinting speed in soccer.
Brief review. Int J Sports Physiol Perform. 2014; 9(3): 432–41. https://doi.org/10.1123/ijspp.2013-0121
PMID: 23982902
3.
Petrakos G, Morin JB, Egan B. Resisted sled sprint training to improve sprint performance: a systematic
review. Sports Med. 2016; 46(3):381–400. https://doi.org/10.1007/s40279-015-0422-8 PMID:
26553497
4.
Fenn WO, Marsh BS. Muscular force at different speeds of shortening. J Physiol. 1935; 85(3): 277–97.
https://doi.org/10.1113/jphysiol.1935.sp003318 PMID: 16994712
5.
Wilkie DR. The relation between force and velocity in human muscle. J Physiol. 1950; 110(3–4):249–
80.
6.
Morin JB, Edouard P, Samozino P. Technical ability of force application as a determinant factor of sprint
performance. Med Sci Sports Exerc. 2011; 43(9):1680–8. https://doi.org/10.1249/MSS.
0b013e318216ea37 PMID: 21364480
7.
Morin JB, Bourdin M, Edouard P, Peyrot N, Samozino P, Lacour JR. Mechanical determinants of 100-m
sprint running performance. Eur J Appl Physiol. 2012; 112(11): 3921–30. https://doi.org/10.1007/
s00421-012-2379-8 PMID: 22422028
8.
Rabita G, Dorel S, Slawinski J, Sàez-de-Villarreal E, Couturier A, Samozino P, et al. Sprint mechanics
in world-class athletes: a new insight into the limits of human locomotion. Scand J Med Sci Sports.
2015; 25(5):583–94. https://doi.org/10.1111/sms.12389 PMID: 25640466
9.
Samozino P, Rabita G, Dorel S, Slawinski J, Peyrot N, Saez de Villarreal E, et al. A simple method for
measuring power, force, velocity properties, and mechanical effectiveness in sprint running. Scand J
Med Sci Sports. 2016; 26(6):648–58. https://doi.org/10.1111/sms.12490 PMID: 25996964
10.
Slawinski J, Termoz N, Rabita G, Guilhem G, Dorel S, Morin JB, et al. How 100-m event analyses
improve our understanding of world-class men’s and women’s sprint performance. Scand J Med Sci
Sports. 2017; 27(1):45–54. https://doi.org/10.1111/sms.12627 PMID: 26644061
11.
Morin JB, Samozino P. Interpreting power-force-velocity profiles for individualised and specific training.
Int J Sports Physiol Perform. 2016; 11(2):267–72. https://doi.org/10.1123/ijspp.2015-0638 PMID:
26694658
12.
Cross MR, Brughelli M, Samozino P, Morin JB. Methods of power-force-velocity profiling during sprint
running: A narrative review. Sports Med. 2017; 47(7):1255–69. https://doi.org/10.1007/s40279-016-
0653-3 PMID: 27896682
13.
Rakovic E, Paulsen G, Helland C, Eriksrud O, Haugen T. The effect of individualised sprint training in
elite female team sport athletes: A pilot study. J Sports Sci. 2017; 9: 1–7. https://doi.org/10.1080/
02640414.2018.1474536 PMID: 29741443
14.
Monte A, Nardello F, Zamparo P. Sled Towing: The optimal overload for peak power production. Int J
Sports Physiol Perform. 2017; 12(8):1052–1058. https://doi.org/10.1123/ijspp.2016-0602 PMID:
27967284
15.
Pantoja PD, Carvalho AR, Ribas LR, Peyre´-Tartaruga LA. Effect of weighted sled towing on sprinting
effectiveness, power and force-velocity relationship. PLoS One 2018; 13(10):e0204473. https://doi.org/
10.1371/journal.pone.0204473 PMID: 30289907 eCollection 2018.
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
12 / 14
16.
Buchheit M, Samozino P, Glynn JA, Michael BS, Al Haddad H, Mendez-Villanueva A, et al. Mechanical
determinants of acceleration and maximal sprinting speed in highly trained young soccer players. J
Sports Sci. 2014; 32(20):1906–13. https://doi.org/10.1080/02640414.2014.965191 PMID: 25356503
17.
Cross MR, Brughelli M, Brown SR, Samozino P, Gill ND, Cronin JB, et al. Mechanical properties of
sprinting in elite rugby union and rugby league. Int J Sports Physiol Perform. 2015; 10(6):695–702.
PMID: 25310279
18.
Nagahara R, Morin JB, Koido M. Impairment of sprint mechanical properties in an actual soccer match:
A pilot study. Int J Sports Physiol Perform. 2016; 11(7):893–8. https://doi.org/10.1123/ijspp.2015-0567
PMID: 26791405
19.
Morin JB, Petrakos G, Jime´nez-Reyes P, Brown SR, Samozino P, Cross MR. Very-heavy sled training
for improving horizontal-force output in soccer players. Int J Sports Physiol Perform. 2017; 12(6):840–4.
https://doi.org/10.1123/ijspp.2016-0444 PMID: 27834560
20.
Winter EM, Maughan RJ. Requirements for ethics approvals. J Sports Sci. 2009; 27(10): 985. https://
doi.org/10.1080/02640410903178344 PMID: 19847681
21.
Morin JB, Samozino P. Spreadsheet for sprint acceleration force-velocity-power profiling. Research-
Gate 2017. https://www.researchgate.net/publication/321767606_Spreadsheet_for_Sprint_
acceleration_force-velocity-power_profiling?_sg=CuBBw_XwgEAtdCkL8QKaMMLUEFzmLp
EIkMDsHU8dJoYTgIEc2ajruKZRlXrNB7njaQPI-HyYZ5SJiJ5I9ViryIwEvCxzpweYnPHHcVjJ.FNC82J-
TAKYvWmcYRn5itbWhbTST_WIyHM9Z7VPv_bAQ-yvAm9WMnmdEyYOb7DccGEWA3g1_
GmcC3KlwRiQERQ
22.
Haugen T, Tønnessen E, Seiler S. The difference is in the start: impact of timing and start procedure on
sprint running performance. J Strength Cond Res. 2012: 26(2):473–9. https://doi.org/10.1519/JSC.
0b013e318226030b PMID: 22233797
23.
Haugen T, Tønnessen E, Svendsen I, Seiler S. Sprint time differences between single and dual beamed
timing systems. Technical report. J Strength Cond Res. 2014; 28(8): 2376–9. https://doi.org/10.1519/
JSC.0000000000000415 PMID: 24531428
24.
Haugen TA, Breitscha¨del F, Samozino P. Power-force-velocity profiling of sprinting athletes: methodo-
logical and practical considerations when using timing gates. J Strength Cond Res. 2018. https://doi.
org/10.1519/JSC.0000000000002890 PMID: 30273283 (forthcoming).
25.
Haugen T, Buchheit M. Sprint running performance monitoring: methodological and practical consider-
ations. Sports Med. 2016; 46(5):641–56. https://doi.org/10.1007/s40279-015-0446-0 PMID: 26660758
26.
Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine
and exercise science. Med Sci Sports Exerc. 2009; 41(1):3–13. https://doi.org/10.1249/MSS.
0b013e31818cb278 PMID: 19092709
27.
Hopkins WG, Batterham AM. The vindication of magnitude-based inference. Sportsci. 2018; 22:19–29.
28.
Hopkins WG. A spreadsheet for combining outcomes from several subject groups. Sportsci. 2006;
10:51–53.
29.
Helland C, Haugen T, Rakovic E, Eriksrud O, Seynnes O, Mero AA, et al. Force-velocity profiling of
sprinting athletes: single- vs. multiple-run methods. Eur J Appl Physiol. 2018. https://doi.org/10.1007/
s00421-018-4045-2 PMID: 30519907 (forthcoming).
30.
Haugen T, Tønnessen E, Seiler S. 9.58 and 10.49: Nearing the citius end for 100-m? Invited commen-
tary. Int J Sports Physiol Perform. 2015; 10(2):269–72. https://doi.org/10.1123/ijspp.2014-0350 PMID:
25229725
31.
Loturco I, Contreras B, Kobal R, Fernandes V, Moura N, Siqueira F, et al. Vertically and horizontally
directed muscle power exercises: Relationships with top-level sprint performance. PLoS One 2018; 13
(7):e0201475. https://doi.org/10.1371/journal.pone.0201475 PMID: 30048538
32.
Graubner R, Nixdorf E. Biomechanical analysis of the sprint and hurdles events at the 2009 IAAF World
Championships in athletics. New Stud Athletics 2011; 26:19–53.
33.
Haugen T, Tønnessen E, Seiler S. Anaerobic performance testing of professional soccer players 1995–
2010. Int J Sport Physiol Perform. 2013; 8(2):148–56.
34.
Haugen T, Tønnessen E, Seiler S. Speed and countermovement jump characteristics of elite female
soccer players 1995–2010. Int J Sport Physiol Perform. 2012; 7(4):340–9.
35.
Schimpchen J, Skorski S, Nopp S, Meyer T. Are "classical" tests of repeated-sprint ability in football
externally valid? A new approach to determine in-game sprinting behaviour in elite football players. J
Sports Sci. 2016; 34(6):519–26. https://doi.org/10.1080/02640414.2015.1112023 PMID: 26580089
36.
Seiler S, De Koning JJ, Foster C. The fall and rise of the gender difference in elite anaerobic perfor-
mance 1952–2006. Med Sci Sports Exerc. 2007; 39(3):534–40. https://doi.org/10.1249/01.mss.
0000247005.17342.2b PMID: 17473780
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
13 / 14
37.
Haugen T, Paulsen G, Seiler S, Sandbakk Ø. New records in human power. Int J Sports Physiol Per-
form. 2018; 13(6):678–86. https://doi.org/10.1123/ijspp.2017-0441 PMID: 28872385
38.
Tønnessen E, Svendsen I, Olsen IC, Guttormsen A, Haugen T. Performance development in adoles-
cent track and field athletes according to age, sex and sport discipline. PLoS ONE 2015; 10(6):
e0129014. https://doi.org/10.1371/journal.pone.0129014 PMID: 26043192
39.
Haugen T, Solberg PA, Mora´n-Navarro R, Breitscha¨del F, Hopkins W, Foster C. Peak age and perfor-
mance progression in world-class track-and-field athletes. Int J Sports Physiol Perform. 2018; 13
(9):1122–1129. https://doi.org/10.1123/ijspp.2017-0682 PMID: 29543080
40.
Lai A, Schache AG, Brown NA, Pandy MG. Human ankle plantar flexor muscle-tendon mechanics and
energetics during maximum acceleration sprinting. J R Soc Interface 2016; 13(121). https://doi.org/10.
1098/rsif.2016.0391 PMID: 27581481
41.
Miller RH, Umberger BR, Caldwell GE. Sensitivity of maximum sprinting speed to characteristic param-
eters of the muscle force-velocity relationship. J Biomech. 2012; 45(8):1406–43. https://doi.org/10.
1016/j.jbiomech.2012.02.024 PMID: 22405495
42.
Ferro A, Riveral A, Pagola I, Ferreruela M, Martin A, Rocandio V. A kinematic study of the sprint events
at the 1999 World Championships in athletics in Sevilla. 20th International Symposium on Biomechan-
ics in Sports, 2002.
43.
Babić V, Čoh M, Dizdar D. Differences in kinematics parameters of athletes of different running quality.
Biol Sport. 2011; 28:115–121.
44.
Chatzilazaridis I. Stride characteristics progress in a 40-m sprinting test executed by male preadoles-
cent, adolescent and adult athletes. Biol Exerc. 2012; 8:59–77. https://doi.org/10.4127/jbe.2012.0060
45.
Debaere S, Jonkers I, Delecluse C. The contribution of step characteristics to sprint running perfor-
mance in high-level male and female athletes. J Strength Cond Res. 2013; 27(1):116–24. https://doi.
org/10.1519/JSC.0b013e31825183ef PMID: 22395270
Sprint mechanical variables in athletes
PLOS ONE | https://doi.org/10.1371/journal.pone.0215551
July 24, 2019
14 / 14

Sprint mechanical variables in elite athletes: Are force-velocity profiles sport specific or individual?
Haugen, Thomas A,Breitschädel, Felix,Seiler, Stephen