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Recent studies have brought new insights into the evaluation of power-force-velocity profiles in both ballistic push-offs (e.g. jumps) and sprint movements. These are major physical components of performance in many sports, and the methods we developed and validated are based on data that are now rather simple to obtain in field conditions (e.g. body mass, jump height, sprint times or velocity). The promising aspect of these approaches is that they allow for a more individualized and accurate evaluation, monitoring, and training practices; the success of which are highly dependent on the correct collection, generation and interpretation of athletes' mechanical outputs. We therefore wanted to provide a practical vade mecum to sports practitioners interested in implementing these power-force-velocity profiling approaches. After providing a summary of theoretical and practical definitions for the main variables, we have first detailed how vertical profiling can be used to manage ballistic push-off performance with emphasis on the concept of optimal force-velocity profile and the associated force-velocity imbalance. Further, we have discussed these same concepts with regards to horizontal profiling in the management of sprinting performance. These sections have been illustrated by typical examples from our own practice. Finally, we have provided a practical and operational synthesis, and outlined future challenges that will help in further developing these approaches.
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267
INVITED COMMENTARY
International Journal of Sports Physiology and Performance, 2016, 11, 267 -272
http://dx.doi.org/10.1123/ijspp.2015-0638
© 2016 Human Kinetics, Inc.
One of the main physical performance determinants in sports
such as athletics, rugby, soccer, football, volleyball, and basketball
is the ability to produce high mechanical power output during jumps
and sprint accelerations.
1,2
This power output depends on the ability
of athletes’ neuromuscular and osteoarticular systems to generate
high levels of force, apply it with effectiveness onto the environ-
ment (ie, supporting ground, ball, projectile), and produce this
force at high contraction velocity. Force and velocity are therefore
considered the underpinning features of mechanical power output
in sport movements.
3,4
Although the assessment and long-term
monitoring of these capabilities is paramount for both performance
and rehabilitation processes, such an accurate evaluation has long
been associated with expensive and often laboratory-based tech-
nologies. Recently, our research group has presented simple eld
methods to compute force, velocity, and power output in jumping
5
and sprinting
6
calculated via measurements from widely accessible
and practical devices. Thanks to these methods, all the important
mechanical outputs of jumping and sprinting can be derived from
basic measures of body mass, lower-limb length, jump height, and
distance–time or speed–time measurements only.
6–8
Recently, we had the opportunity to discuss the implementa-
tion of these “simple methods” with many sport practitioners, and
we realized that beyond the description presented in the published
papers, it was necessary to detail how to interpret the measurements
for an efcient use in everyday practice. Our aim here is to provide
a practical vade mecum to readers wishing to use power-force-
velocity proling for more individualized diagnostic and efcient
training. The key points of this commentary will be supported by
illustrations of typical data collected in our research, training, or
consultancy practice over the past decade.
Definitions
The power-force-velocity–proling approach is based on force–
velocity (F–V) and power–velocity relationships characterizing the
maximal mechanical capabilities of the lower limbs’ neuromuscular
system. The denition and the practical interpretation of the main
mechanical variables of interest are presented in Table 1.
Vertical Profiling for Ballistic Push-Off
Performance
The input measurements necessary to correctly determine vertical
prole
5,9,10
are the athlete’s body mass, lower-limb length in fully
extended position, starting height, and jump height (measured under
a spectrum of loading parameters). The latter can now be easily and
accurately measured using simple and accessible devices.
7,8
Jump
height should be measured across repeated measurements with at
least 5 additional loads (evenly ranging between 0 kg and the addi-
tional load with which the athlete is able to jump about 10 cm), after
which the F–V prole and all other computations can be completed.
Research conclusions show that jumping performance is deter-
mined by maximal mechanical power output (VTC-Pmax) and the
magnitude of the relative difference between the slope of the linear
F–V relationship (Sfv) and Sfv
opt
for a given individual (FVimb).
9
Thus, in practical terms, should a training program be designed to
improve athletes’ ballistic push-off performance (eg, jumps, single
maximal push-offs, change of direction), the focus should be placed
on increasing VTC-Pmax and/or decreasing FVimb. With regard to
athletes displaying signicant imbalance in mechanical capacities,
Interpreting Power-Force-Velocity Profiles
for Individualized and Specific Training
Jean-Benoît Morin and Pierre Samozino
Recent studies have brought new insights into the evaluation of power-force-velocity proles in both ballistic push-offs (eg,
jumps) and sprint movements. These are major physical components of performance in many sports, and the methods the authors
developed and validated are based on data that are now rather simple to obtain in eld conditions (eg, body mass, jump height,
sprint times, or velocity). The promising aspect of these approaches is that they allow for more individualized and accurate
evaluation, monitoring, and training practices, the success of which is highly dependent on the correct collection, generation,
and interpretation of athletes’ mechanical outputs. The authors therefore wanted to provide a practical vade mecum to sports
practitioners interested in implementing these power-force-velocity–proling approaches. After providing a summary of theo-
retical and practical denitions for the main variables, the authors rst detail how vertical proling can be used to manage bal-
listic push-off performance, with emphasis on the concept of optimal force–velocity prole and the associated force–velocity
imbalance. Furthermore, they discuss these same concepts with regard to horizontal proling in the management of sprinting
performance. These sections are illustrated by typical examples from the authors’ practice. Finally, they provide a practical and
operational synthesis and outline future challenges that will help further develop these approaches.
Keywords: explosive performance, jump, sprint, team sports, athletics, strength training
Morin is with the Laboratory of Human Motricity, Education Sport and
Health, University of Nice Sophia Antipolis, Nice, France. Samozino is
with the Inter-Universitary Laboratory of Human Movement Biology,
University Savoie Mont Blanc, Le Bourget-du-Lac, France. Address author
correspondence to Jean-Benoît Morin at jean-benoit.morin@unice.fr.
268 IJSPP Vol. 11, No. 2, 2016
Table 1 Definition and Practical Interpretation of the Main Variables of Interest When Using Power-Force-Velocity
Profiling in Ballistic Push-Offs (Vertical Profiling) and Sprinting (Horizontal Profiling)
Profiling variable Definition and computation Practical interpretation
Vertical
VTC-F0 (N/kg) Theoretical maximal force production of
the lower limbs as extrapolated from the
linear loaded jump squats’ force–veloc-
ity (F–V) relationship; y-intercept of the
linear F–V relationship.
Maximal concentric force output (per unit body mass) that the athlete’s lower
limbs can theoretically produce during ballistic push-off. Determined from
the entire F–V spectrum, it gives more integrative information on force capa-
bility than, eg, concentric squat 1-repetition-maximum load.
VTC-V0 (m/s) Theoretical maximal extension velocity
of the lower limbs as extrapolated from
the linear loaded jump squats’ F–V rela-
tionship; x-intercept of the linear F–V
relationship.
Maximal extension velocity of the athlete’s lower limbs during ballistic
push-off. Determined from the entire F–V spectrum and very difcult, if not
impossible, to reach and measure experimentally. It also represents the capa-
bility to produce force at very high extension velocities.
VTC-Pmax (W/kg) Maximal mechanical power output,
computed as Pmax = F0 × V0/4 or as the
apex of the P–V 2nd-degree polynomial
relationship.
Maximal power output capability of the athlete’s lower-limb neuromuscular
system (per unit body mass) in the concentric and ballistic extension motion.
Sfv Slope of the linear F–V relationship,
computed as Sfv = –F0/V0.
Index of the athlete’s individual balance between force and velocity capa-
bilities. The steeper the slope, the more negative its value, the more “force-
oriented” the F–V prole, and vice versa.
Sfv
opt
For a given push-off distance, body
mass, and Pmax, the unique value of Sfv
that maximizes jump height. For detailed
computation, see Appendix in Samozino
et al.
10
The optimal F–V prole that represents the optimal balance, for a given indi-
vidual, between force and velocity capabilities. For a given maximal power
Pmax, this prole will be associated, ceteris paribus, with the highest ballistic
push-off performance possible for this individual. Training programs should
be designed to both increase Pmax and orient Sfv toward Sfv
opt
.
FVimb (%) Magnitude of the relative difference
between Sfv and Sfv
opt
for a given indi-
vidual. Computed as (Sfv/Sfv
opt
) × 100
and expressed in percentage.
Magnitude of the difference between actual and optimal F–V proles. A value
of 100% means Sfv = Sfv
opt
, ie, optimized F–V prole. Values above 100%
mean an imbalance with a decit in velocity, and vice versa. The larger the
difference with the optimal 100% value, the larger the imbalance.
Horizontal
HZT-F0 (N/kg) Theoretical maximal horizontal force
production as extrapolated from the
linear sprint F–V relationship; y-inter-
cept of the linear F–V relationship.
Maximal force output (per unit body mass) in the horizontal direction. Cor-
responds to the initial push of the athlete onto the ground during sprint accel-
eration. The higher the value, the higher the sprint-specic horizontal force
production.
HZT-V0 (m/s) Theoretical maximal running velocity as
extrapolated from the linear sprint F–V
relationship; x-intercept of the linear
F–V relationship.
Sprint-running maximal velocity capability of the athlete. Slightly higher than
the actual maximal velocity. The theoretical maximal running velocity the
athlete would be able to reach should mechanical resistances (ie, internal and
external) against movement be null. It also represents the capability to pro-
duce horizontal force at very high running velocities.
HZT-Pmax (W/kg) Maximal mechanical power output in the
horizontal direction, computed as Pmax
= F0 × V0/4, or as the apex of the P–V
2nd-degree polynomial relationship.
Maximal power-output capability of the athlete in the horizontal direction
(per unit body mass) during sprint acceleration.
RF (%) Ratio of force, computed as the ratio of
the step-averaged horizontal component
of the ground-reaction force to the cor-
responding resultant force.
Direct measurement of the proportion of the total force production that is
directed in the forward direction of motion, ie, the mechanical effectiveness
of force application of the athlete. The higher the value, the more important
the part of the total force output directed forward.
RFmax (%) Maximal value of RF, computed as max-
imal value of RF for sprint times >0.3 s.
Theoretically maximal effectiveness of force application. Direct measurement
of the proportion of the total force production that is directed in the forward
direction of motion at sprint start.
D
RF
Rate of decrease in RF with increasing
speed during sprint acceleration, com-
puted as the slope of the linear RF–V
relationship.
Describes the athlete’s capability to limit the inevitable decrease in mechani-
cal effectiveness with increasing speed, ie, an index of the ability to maintain
a net horizontal force production despite increasing running velocity. The
more negative the slope, the faster the loss of effectiveness of force applica-
tion during acceleration, and vice versa.
IJSPP Vol. 11, No. 2, 2016
Power-Force-Velocity Profiles and Training 269
training programs should prioritize training the lacking mechanical
capability to shift Sfv toward Sfv
opt
. The main interest of the current
approach is that the diagnostics, and resultant training periodization,
are individualized and easily monitored. Consequently, the ability
to frequently monitor these outputs permits the analysis of changes
in VTC-Pmax and FVimb over time (eg, once every month) and
can assist in the targeted implementation and reimplementation of
efcient and dynamic programming practices. The rst case report
(Figure 1) illustrates this with data from 2 athletes with a similar
push-off distance. Although athlete A has a higher VTC-Pmax, his
squat-jump performance is lower because he has an F–V imbalance.
Athlete B has a lower VTC-Pmax, but his prole is almost exactly
equal to his individual optimal prole (only 1% imbalance). The
current approach would suggest, therefore, that athlete As training
should prioritize the development of maximal force capabilities to
correct his imbalance and increase VTC-Pmax. Once this goal is
achieved, he may transition into training similar to that of athlete
B, to improve his VTC-Pmax while maintaining his corrected (ie,
optimal) prole.
The second example shows 2 young players from the same
soccer team (French rst-league professional club academy U19).
As shown in Figure 2, these players have quite similar VTC-Pmax
and Sfv
opt
values but display opposing FVimb characteristics: Player
A shows a force decit, whereas player B shows a velocity decit.
Furthermore, the absolute difference with their respective Sfv
opt
is
lower in player B than in player A (28% vs 37%). This relatively
smaller FVimb and slightly higher VTC-Pmax in player B explain
his higher squat-jump performance.
With this result in mind, this approach suggests that the most
efcient way to train and improve ballistic push-off performance
in both these players would be an individualized program (indexed
on each player’s FVimb) that targets the development of different
Figure 1
Vertical force–velocity proles of 2 track and eld athletes (body mass for A, 67.2 kg, and B, 82.8 kg; push-off distance for A, 0.34 m, and
B, 0.35 m) obtained from maximal squat jumps (SJ) against additional loads of 0, 10, 20, 30, and 40 kg. Despite a higher VTC-Pmax (maximal mechanical
power output) value, athlete As squat-jump performance is lower because his FV
imb
(magnitude of the relative difference between the slope of the linear
force–velocity relationship [Sfv] and Sfv
opt
) is greater than for athlete B. For athlete A, the black line indicates the actual prole, and the dashed line, the
optimal prole. Note that athlete B’s prole if almost optimal, and therefore the actual and optimal relationships are confounded in the right panel (gray
line and black dashed line). Abbreviations: VTC-F0, maximal force production of the lower limbs; VTC-V0, maximal extension velocity of the lower limbs.
Figure 2 Vertical force–velocity proles of 2 elite young (under-19) soccer players (body mass for A, 78 kg, and B, 75.5 kg; push-off distance for
A, 0.26 m, and B, 0.28 m) obtained from maximal squat jumps (SJ) against additional loads of 0, 10, 20, 40, and 50 kg. One player has a force decit
(magnitude of the relative difference between the slope of the linear force–velocity relationship [Sfv] and Sfv
opt
[FV
imb
] of 72%), whereas the other has a
velocity decit (FV
imb
of 137%). Player A is a central defender and player B is a goalkeeper. Abbreviation: VTC-Pmax, maximal mechanical power output.
IJSPP Vol. 11, No. 2, 2016
270 Morin and Samozino
capabilities. Our yet-unpublished observations have shown that
such an individually optimized approach is more efcient that a
one-size-ts-all program, identical for those 2 players.
The latter example raises an important question, however, with
regard to the application of improving ballistic push-off performance
in cyclic movements such as sprint running. This particular question
is the main interest when developing forward (sprint) acceleration
and performance characteristics, for instance, in soccer or rugby
players (except for some players like goalkeepers or some specic
sport actions involving jumps), and will be discussed in the follow-
ing section detailing horizontal proling for sprint performance.
Horizontal Profiling for Sprint Performance
The inputs that must be measured to determine the horizontal
prole
6
are the athlete’s body mass and height and either distance–
time or speed–time running data. The latter can be measured using
a series of timing gates (at least 5 split times, eg, 5, 10, 20, 30,
and 40 m) or a laser or radar device (eg, ~50-Hz Stalker ATSII
radar, Applied Concepts Inc, Plano, TX). Wind speed, ambient
temperature, and pressure must also be known to accurately esti-
mate air-friction force. The entire power-force-velocity prole can
then be computed from the simple modeling of the derivation of
the speed–time curve that leads to horizontal acceleration data.
Likewise, the mechanical effectiveness of force application can be
determined via the linear relationship between ratio of force (RF)
and running velocity
11
(Figure 3). Our research has shown that, in
addition to maximal mechanical power output in the horizontal
direction (HZT-Pmax), 100-m performance was related to the ability
to apply high amounts of force in the horizontal direction (RF and
rate of decrease in RF [D
RF
] indices).
11–13
With regard to shorter
sprints (ie, acceleration-only phases, eg, up to 10–20 m in rugby
or soccer specialists), recent results have shown that the shorter
the distance considered, the higher the relationship between sprint
performance and maximal horizontal force production (HZT-F0)
(unpublished observations). Thus, in practical terms, if a training
program is designed to improve sprint-acceleration performance,
the focus should be placed on increasing HZT-Pmax by improving
its components (HZT-F0 and maximal running velocity [HZT-V0]).
This could be done by rst comparing the relative strengths and
weaknesses in each player’s prole with the rest of the team, and
then programming the training content depending on the distance
over which sprint acceleration should be optimized. As for vertical
proling, the main value of this approach is that the diagnostic and
subsequent targeted training interventions are individualized, and
frequent monitoring of program-induced changes in HZT-Pmax and
its mechanical determinants can make this program more efcient
and dynamic in terms of adaptation to individual changes over
time. In particular, since HZT-F0 and RF are paramount for short
sprint-acceleration performances, coupling the vertical proling to
the horizontal proling can help identify the determinants of HZT-
F0. Using this approach, we consider HZT-F0 to result from the
interaction of the overall strength capability of the athlete at each
lower-limb extension (as assessed by the vertical prole) and his
or her ability to transfer this overall strength level to the specic,
forward sprint motion at the rst steps (as evidenced by RFmax) or
at steps at high velocities (as evidenced by D
RF
) (Table 1, Figure 4).
In short, a high HZT-F0 can result from high VTC-Pmax and a high
quality of vertical-to-horizontal transfer (ie, good RFmax and D
RF
values), whereas a low HZT-F0 can result from a high VTC-Pmax
with a low-quality transfer (poor RFmax and D
RF
values); vice versa,
a low VTC-Pmax with a high-quality transfer (good RFmax and D
RF
values); or any possible intermediate combination.
The case report used to illustrate these points shows data from
2 players of an elite rugby union team. Figure 3 shows that the 2
players have similar 20-m times (maximal acceleration from a stand-
ing start) and HZT-Pmax values, yet with opposite F–V proles and
RF-velocity proles. Indeed, player C has higher horizontal force-
production capabilities (in the specic context of sprint push-off),
especially at the beginning of the sprint and notably due to a higher
effectiveness of ground-force application (indicated in a higher
RFmax). However, his D
RF
is more negative, meaning his higher
initial effectiveness decreases at a greater rate as speed increases
than for player D. This has likely contributed to higher velocity
Figure 3 — Horizontal force–velocity proles of 2 elite rugby union players (body mass for C, 108.8 kg, and D, 86.1 kg) obtained from maximal
30-m sprints. Both players reached their maximal running speed before the 30-m mark. Abbreviations: HZT-Pmax, maximal mechanical power output
in the horizontal direction; D
RF
, rate of decrease in ratio of force with increasing speed during sprint acceleration; HZT-F0, maximal horizontal force
production; HZT-V0, maximal running velocity.
IJSPP Vol. 11, No. 2, 2016
Power-Force-Velocity Profiles and Training 271
capabilities, which explains the higher HZT-V0 of player D. As for
ballistic push-off, we suggest that the training program designed to
improve sprint performance (eg, here 20-m time) in each of these
2 players should target different capabilities. A similar program
given to these players (which is current practice in the majority of
teams, based on our perception) will very likely result in subopti-
mal adaptations for both of them. In particular, player D’s training
should target as a priority his HZT-F0 capabilities. Here, in terms of
injury prevention, this suggests that this player could be given more
strength and horizontal strength work than others and probably less
maximal sprint velocity work. This could directly reduce the risk for
sprinting-related injuries for this player by reducing the total time
he would be exposed to high-speed running.
14–17
For this player,
compared with player C (and potentially compared with the average
value of the group/team), HZT-F0 should be developed, especially
through increasing RFmax. Adding the previously described verti-
cal proling to this horizontal proling could help better determine
whether a lower HZT-F0 is due to an overall decit of lower-limb
strength (as indicated by a low VTC-Pmax) and/or a decit in the
transfer of this strength in the specic horizontal push-off motion
(technical capability). Differences in horizontal proles have been
reported in elite rugby players according to individual player posi-
tions
18
and in young soccer players.
19
Practical Synthesis
Figure 4 shows a decision tree, with a specic focus on ballistic
push-off and sprint-acceleration performance, which are 2 major
physical determinants in many sports. This gure is designed to help
practitioners use the vertical and horizontal proling approach to
better detect the strengths and weaknesses in their athletes and design
more-effective training interventions. Vertical proling will provide
information as to what physical capabilities should be developed
to improve ballistic push-off performance and as to the maximal
levels of force and velocity of the athlete’s neuromuscular system.
Horizontal proling will provide information as to the specic sprint-
acceleration motion and as to what underlying physical or technical
feature(s) mainly limit each individual’s sprint performance.
Conclusion and Perspectives
These novel approaches of vertical and horizontal force-velocity-power
proling have the potential to provide sport practitioners simple, cheap,
yet accurate methods for more individualized monitoring and training
of physical and technical capabilities. These methods can be easily
implemented on a regular basis, since they are based on common and
sport-specic movements (ballistic push-offs and sprint accelerations),
and can therefore be used for long-term monitoring and training pro-
cesses. Furthermore, they may also be implemented in injury-prevention
and -rehabilitation processes since diagnostic information will assist in
better-designed sprint-related training programs, and clear differences
have been observed between injured and noninjured players.
20,21
The limitations of these approaches have been extensively
discussed,
5,6,22
and the main perspective stems from the fact that
these proling methods give information as to what specic muscle
outputs should be developed, not how this should be done. This
will be the next challenge that we are pleased to undertake: testing
and investigating the most-efcient practical (training) methods to
improve each mechanical determinant of performance and further
extending the current knowledge on this topic
4
using the novel
approaches presented here.
Acknowledgments
We are forever grateful for the help (and trust) of all the sports practitioners
(coaches, physiotherapists, managers, doctors, researchers, students) who have
helped us develop these approaches over the last 10 years. We also thank all
the athletes, of all levels of performance, who did, do, or will give voluntarily
and enthusiastically their best effort during testing. A special thanks goes to
our friend and colleague Pedro Jimenez-Reyes, for his dedicated work and
help in developing this approach. We gratefully thank Matt Cross and Matt
Brughelli for their careful reading and comments on the revised manuscript.
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Figure 4 — Decision tree to interpret the force-velocity-power proles in relationship with ballistic push-off (eg, jumping) and sprinting performances.
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18,19
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Supplementary resource (1)

... Vertical jump power-force-velocity profiles (P-F-v) are considered time-efficient methods to estimate the theoretical maximum force (F 0 ), velocity (v 0 ), and power (P max ) and the slope of the force-velocity relationship (S FV ). [1][2][3][4] Initially proven reliable 1 and valid, the test concept is used to detect and modify exercise-related gains or to prescribe individualized training programs based on P-F-v parameters. 5 However, recent research has questioned P-F-v profiling for methodological 6 and task familiarity considerations. ...
... The P-F-v parameters have been described as maximal mechanical muscle capacities or properties, 26 which characterize the function of the lower limbs' neuromuscular system. 3,14 This description suggests a holistic picture of maximal mechanical muscle capacities irrespective of the tested movement. However, this assumption seems speculative and a construct validation remains difficult to impossible. ...
Article
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Purpose: To evaluate the test-retest reliability of isokinetic leg-press power-force-velocity profile (P-F-v) parameters in male and female elite athletes. In addition, we determined the concurrent validity of leg-press against squat-jump (SJ) P-F-v parameters in task-experienced athletes. Methods: For test-retest reliability, 22 female and 23 male elite athletes (22.3 [4.1] y) with different sporting backgrounds conducted 3 isokinetic leg-press test sessions over 3 consecutive weeks. The testing consisted of bilateral leg extensions at isokinetic velocities of 0.1, 0.3, 0.7, and 1.2 m·s-1. For concurrent validity, 13 ski jumpers (20.3 [3.3] y) were recruited to perform the isokinetic leg-press and SJ P-F-v profile tests using 5 predefined loading conditions of 0%, +20%, +40%, +60%, and +80% of body mass. Results: Relative and absolute reliability were acceptable for female (intraclass correlation coefficient ≥.87 and coefficient of variation ≤6.5%) and male (intraclass correlation coefficient ≥.89 and coefficient of variation ≤5.7%) elite athletes. In contrast, concurrent validity was insufficient, with correlations ranging from -.26 to .69 between isokinetic and SJ P-F-v parameters. Conclusion: Irrespective of sex, isokinetic leg-press P-F-v profiles provide reliable parameters. However, leg-press P-F-v profiles do not serve as a valid substitute for SJ P-F-v profiles. P-F-v parameter magnitudes are likely dependent on the constraints of the tested movement and testing device.
... An avenue which has shown promise with regards to field-based speed testing relates to force-velocity-power (FVP) profiling of athletes [22][23][24]. The initiation of a sprint from a stationary position, as would be required in many sports, typically requires a large force application to overcome the inertia of the body and to accelerate to some sub-maximum speed [23]. ...
... An avenue which has shown promise with regards to field-based speed testing relates to force-velocity-power (FVP) profiling of athletes [22][23][24]. The initiation of a sprint from a stationary position, as would be required in many sports, typically requires a large force application to overcome the inertia of the body and to accelerate to some sub-maximum speed [23]. Intriguingly, a relatively linear relationship exists between horizontal force application and the horizontal velocity associated with it such that the theoretical maximal force (F 0 ), velocity (v 0 ), and power (P 0 ) can be accurately predicted for each athlete [25,26]. ...
Article
Force-velocity-power (FVP) profiling offers insights related to key factors that may enhance or hinder sprinting performances. Whether the same FVP principles could be applied to the sprinting portion of the 3-minute all-out test for running (3MT) has not been previously investigated. Twenty moderately trained participants volunteered for the study (age: 24.75 ± 3.58 yrs; height: 1.69±0.11 m; mass: 73.74±12.26 kg). After familiarization of all testing procedures, participants completed: (i) a 40-m all-out sprint test, and (ii) a 3MT. Theoretical maximal force and power, but not velocity, were significantly higher for the 40-m sprint test. Most FVP variables from the two tests were weakly to moderately correlated, with the exception of maximal velocity. Finally, maximal velocity and relative peak power were predictive of D’, explaining approximately 51% of the variance in D’. Although similar maximal velocities are attained during both the 40-m sprint and the 3MT, the underlying mechanisms are markedly different. The FVP parameters obtained from either test are likely not interchangeable but do provide valuable insights regarding the potential mechanisms by which D’ may be improved.
... This result is in line with previous findings of strong correlations between jump height and mechanical power in unloaded vertical jumps (Aragón-Vargas & Gross, 1995;Barker, Harry, & Mercer, 2018). Furthermore, the finding that only Pmax showed good criterion validity could also fit the original assumption made by Morin and Samozino (2016) that two athletes could have similar Pmax values, but their individual contribution of F0 and v0 could be different. Hence, individuals inherently express variable contributions of F0 and v0 in P-F-v profiles, consequently evaluating criterion validity for these parameters would be contrary to this P-F-v assumption and anyway statistically unverifiable. ...
... Hence, individuals inherently express variable contributions of F0 and v0 in P-F-v profiles, consequently evaluating criterion validity for these parameters would be contrary to this P-F-v assumption and anyway statistically unverifiable. P-F-v parameters have been frequently described as "maximal mechanical muscle capacities" (Jimenez-Reyes, Samozino, Pareja-Blanco, et al., 2017), which characterize neuromuscular system function (Morin & Samozino, 2016;Padulo et al., 2017). While this attribution of P-F-v profiling has been assumed, we could not find a concrete validation of this construct. ...
... It is likely that the two-point method is more timeefficient, as it can be performed in conjunction with the warm-up and is even suitable for assessing injuries, as differences were observed between injured and uninjured players [21]. Some limitations have been addressed [57], with an emphasis on the specificity of the tests [59]. This study focused on Paralympic Powerlifting athletes, aiming to provide more tools for this segment. ...
Article
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Background Due to the absence of evidence in the literature on Paralympic Powerlifting the present study investigated various methods to assess bench press maximum repetition and the way each method influences the measurement of minimum velocity limit (MVT), load at zero velocity (LD0), and force–velocity (FV). Objective To evaluate the precision of the multi-point method using proximal loads (40, 50, 60, 70, 80, and 90% of one repetition maximum; 1RM) compared to the four-point method (50, 60, 70, and 80% of 1RM) and the two-point method using distant loads (40 and 80% and 50 and 80% of 1RM) in in the MVT, LD0, and FV, in bench press performed by Paralympic Powerlifters (PP). Methods To accomplish this, 15 male elite PP athletes participated in the study (age: 27.7 ± 5.7 years; BM: 74.0 ± 19.5 kg). All participants performed an adapted bench press test (free weight) with 6 loads (40, 50, 60, 70, 80, and 90% 1RM), 4 loads (50, 60, 70, and 80% 1RM), and 2 loads (40–80% and 50–80% 1RM). The 1RM predictions were made by MVT, LD0, and FV. Results The main results indicated that the multiple (4 and 6) pointsmethod provides good results in the MVT (R ² = 0.482), the LD0 (R ² = 0.614), and the FV (R ² = 0.508). The two-point method (50–80%) showed a higher mean in MVT [1268.2 ± 502.0 N; ICC95% 0.76 (0.31–0.92)], in LD0 [1504.1 ± 597.3 N; 0.63 (0.17–0.86)], and in FV [1479.2 ± 636.0 N; 0.60 (0.10–0.86)]. Conclusion The multiple-point method (4 and 6 points) and the two-point method (40–80%) using the MVT, LD0, and FV all showed a good ability to predict bench press 1RM in PP.
... However, inter-individual differences in force-velocity profiles certainly exist within athletic populations, and are important for specific training prescription (Morin and Samozino, 2016). Our framework could potentially supplement existing forcevelocity profiling methods by determining whether athletes are performing up to their F 0 or a 0 capabilities. ...
Article
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Sprinting performance is critical for a variety of sports and competitive activities. Prior research has demonstrated correlations between the limits of initial acceleration and maximum velocity for athletes of different sprinting abilities. Our perspective is that hip torque is a mechanistic link between these performance limits. A theoretical framework is presented here that provides estimates of sprint acceleration capability based on thigh angular acceleration and hip torque during the swing phase while running at maximum velocity. Performance limits were calculated using basic anthropometric values (body mass and leg length) and maximum velocity kinematic values (contact time, thigh range of motion, and stride frequency) from previously published sprint data. The proposed framework provides a mechanistic link between maximum acceleration and maximum velocity, and also explains why time constant values (τ, ratio of the velocity limit to acceleration limit) for sprint performance curves are generally close to one-second even for athletes with vastly different sprinting abilities. This perspective suggests that specific training protocols targeted to improve thigh angular acceleration and hip torque capability will benefit both acceleration and maximum velocity phases of a sprint.
Thesis
The objectives of this thesis were to investigate the performance determinants of trail running, and to evaluate the changes in running economy following prolonged endurance running exercise. First, we tested elite road and trail runners for differences in performance factors. Our results showed that elite trail runners are stronger than road runners, but they have greater cost of running when running on flat ground. In the second study, we evaluated the performance factors that predicted performance in trail running races of different distances, ranging from 40 to 170 km. We found that maximal aerobic capacity was a determinant factor of performance for races up to 100 km. Performance in shorter races, up to approximately 55 km, was also predicted by lipid utilization at slow speed, while performance in the 100 km race was also predicted by maximal strength and body fat percentage. The most important factors of performance for races longer than 100 km are still debated. We also tested the effects of trail running race distance on cost of locomotion, finding that cost of running increased after races up to 55 km, but not after races of 100-170 km. Finally, we tested the. effects of two different exercise modalities, cycling and running, on cost of locomotion, after 3 hours of intensity-matched exercise. Cost of locomotion increased more following cycling than running, and the change in cost of locomotion was related to changes in cadence and loss of force production capacity.
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Short sprint performance is one of the most distinguishable and admired physical traits in sports. Short sprints have been modeled using the mono-exponential equation that involves two parameters: (1) maximum sprinting speed (MSS) and (2) relative acceleration (TAU). The most common methods to assess short sprint performance are with a radar gun or timing gates. In this paper, we: 1) provide the shorts package that can model sprint timing data from these two sources; 2) discuss potential issues with assessing sprint time (synchronization and flying start, respectively); and 3) provide model definitions within the shorts package to help alleviate errors within the subsequent parameter outcomes.
Research
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The subject of study on which this work focuses is related to high-performance track and field, specifically sprinting. The aim is to analyse a weakness in the entity where the university internships have been carried out in order to be able to implement proposals for improvement that optimise, in this case, sport performance. To do this, a group of sprint training belonging to the Club Escuela Atletismo Majadahonda were analysed, using various techniques such as direct observation, monitoring of training sessions or informal conversations with the coach and athletes. The main weakness observed was a high training load in the form of plyometric and strength work, of which the magnitude of metabolic and mechanical stress they can cause is unknown. These aspects are of great interest, as the existence of a relationship between jumping capacity, strength production and performance in high-intensity sprints is being considered. In order to establish a proposal for improvement, a broad theoretical framework will be described in which the determining aspects of the training process on sprinting performance will be presented. Finally, a series of practical tests with their respective technological tools are presented. In addition, recommendations are established in terms of the periodisation of the tests, the programming of the protocol and of these tests throughout the season. Once the effects of the training load are known with greater accuracy, a strength training programming proposal is described, prioritising certain exercises with a greater transfer to sprint performance. By way of conclusion, we reflect on the traditional training programmes with high fatiguing volumes, dismantling this old conception through evidence of the same effects with a reduction in training.
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Background: Rugby union is a physically demanding collision sport that requires optimal neuromuscular functionfor maximal power output, with mechanical power an integral component of performance. Peak power (Pp) andrelative Pp are parameters of neuromuscular function commonly assessed through the countermovement jump(CMJ) as a measure of fatigue. The Wattbike cycle ergometer test (CET) is a non-load bearing method of evaluating lower limb power. The cost-effective CET could therefore offer a viable alternative to the CMJ. Objectives: This study aimed to determine the concurrent validity of the CMJ and CET. Methods: Thirty-eight professional rugby union players performed twelve CMJs on a force platform with four loads(bodyweight: BW-CMJ; 20kg: 20-CMJ; 40kg: 40-CMJ and 60kg: 60-CMJ) and a six second peak power (6PPO) CET assessment on a Wattbike ergometer. Results: CMJ power outputs were [BW-CMJ: Pp - 3101±648 W; 20-CMJ: Pp - 2724±513 W; 40-CMJ: Pp - 2490±496 W; 60-CMJ: Pp - 2238±366 W] and CET [Pp – 1310±161 W]. None of the CMJ-Pp values showed relationships with any CET power variables. Large (r = 0.51-0.63; p = 0.000 – 0.001) relationshipswere found to be between relative CMJ and relative CET power outputs. Bland-Altman plots, which were used todetermine the level of agreement between the two assessments, showed the agreement between the tests waspoor. Conclusion: Though positive relationships existed between relative CMJ and relative CET power variables, analyses of the level of agreement in the Bland-Altman plots suggest that the two power assessment methods are not interchangeable measures of power.
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Very little is currently known about the effects of acute hamstring injury on over-ground sprinting mechanics. The aim of this research was to describe changes in power-force-velocity properties of sprinting in two injury case studies related to hamstring strain management: Case 1: during a repeated sprint task (10 sprints of 40 m) when an injury occurred (5th sprint) in a professional rugby player; and Case 2: prior to (8 days) and after (33 days) an acute hamstring injury in a professional soccer player. A sports radar system was used to measure instantaneous velocity-time data, from which individual mechanical profiles were derived using a recently validated method based on a macroscopic biomechanical model. Variables of interest included: maximum theoretical velocity (V0) and horizontal force (FH0), slope of the force-velocity (F-v) relationship, maximal power, and split times over 5 and 20 m. For Case 1, during the injury sprint (sprint 5), there was a clear change in the F-v profile with a 14% greater value of FH0 (7.6-8.7 N/kg) and a 6% decrease in V0 (10.1 to 9.5 m/s). For Case 2, at return to sport, the F-v profile clearly changed with a 20.5% lower value of FH0 (8.3 vs. 6.6 N/kg) and no change in V0. The results suggest that the capability to produce horizontal force at low speed (FH0) (i.e. first metres of the acceleration phase) is altered both before and after return to sport from a hamstring injury in these two elite athletes with little or no change of maximal velocity capabilities (V0), as evidenced in on-field conditions. Practitioners should consider regularly monitoring horizontal force production during sprint running both from a performance and injury prevention perspective.
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The objective of this study was to characterize the mechanics of maximal running sprint acceleration in high-level athletes. Four elite (100-m best time 9.95–10.29 s) and five sub-elite (10.40–10.60 s) sprinters performed seven sprints in overground conditions. A single virtual 40-m sprint was reconstructed and kinetics parameters were calculated for each step using a force platform system and video analyses. Anteroposterior force (FY), power (PY), and the ratio of the horizontal force component to the resultant (total) force (RF, which reflects the orientation of the resultant ground reaction force for each support phase) were computed as a function of velocity (V). FY-V, RF-V, and PY-V relationships were well described by significant linear (mean R2 of 0.892 ± 0.049 and 0.950 ± 0.023) and quadratic (mean R2 = 0.732 ± 0.114) models, respectively. The current study allows a better understanding of the mechanics of the sprint acceleration notably by modeling the relationships between the forward velocity and the main mechanical key variables of the sprint. As these findings partly concern world-class sprinters tested in overground conditions, they give new insights into some aspects of the biomechanical limits of human locomotion.
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The purpose of this investigation was to analyze the concurrent validity and reliability of an iPhone app (called: My Jump) for measuring vertical jump performance. Twenty recreationally-active healthy men (age: 22.1 ± 3.6 years) completed five maximal countermovement jumps (CMJ), which were evaluated using a force platform (time in the air –TIA– method) and a specially designed iPhone app. My jump was developed to calculate the jump height from flight time using the high-speed video recording facility on the iPhone 5s. Jump heights of the 100 jumps measured, for both devices, were compared using the intraclass correlation coefficient (ICC), Pearson product-moment correlation coefficient (r), Cronbach’s alpha (α), coefficient of variation (CV) and Bland-Altman plots. There was an almost perfect agreement between the force platform and My Jump for the CMJ height (ICC = 0.997, p < 0.001; Bland-Altman bias= 1.1 ±0.5cm, p < 0.001). In comparison with the force platform, My Jump showed good validity for the CMJ height (r = 0.995, p < 0.001). The results of the present study show that CMJ height can be easily, accurately, and reliably evaluated using a specially developed iPhone 5s app.
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The aim of the present study was to examine, in highly trained young soccer players, the mechanical horizontal determinants of acceleration (Acc) and maximal sprinting speed (MSS). Eighty-six players (14.1 ± 2.4 year) performed a 40-m sprint to assess Acc and MSS. Speed was measured with a 100-Hz radar, and theoretical maximal velocity (V 0), horizontal force (F 0) and horizontal power (P max) were calculated. Within each age group, players were classified as high Acc/fast MSS (>2% faster than group mean), medium (between −2% and +2%), and low/slow (>2% slower). Acc and MSS were very largely correlated (−0.79; 90% confidence limit [−0.85; −0.71]). The determinants (multiple regression r 2 = 0.84 [0.78; 0.89]) of Acc were V 0 (partial r: 0.80 [0.72; 0.86]) and F 0 (0.57 [0.44; 0.68]); those of MSS (r 2 = 0.96 [0.94; 0.97]) were V 0 (0.96 [0.94; 0.97]) and P max (0.73 [0.63; −0.80]). High/Med have likely greater F 0 (Cohen’s d: +0.8 [0.0; 1.5]), V 0 (+0.6 [−0.1; 1.3]) and P max (+0.9 [0.2; 1.7]) than Low/Med. High/Fast have an almost certainly faster V 0 (+2.1 [1.5; 2.7]) and a likely greater P max (+0.6 [−0.1; 1.3]) than High/Med, with no clear differences in F 0 (−0.0 [−0.7; 0.6]). Speed may be a generic quality, but the mechanical horizontal determinants of Acc and MSS differ. While maximal speed training may improve both Acc and MSS, improving horizontal force production capability may be efficient to enhance sprinting performance over short distances.
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The International Association of Athletics Federation has systematically surveyed all Athletics injuries in their competitions since 2007 in order to develop strategies for health protection of their athletes. Analysis of frequency and characteristics of injuries during 13 international Athletics championships from 2007 to 2012 regarding different types of championships and discipline categories. The team physicians and the Local Organizing Committee reported daily all injuries on a standardised injury report form during each championship. A total of 1470 injuries were reported, equivalent to 81.1±4.2 injuries per 1000 registrations of which 36.7±2.9 were expected to result in absence from sports. The incidence of time-loss injuries was significantly higher in competition (29.0±2.6) than in training (5.8±1.9), and in outdoor (46.4±4.0) than in indoor (23.7±6.2) or youth/junior championships (13.2±4.0). While most in-competition time-loss injuries were reported during short distance events (32.5%), combined events had the highest incidence of in-competition time-loss injuries (106±26.5). The most frequent diagnosis was thigh strain (28.2%), followed by lower leg strain and ankle sprain. Injury location varied between different discipline categories: in long distances the lower leg, in Marathon the foot and in throws the upper extremity were mainly affected. The incidence of injuries varied substantially between different types of Athletics championships and between discipline categories. Special attention should be paid to combined events, running disciplines and (thigh) strain to better understand the injury mechanisms and risk factors and develop related preventive measures.
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The objectives of this study were to examine the consequences of an acute hamstring injury on performance and mechanical properties of sprint-running at the time of returning to sports and after the subsequent ~2 months of regular soccer training after return. 28 semi-professional male soccer players, 14 with a recent history of unilateral hamstring injury and 14 without prior injury, participated in the study. All players performed two 50-m maximal sprints when cleared to return to play (Test 1), and 11 injured players performed the same sprint test about 2 months after returning to play (Test 2). Sprint performance (i. e., speed) was measured via a radar gun and used to derive linear horizontal force-velocity relationships from which the following variables obtained: theoretical maximal velocity (V 0 ), horizontal force (F H0 ) and horizontal power (Pmax). Upon returning to sports the injured players were moderately slower compared to the uninjured players. F H0 and Pmax were also substantially lower in the injured players. At Test 2, the injured players showed a very likely increase in F H0 and Pmax concomitant with improvements in early acceleration performance. Practitioners should consider assessing and training horizontal force production during sprint running after acute hamstring injuries in soccer players before they return to sports. Open Access: https://www.thieme-connect.de/products/ejournals/html/10.1055/s-0033-1363192
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My Jump Health and Fitness iOS 7.0 or later; Optimised for iPhone 5, iPhone 6 and iPhone 6 Plus. Compatible with iPhone, iPad and iPod touch. $A7.49 Current version is V.2.1 which has iPhone 6 and iPhone 6 Plus support, and iOS8 support. No trial version is available. Vertical jump is a widely used measure of functional performance in athletic and non-athletic populations.1 My Jump is a low-cost, easy-to-use application which integrates with the video camera to assess vertical jump performance (figure 1). The in-app settings allow slow-motion playback for easy identification of the video frame in which jump take-off and landing occurs. The app determines the number of …
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This study aimed to validate a simple field method for determining force- and power-velocity relationships and mechanical effectiveness of force application during sprint running. The proposed method, based on an inverse dynamic approach applied to the body center of mass, estimates the step-averaged ground reaction forces in runner's sagittal plane of motion during overground sprint acceleration from only anthropometric and spatiotemporal data. Force- and power-velocity relationships, the associated variables, and mechanical effectiveness were determined (a) on nine sprinters using both the proposed method and force plate measurements and (b) on six other sprinters using the proposed method during several consecutive trials to assess the inter-trial reliability. The low bias (<5%) and narrow limits of agreement between both methods for maximal horizontal force (638 ± 84 N), velocity (10.5 ± 0.74 m/s), and power output (1680 ± 280 W); for the slope of the force-velocity relationships; and for the mechanical effectiveness of force application showed high concurrent validity of the proposed method. The low standard errors of measurements between trials (<5%) highlighted the high reliability of the method. These findings support the validity of the proposed simple method, convenient for field use, to determine power, force, velocity properties, and mechanical effectiveness in sprint running. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Article
Data regarding direct athletic muscle injuries (caused by a direct blunt or sharp external force) compared to indirect ones (without the influence of a direct external trauma) are missing in the current literature-this distinction has clinical implications. To compare incidence, duration of absence and characteristics of indirect and direct anterior (quadriceps) and posterior thigh (hamstring) muscle injuries. 30 football teams and 1981 players were followed prospectively from 2001 until 2013. The team medical staff recorded individual player exposure and time-loss injuries. Muscle injuries were defined as indirect or direct according to their injury mechanism. In total, 2287 thigh muscle injuries were found, representing 25% of all injuries. Two thousand and three were valid for further analysis, of which 88% were indirect and 12% direct. The incidence was eight times higher for indirect injuries (1.48/1000 h) compared to direct muscle injuries (0.19/1000 h) (p<0.01). Indirect muscle injuries caused 19% of total absence, and direct injuries 1%. The mean lay-off time for indirect injuries amounted to 18.5 days and differed significantly from direct injuries which accounted for 7 days (p<0.001). 60% of indirect injuries and 76% of direct injuries occurred in match situations. Foul play was involved in 7% of all thigh muscle injuries, as well as in 2% of indirect injuries and 42% of direct injuries. Muscle anterior and posterior thigh injuries in elite football are more frequent than have been previously described. Direct injuries causing time loss are less frequent than indirect ones, and players can usually return to full activity in under half the average time for an indirect injury. Foul play is involved in 7.5% of all thigh muscle injuries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.