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Velocity-based training (VBT) is a contemporary method of resistance training that enables accurate and objective prescription of resistance training intensities and volumes. This review provides an applied framework for the theory and application of VBT. Specifically, this review gives detail on how to: use velocity to provide objective feedback, estimate strength, develop load-velocity profiles for accurate load prescription, and how to use statistics to monitor velocity. Furthermore, a discussion on the use of velocity loss thresholds, different methods of VBT prescription, and how VBT can be implemented within traditional programming models and microcycles is provided.
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Velocity-Based Training:
From Theory to
Application
Jonathon Weakley, PhD,
1,2
Bryan Mann, PhD,
3
Harry Banyard, PhD,
4
Shaun McLaren, PhD,
2,5
Tannath Scott, PhD,
2,6
and Amador Garcia-Ramos, PhD
7,8
1
School of Behavioural and Health Sciences, Australian Campus University, Brisbane, Queensland, AustraliaAU1 ;
2
Carnegie Applied Rugby Research (CARR) Centre, Institute for Sport, Physical Activity and Leisure, Leeds Beckett
University, Leeds, West Yorkshire, United Kingdom
AU2 ;
3
Department of Kinesiology and Sport Sciences, University of
Miami, Miami, Florida;
4
Department of Health and Medical Sciences, Swinburne University of Technology, Melbourne,
Australia;
5
England Performance Unit, The Rugby Football League, Leeds, West Yorkshire, United Kingdom;
6
School
of Science and Technology, University of New England, Armidale, Australia;
7
Department of Sports Sciences and
Physical Conditioning, Faculty of Education, Universidad Catolica de la Santisima Concepcion, Concepcio
´n, Chile;
and
8
Department of Physical Education and Sport, Faculty of Sport Sciences, University of Granada, Granada, Spain
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided
in the HTML and PDF versions of this article on the journal’s Web site (http://journals.lww.com/nsca-scj).
ABSTRACT
Velocity-based training (VBT) is a con-
temporary method of resistance training
that enables accurate and objective
prescription of resistance training inten-
sities and volumes. This review provides
an applied framework for the theory and
application of VBT. Specifically, this
review gives detail on how to: use
velocity to provide objective feedback,
estimate strength, develop load-velocity
profiles for accurate load prescription,
and how to use statistics to monitor
velocity. Furthermore, a discussion on
the use of velocity loss thresholds, dif-
ferent methods of VBT prescription, and
how VBT can be implemented within
traditional programming models and mi-
crocycles is provided.
INTRODUCTION
Athletes perform resistance train-
ing to develop strength, power,
and lean body mass (81,82). To
achieve this, coaches typically prescribe
specific resistance training loads relative
to an individual’s maximal ability (e.g.,
70% of one repetition maximum
[1RM]) (35,95). In addition, athletes
are commonly assigned a specified num-
ber of sets and repetitions to complete
(e.g., 5 sets of 10 repetitions) based on the
desired training goal (9). However, using
an athlete’s previous maximal ability to
prescribe training loads can be problem-
atic if the athlete’s 1RM changes as a con-
sequence of training because the
prescribed load may not match the %
of 1RM intended for the particular ses-
sion. In addition, it is known that the
number of repetitions that can be per-
formed with a given % of 1RM differs
between athletes and, therefore, assign-
ing the same number of sets and repeti-
tions for all athletes may induce different
levels of effort and fatigue (72,88). There-
fore, alternative methods such as
velocity-based training (VBT) have been
developed to provide accurate and
objectivedatatosupporttheprescription
of resistance training (7–9).
WHAT IS VELOCITY-BASED
TRAINING?
VBT is a term that covers a wide array
of training topics and approaches. The
integration of VBT lies on a continuum
and can be used with varying emphasis
(F1Figure 1). At its most basic level,
velocity can be used as an accessory
to traditional percentage-based train-
ing. For example, visual or verbal feed-
back of velocity can be provided to
athletes to enhance performance and
improve motivation and competitive-
ness (1,90,91,93,96). Alternatively,
VBT can be implemented across all
facets of a resistance training program-
ming and support the prescription of
load, sets, number of repetitions, and
the programming method applied
(9,20,49,95). For this reason, VBT
should be defined as a method that
“uses velocity to inform or enhance
training practice.” This definition ac-
counts for the broad implementation
of training methods that use velocity
and assist the practitioner in achieving
their training goals.
Address correspondence to Dr. Jonathon
Weakley, Jonathon.weakley@acu.edu.au.
KEY WORDS:
VBT; 1RM prediction; load-velocity pro-
file; periodization; fatigue; statistics
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WHY VELOCITY?
Velocity is commonly used over other
kinetic or kinematic outputs (e.g.,
power) when resistance training for 3
reasons. First, it is well established that
as an external mass is increased, reduc-
tions in lifting velocity occur (45). This
loss of velocity continues until a 1RM
load is achieved which corresponds
with the minimum/terminal velocity
threshold (V1RM) (45). Second, there
is a nearly perfect linear relationship
between velocity and intensity as a per-
centage of maximum ability (i.e., % of
1RM). This has been demonstrated con-
sistently across a range of exercises and
submaximal loads (13,27). Third, a com-
mon element to many definitions of
exercise-induced fatigue is that as fatigue
increases, there is a transient decline in
muscle fiber shortening speeds, relaxa-
tion times, and force-generating capac-
ity that cause subsequent reductions in
voluntary exercise velocity (33,74). Put
simply, as fatigue accrues, exercise veloc-
ity decreases. By acknowledging these
fundamental concepts, practitioners
can use velocity outputs to accurately
and objectively prescribe external loads
and training volumes for each session,
irrespective of fluctuations in fatigue and
athlete readiness.
USING VELOCITY TO PROVIDE
FEEDBACK AND ENHANCE
PERFORMANCE
The use of feedback during resistance
training is a powerful tool for acute per-
formance enhancement and adaptation.
Although feedback can occur in many
forms, visual and verbal feedback of bar-
bell velocities have received the most
investigation (1,50,59,92,93,96,98). It
has been demonstrated that these forms
of feedback can cause improvements in
performance in male (96) and female
(93), adults (92) and adolescents
(93,96), and professional (1,59) and
nonprofessional (50) athletes. Not only
do these improvements occur instanta-
neously during training (93,96) but
also when feedback is supplied and
then removed, performance returns
to baseline levels (50). These changes
in performance have been found to
occur alongside improvements in
psychological characteristics, with
increases in motivation and compet-
itiveness being demonstrated when
feedback of velocity performance is
provided (92,93,96–98).
Although feedback of velocity can eas-
ily be provided within the training rou-
tine, the frequency, method of delivery,
and personality of the athlete should
be considered. Recent research (59)
has demonstrated that different modes
of feedback delivery influence perfor-
mance adaptations. Nagata et al. (59)
has shown immediate improvements
and greater long-term physical devel-
opment of loaded jump ability when
verbal feedback of barbell velocity is
supplied after each repetition. This
was compared with the provision of
average set velocity or a visual record-
ing of the set. Furthermore, it is
acknowledged that athletes may have
a preference of whether they are visu-
ally or verbally informed of their per-
formance outcomes (92). These
differences may be due to intrinsic or
extrinsic motivating factors (i.e., com-
petition within or between athletes)
and levels of athlete conscientiousness
(92). However, it should be noted that
in athletes with low levels of
conscientiousness, verbally encourag-
ing statements after each repetition
may provide the greatest benefit (92).
Finally, the chronic delivery of feed-
back during training is known to be
of substantial benefit. Over a 6-week
period, Randell et al. (71) provided
either feedback or no-feedback at the
completion of each repetition of the
jump squat and observed small to mod-
erately greater improvements in stand-
ing broad jump (effect size [ES] 5
0.28) and 30 m sprint performance
(ES 50.46). In addition, recent
research by Weakley et al. (90) has
highlighted greater improvements in
10- and 20-m sprint performance (ES
50.69 and 0.71, respectively), jump
height (ES 50.21), and 3RM squat
and bench press strength (ES 50.28
and 0.21, respectively) when feedback
is provided after each repetition of each
exercise across a 4-week mesocycle.
Also, of interest for the strength and
conditioning practitioner, was that this
study emphasized the benefit of pro-
viding feedback of performance when
performing sprint drills (Table 1) AU5.
Sprint times and T1average velocity
across a known distance can easily be
conveyed to athletes and are believed
to promote similar improvements in
motivation and feelings of competitive-
ness within and between athletes as
feedback during resistance train-
ing (90).
THE DIFFERENT TYPE OF
VELOCITY VARIABLES AND WHEN
TO USE THEM
The 2 velocity variables most com-
monly used in practice and scientific
research are mean velocity (MV) (i.e.,
the average velocity across the entire
concentric phase) and peak velocity
(PV) (i.e., the maximum instantaneous
velocity reached during the concentric
phase) (68,83). However, mean propul-
sive velocity (MPV) (i.e., the average
velocity from the start of the concen-
tric phase until the acceleration is less
than gravity [29.81 m$s
22
]) has also
been proposed as an alternative (77).
The difference between the MPV and
MV is that the latter does not account
for the braking phase of the movement.
Figure 1. Velocity-based training continuum highlighting the varying emphasis on
velocity within a training program.
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However, it is our opinion that MV and
PV provide more valuable information
for strength and conditioning practi-
tioners for both testing and training
purposes.
MONITORING VELOCITY DURING
TESTING
Neuromuscular function can be as-
sessed by measuring the velocity value
achieved against a given load using tra-
ditional (e.g., bench press or squat) or
ballistic (e.g., bench press throw or ver-
tical jump) exercises (15,66). When
testing with light/moderate loads
(#70% 1RM), it is recommended that
ballistic exercises are used (e.g., bench
press throw rather than the traditional
bench press variant). This removes the
braking portion of the concentric
movement and can provide greater
reliability of velocity outcomes
(61,66). However, using MV and
MPV to measure ballistic performance
is problematic because these metrics
include the flight phase. Furthermore,
MPV values could be even more prob-
lematic due to difficulties in detecting
the exact moment take-off occurs. This
issue may explain counterintuitive find-
ings reported in the scientific literature
such as the power developed in a tradi-
tional exercise (e.g., bench press) being
greater than its ballistic variant (e.g.,
bench press throw) (46). Conse-
quently, we recommend the use of
PV for the testing of ballistic exercises.
On the other hand, nonballistic var-
iants of exercises are advised for testing
heavier loads (.70% 1RM), with MV
and MPV providing virtually the same
information (28,32,76). Therefore,
when testing “heavy” (.70% 1RM),
nonballistic exercises, all velocity vari-
ables could be equally valid.
MONITORING VELOCITY DURING
TRAINING
Although velocity can be used in many
ways during training, 3 important ap-
plications are (I) estimating the 1RM,
(II) prescribing the volume and relative
intensity of the training session based
off the magnitude of velocity loss, and
(III) increasing motivation and com-
petitiveness through the provision of
real-time velocity feedback. Presum-
ably, all 3 velocity variables could be
equally valid to fulfill the applications
of points II and III. However, we rec-
ommend the use of MV to estimate the
1RM because of its greater reliability
(when compared with MPV) when
lifting light relative loads (23,67). The
advantage of MV over PV is that the
former varies less between different de-
vices designed to measure movement
velocity (22,30), the relationship
between load and velocity is more lin-
ear using MV (31), and that between-
subject variability in the velocity at-
tained during 1RM attempts may
be lower.
ONE REPETITION MAXIMUM
PREDICTION METHODS
One interesting application of VBT is
the possibility of estimating 1RM
strength from the velocity recorded
against submaximal loads. General
load-velocity (L-V) relationships (36)
and individual L-V relationships (52)
have previously been proposed to
estimate the 1RM. The general L-V rela-
tionship was introduced by Gonza
´lez-
Badillo and Sa
´nchez-Medina (36) AU6who
used a second-order polynomial regres-
sion equation to estimate the %1RM dur-
ing the bench press exercise. After this
seminal work, similar equations have
been proposed in other resistance train-
ing exercises (3,5,13,28,30,31,54,65,75).
Although general L-V relationship equa-
tions enable a quick estimation of the
1RM from the MV recorded during a sin-
gle repetition, coaches should be aware
of several limitations that may limit their
use in practice. Briefly, the relationship
between the MV recorded during a single
repetition and the %1RM may be influ-
enced by the type of exercise (e.g., squat
versus leg press) (13,38,75), execution
technique (e.g., concentric-only vs.
eccentric-concentric) (28,65), sex (higher
values for men at lower %1RM) (3,84),
and measurement device (4,22,26,91). Of
even more importance could be that the
MV-%1RM relationship, especially at
light relative loads, is subject-specific
(70). Finally, from a statistical point of
view, another problem of the general
Table 1
Feedback variables and their effects on acute-training performance
Variable
Frequency Frequency after each repetition has been shown to have greater effects than after each set
(59)
Quantitative vs. qualitative Quantitative feedback of velocity enhances performance greater than observing video
recording of previous exercise (59).
Conscientiousness Athletes with low levels of conscientiousness have the greatest improvements in kinematic
outputs when verbal encouragement is supplied (92).
Motivation and competitiveness When visual feedback of kinematic outputs are supplied, improvements are observed in both
males and females (92,93,96–98)
Intrinsically vs. extrinsically
motivated athletes
Intrinsically motivated athletes may prefer visual feedback, while extrinsically motivated may
prefer to hear feedback (92).
Encouragement Verbally encouraging statements can enhance barbell velocity and power output (92).
Applying Velocity-Based Training
VOLUME 00 | NUMBER 00 | MONTH 2020
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Copyright © National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
L-V relationships is an overestimation of
the data fit because of the presence of
autocorrelation because authors included
more than one observation from the
same participant to calculate the general
L-V relationships (60).
The individual L-V relationship was pro-
posed to overcome the limitations high-
lighted above. The standard test used to
determine the individual L-V relation-
ship consists of recording MV against
multiple submaximal loads (z5 loads)
and, subsequently, modeling the L-V
relationship through a linear regression
to estimate the 1RM as the load associ-
ated with the MV of the 1RM (V1RM)
(6,73) (T2 Table 2). The biggest challenge
associated with individualized L-V pro-
filing is the selection of the V1RM used
to predict the 1RM. Previous studies
have used the individual V1RM (6,73)
or mean V1RM for all subjects (24).
However, because of the low reliability
of the individual V1RM (6,29,73), and
the trivial differences between the
between- and within-subject variability
for the V1RM (70), the use of a general
V1RM for all subjects could be recom-
mended to simplify the testing proce-
dure. The V1RM reported in the
scientific literature for commonly used
resistance training exercises is provided
in Table 2. It is also possible that using
the individual V1RM would provide
a more accurate estimation of the 1RM
compared with using a general V1RM.
However, this assumption needs to be
supported with experimental data. To
date, no study has compared the preci-
sion in the estimation of the 1RM when
using the individual and general V1RM.
Since the individual L-V relationship is
highly linear (6,47,73), a solution to
reduce the duration of the testing pro-
cedure could be to determine the indi-
vidual L-V relationship from the MV
recorded against only 2 loads (i.e., 2-
point method) (24,25). This has been
demonstrated by Garcı
´a-Ramos et al.
(24) who have shown that the individual
L-Vrelationshi p modeled through th e 2-
point method provides a more accurate
Table 2
Minimum velocity threshold for commonly used resistance training exercises
Exercise Study Sample 1RM MV (mean 6SD) V1RM
Bench press Gonza
´lez-Badillo and Sa
´nchez-Medina
a
(32)
Sa
´nchez-Medina
a
et al. (75)
Garcı
´a-Ramos
a
et al. (27)
Helms et al. (38)
120 young healthy males
75 athletes
30 healthy males
15 powerlifters
0.16 60.04 m/s
0.17 60.04 m/s
0.17 60.03 m/s
0.10 60.04 m/s
0.17 m/s
Prone bench
pull
Loturco et al. (54)
Sa
´nchez-Medina
a
et al. (75)
Garcı
´a-Ramos et al. (30)
30 athletes
75 athletes
26 athletes
0.51 60.07 m/s
0.52 60.06 m/s
0.48 60.04 m/s
0.50 m/s
Prone pull-up Sa
´nchez-Moreno et al. (78)
Mun
˜oz-Lopez et al. (58)
52 firefighter candidates
82 resistance-trained males
0.20 60.05 m/s
0.26 60.05 m/s
0.23 m/s
Seated military
press
Balsalobre-Ferna
´ndez
a
et al. (3)
Garcı
´a-Ramos
a
et al. (29)
39 resistance trained
participants
24 healthy participants
0.19 60.05 m/s
0.20 60.05 m/s
0.19 m/s
Lat pulldown Perez-Castilla et al. (69) 23 healthy participants 0.47 60.04 m/s 0.47 m/s
Seated cable row Perez-Castilla et al. (69) 23 healthy participants 0.40 60.05 m/s 0.40 m/s
Squat Conceic¸a
˜o
a
et al. (13)
Sa
´nchez-Medina and
a
Gonza
´lez-Badillo
(74)
Banyard et al. (6)
Helms et al. (38)
15 male athletes
80 strength-trained males
17 strength-trained males
15 powerlifters
0.32 60.04 m/s
0.32 60.03 m/s
0.24 60.06 m/s
0.23 60.05 m/s
0.30 m/s
Deadlift Ruf et al. (73)
Helms et al. (38)
Lake et al. (50)
11 resistance-trained athletes
15 powerlifters
12 active males
Not stated
0.14 60.05 m/s
0.16 60.05 m/s
0.15 m/s
Hip-thrust de Hoyo et al. (20) 102 sport science students 0.25 60.03 m/s 0.25 m/s
Leg press Conceic¸a
˜o et al. (13) 15 male athletes 0.21 60.04 m/s 0.21 m/s
a
Smith machine variation of the exercise.
1RM 5one repetition maximum; MV 5mean velocity; V1RM 5velocity at 1RM.
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estimation of the bench press 1RM per-
formed in a Smith machine than previ-
ously published general L-V
relationships. Furthermore, provided
that 2 distant loads are used (e.g.,
approximately 45% 1RM and 85%
1RM), the addition of intermediate
loads does not significantly improve
the precision in the estimation of the
1RM (69). The validity of the 2-point
methodhasalsobeenconrmedfor
upper-body free-weight exercises (e.g.,
bench pull (31) and bench press (48))
and also during the lat pull-down and
seated cable row exercises (69), but its
validity has never been explored during
lower-body exercises. Therefore,
coaches are encouraged to use the 2-
point method as an accurate, quick,
and relatively fatigue-free method to
estimate the 1RM during upper-body
exercises. This can be performed in 3
simple steps: (I) setting of the exercise-
specific V1RM (found within Table 2),
(II) recording of the MV against a light
(z45% 1RM) and a heavy load (z85%
1RM), and (III) modelling of the indi-
vidual load-velocity relationship and
determining the 1RM as the load asso-
ciated with the V1RM. However,
coaches should be aware that the
accuracy of the 2-point method and
other velocity-based 1RM prediction
methods is expected to be compromised
during free-weight lower-body exercises
(6,43,44,52). Therefore, although the
recommendations provided in this sec-
tion can be followed to obtain an accu-
rate estimation of the 1RM during some
upper-body exercises, it should be noted
that the available scientific evidence in-
dicates that velocity recordings cannot
be used to obtain an accurate estimation
of the 1RM during lower-body exercises
such as the squat or deadlift. It is
hypothesized that discrepancies in the
accuracy of prediction may be due to
the greater technical complexity of
lower-body exercises (e.g., squat or
deadlift) compared with upper-body ex-
ercises (e.g., bench press or bench pull).
Finally, it should also be noted that the
direct measurement of the 1RM is more
reliable than the estimation from the L-
V relationship (24).
DEVELOPING A LOAD-VELOCITY
PROFILE FOR THE PRESCRIPTION
OF MEAN SET VELOCITIES
A key aspect of training with L-V pro-
files is for a coach to differentiate
between normal variation in velocity
across training sessions and legitimate
fluctuations in velocity that occur from
training-induced adaptation. This is crit-
ical, so that decisions regarding training
load modification can be made with
a high degree of accuracy. Recent stud-
ies have shown that the L-V relationship
is stable when using MV, PV, or MPV in
the free-weight back squat and Smith
machine bench press (8,27). In terms of
meaningful changes in velocity, the
smallest detectable difference in MV,
PV, and MPV for the free-weight back
squat has been reported to be 60.06–
0.08 m$s
21
,60.11–0.19 m$s
21
,and
60.08–0.11 m$s
21
, respectively (6). This
suggests that if valid velocity measuring
devices are used for monitoring, mean-
ingful changes in velocity between train-
ing sessions are likely to reflect acute
fatigue or gains in strength. Furthermore,
it may also allow for the accurate pre-
scription of resistance training load dur-
ing training and across mesocycles.
There are 4 simple steps for the devel-
opment of an individualized L-V profile
(T3Ta b l e 3 ) ( 8). First, the athlete performs
a 1RM assessment in the relevant exer-
cise to determine their maximum
strength and to allow for monitoring
Table 3
Steps for developing an L-V profile for an athlete in the back squat
Session 1 Session 2
1. Warm-up with dynamic movements and stretches 1. After 48-h rest, the athlete returns and completes repetitions
with 20, 40, 60, 80, and 90% of 1RM
2. Complete 3 repetitions at 20, 40, and 60%. 2. Three repetitions should be used for loads 20–60% and 1
repetition for 80–90%.
3. Complete 1 repetition at 80 and 90%. 3. For sets that involved multiple repetitions (i.e., loads 20–60%),
the repetition with the fastest MV should be recorded.
4. Then 5 maximal attempts at achieving a 1RM are permitted
#
4. With this information, individualized L-V profiles can be
constructed within Microsoft Excel using the MV plotted
against relative load and by applying a line of best fit.
5. After successful attempts, barbell load can be increased in
consultation with the athlete with loads between 0.5 and 2.5
kg.
5. A linear regression equation can then be used to modify
training loads within and between sessions
6. The last successful attempt with a full depth squat with
correct technique can be established as the 1RM.
48 hours have been provided between testing occasions.
1RM 5one repetition maximum.
Applying Velocity-Based Training
VOLUME 00 | NUMBER 00 | MONTH 2020
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of velocity against %1RM over time.
Second (if completing a 1RM assess-
ment provide at least 24 hours’ recov-
ery), perform an incremental loading
test. Previous research has used either
method 1: 3 repetitions with 20, 40,
and 60%, and one repetition with 80
and 90% 1RM, with sets performed 2 mi-
nutes apart (8,9) or method 2: the “2-
point method” with repetitions per-
formed at 2 approximate loads of
;45% 1RM and ;85% 1RM (24). In
step3,thevelocitydataofthefastest
repetition from each intensity
(
F2 Figure 2A) are plotted against the cor-
responding relative load (%1RM), and
then, a linear line of best fit is applied
to extrapolate the regression equation
(Figure 2B). The final step is to create
a velocity table from the regression
equation. This table uses the MV of
the training set, corresponds with a per-
centage of maximum, and can be imple-
mented in much the same way a coach
would traditionally prescribe from a rel-
ativeload(%RM)table(refertoHelms
et al. (37)). In the example table (T4 Tab l e 4 )
if this athlete wanted to complete 6 rep-
etitions at a “Heavy” intensity, the mean
set velocity should be approximately
0.58 m$s
21
. This information may be
particularly useful for practitioners when
accounting for differing rates in adapta-
tion and for the adjustment of training
loads within and across training sessions.
METHODS TO INTERPRET
CHANGES IN VELOCITY-BASED
DATA
Velocity-based testing can serve as
a useful tool for coaches to gain
a “snapshot” of an athlete’s fitness-
fatigue status. For example, when lift-
ing a fixed external load, changes in
peak or mean concentric velocities
may be indicative of altered neuromus-
cular qualities (91). Reductions in
velocity may be symptomatic of
fatigue, overreaching/overtraining, or
detraining/maladaptation, whereas
faster velocities could signify improve-
ments in neuromuscular capacity or
acute potentiation (17).
When interpreting an athlete’s velocity-
based testing data, coaches must con-
sider both the reliability of test perfor-
mance, as well as the practical
importance of a change. The reliability
of test performance is influenced by
measurement error (which is a funda-
mental consideration when purchasing
velocity tracking equipment) and nor-
mal variation within the body’s biolog-
ical systems. A useful metric to quantify
performance reliability is the within-
athlete standard (typical) error (SE).
This can be estimated from a group-
based test-retest reliability study (2,39)
or from the trend in an athlete’s individ-
ual test performance repeated across
a theoretically stable period (e.g., days,
weeks, months) (36,41) (see Appendix
1, Supplemental Digital Content 1,
http://links.lww.com/SCJ/A277).
The SE is reflective of the “typical”
variation in an athlete’s performance
(e.g., mean concentric velocity) that
are due to random factors causing nat-
ural fluctuation. Therefore, applying
the SE to observed test scores as a 6
value can be used to represent
a “normal” range of performance,
should the test be hypothetically
repeated over and over ( F3
Figure 3).
When assessing changes in perfor-
mance, the SE can be used to create
an individual confidence interval (CI)
around change scores and represent
uncertainty in an observed perfor-
mance change (i.e., accounting for
the “noise”). This provides the practi-
tioner a plausible range of values that
are compatible with the data assump-
tions (34) ( F4
Figure 4, see Appendix 2,
Supplemental Digital Content 2,
http://links.lww.com/SCJ/A278).
To know how practically important
a change might be, coaches must decide
on a threshold for a decisive change
and evaluate changes against this value.
Importantly, this concept is entirely
separate from the previously discussed
issues of performance reliability, noise,
and uncertainty. In a hypothetical
world where performance is entirely
stable and changes only due to system-
atic effects (i.e., fitness or fatigue),
changes could simply be evaluated
against a threshold that represents
some value representing practical sig-
nificance. In this regard, we recom-
mend using an anchor-based
approach (79), whereby changes can
be evaluated against a value represent-
ing a “real-world” difference in perfor-
mance. For example, an increase of
one-third of the competition-to-
competition variability in solo athlete
performance, such as weight lifted, best
time, distance thrown, etc., results in
one extra medal every 10 competitions
Figure 2. (A) Mean velocity data attained from an athlete’s L-V profile during the barbell back squat; (B) data, linear regression, and
equation for this athlete’s L-V profile.
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(40). This is often a practice intuitive to
expert coaches who set performance
targets based on their knowledge and
experience of what changes really
make a difference. This threshold infor-
mation could therefore be derived from
expert coach opinion or existing
research on the associations between
test and competitive performance.
Other approaches, such as
distribution-based (e.g., smallest
worthwhile effect), are available, but
can produce arbitrary values lacking
real-world relevance (14).
Once a threshold of practical impor-
tance has been established, coaches
can combine the previously mentioned
concepts and make a decision on an
athlete’s velocity-based testing data.
Of course, we do not operate in a world
where performance is entirely stable,
and therefore, coaches must also con-
sider performance uncertainty. A very
simple and effective way of achieving
this is to visualize the performance
change with its CI against the region
of practical importance (16) (Figure 3).
The decision process is informed by
interpreting the amount of overlap
between the CI and the decisive
threshold. Two such methods that
can assist this include the second-
generation p-value (SGPV) (10,11)
and tests of equivalence using 2 one-
sided tests (TOST) (52,53). In particu-
lar, the SGPV is intended as a descrip-
tive statistic (10) and may therefore be
useful when applied to monitoring
changes in an athlete’s velocity-based
performance. It is beyond the scope of
our review to discuss the application of
SGPV and TOST in detail (refer to
Blume et al. (10,11), Lakens (52) and
Lakens et al. (53)), but we provide sev-
eral recommendations for coaches
using the aforementioned principles
to interpret velocity-based testing data
(see Appendix 3, Supplemental Digital
Content 3, http://links.lww.com/
SCJ/A279). An analysis of changes in
a powerlifter’s mean concentric veloc-
ity from 100-kg warm-up sets of the
barbell back squat throughout a 7-
week training phase (Figure 3) using
several of the concepts we have dis-
cussed is displayed in F5Figure 5.
MANAGEMENT OF FATIGUE
USING RELATIVE VELOCITY LOSS
THRESHOLDS
It is common knowledge that humans
come in different shapes and sizes and
Table 4
An example of an individualized mean set velocity table for the free-weight
back squat with each mean set velocity corresponding to a prescribed
number of repetitions and intensity range
Mean velocity table (m$s
21
)
Intensity
Repetitions
12345678910
Maximum 0.26 0.34 0.38 0.41 0.47 0.51 0.54 0.57 0.60 0.63
Very heavy
Heavy
0.29
0.35
0.35
0.42
0.39
0.46
0.42
0.49
0.48
0.54
0.52
0.58
0.55
0.61
0.58
0.64
0.61
0.67
0.64
0.69
Moderately heavy
Moderate
0.42
0.50
0.49
0.56
0.53
0.59
0.55
0.62
0.60
0.67
0.64
0.70
0.67
0.73
0.70
0.75
0.72
0.78
0.75
0.80
Moderately light
Light
0.57
0.64
0.63
0.70
0.66
0.73
0.68
0.75
0.73
0.79
0.76
0.83
0.79
0.85
0.81
0.87
0.83
0.89
0.86
0.91
Very light 0.71 0.76 0.80 0.82 0.86 0.89 0.91 0.93 0.95 0.97
Figure 3. Mean concentric velocity (MCV ) from 100-kg warm-up sets of the barbell
back squat throughout a powerlifter’s 17-week training phase. Data are
shown as the fastest performance achieved each week 6the standard
(typical) error, derived from the maintenance phase trend (i.e., baseline;
straight red line, weeks 1–10; see Appendix 2, Supplemental Digital Con-
tent 2, http://links.lww.com/SCJ/A278). Loading phase changes from
baseline are evaluated at an alpha of 0.20 (i.e., 80% confidence level). Gray
shaded area 5trivial, based on a minimum practically important difference
of 60.03 m$s
21
and the maintenance trend standard error. From the
athletes’ known load-velocity profile, a 0.03-m$s
21
change in mean con-
centric velocity is indicative of a ;1% change in 1 repetition maximum,
which is 0.3 3the competition-to-competition variability of 3.1%.
Applying Velocity-Based Training
VOLUME 00 | NUMBER 00 | MONTH 2020
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individuals have different physical and
physiological capacities (e.g., marathon
runners compared to sprinters). How-
ever, strength and conditioning practi-
tioners are often taught to use
predictive tables to prescribe resistance
training loads and repetitions
(12,35,80). This is despite the extremely
large variance in the number of repeti-
tions that can be completed with a given
percentage of maximum (19). For exam-
ple, at 80% of 1RM, some individuals
can complete twice as many repetitions
as others (e.g., 8 vs. 16 repetitions) (72).
Thus, when prescribing 3 sets of 8 rep-
etitions at 80% of 1RM, some athletes
will be working to concentric failure,
while others will complete these sets
with relative ease. This heterogeneity
is likely due to a range of factors includ-
ing training history, gender, absolute
strength levels, and recent training
exposure (19,42,72). Consequently, to
ensure improved prescription and to
mitigate divergency in fatigue and adap-
tive responses, relative velocity loss
thresholds can be implemented (63,89).
Recent research (89) has highlighted
the ability of velocity loss thresholds
to maintain velocity and power outputs
when resistance training (Figure 5).
Furthermore, this work has demon-
strated how these thresholds can
account for differences in individual
work capacity. Weakley et al. (89)
showed that when using velocity loss
thresholds, changes in mean barbell
velocity between athletes are possibly
to likely trivial across 5 sets of the back
squat. This is in direct contrast to tra-
ditional prescription methods that
cause very large reductions in velocity
as exercise goes on (85,94). These dif-
ferences in the maintenance of kinetic
and kinematic outputs are due to the
unique “flexible-repetition” schemes
that occur when relative velocity loss
thresholds are applied and allows for
individualization during each set and at
each load/velocity (89). This diverges
from percentage-based methods that
promotes the strength coach to set
arbitrary repetition and set schemes
(e.g., 4 sets of 10 repetitions) that do
not account for athlete differences,
daily readiness, or within-session
fatigue accrual.
Perhaps more important than the con-
trol over training session kinetic and
kinematic outputs is the improved abil-
ity to dictate internal and subsequent
fatigue outcomes by using velocity loss
thresholds ( F6
Figure 6). Recent work
investigating changes in neuromuscu-
lar function have shown that with each
incremental increase in velocity loss
(e.g., 10, 20, and 30% velocity loss),
linear reductions in function occur
(88). This is supported by earlier work
by Sanchez-Medina and Gonza
´lez-
Badillo (74) that assessed velocity and
estimated proximity to concentric fail-
ure. Furthermore, near identical trends
in perceived effort and metabolic re-
sponses also exist (i.e., greater exertion
and metabolic responses in line with
greater increases in velocity loss) (88).
These responses have been found to be
consistent within and between athletes
and demonstrate exceptional levels
of reliability within athletes across
moderate- to long-term training peri-
ods (88).
Figure 4. Hypothetical example of confidence intervals (CIs) applied to a change in
mean concentric velocity. Data are shown as the change 6CI, scaled
against a minimum practically important difference of 60.03 m$s
21
(gray
area).
Figure 5. Analysis of changes a powerlifter’s mean concentric velocity from 100-kg warm-up sets of the barbell back squat
throughout a 7-week training phase (raw data are showing in Figure 3). Changes are derived from baseline performance
established during a priori maintenance phase. CI 5confidence interval; SGPV 5second-generation p-value.
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PROGRAMMING WITH VELOCITY-
BASED TRAINING
Although the ability to have greater
control over training outcomes is an
exciting prospect for the strength and
conditioning practitioner, understand-
ing the varying methods of program-
ming that are available through VBT
is vital for designing effective training
programs. Several studies have sug-
gested that the velocity associated with
a given percentage of 1RM is consistent
across training sessions (3,8,18,24).
However, it has been shown that the
velocity at a given %1RM may shift
due to fatigue (86) or after a short-
term power-oriented resistance training
program (64). Therefore, for accurate
prescription of relative loads, it is
advised to periodically assess the L-V
relationship. Considering this, between
athletes and training sessions, relative
losses in exercise velocity cause consis-
tent internal and external responses at
Figure 6. (A) The individual andAU12 mean group velocity (SD represented by the shaded area) when training with a 20% velocity loss
threshold across 3 sets of the back squat. Data from Weakley et al. (89). (B) The individual and mean group velocity (SD
represented by the shaded area) when training with 3 sets of the back squat with a set repetition scheme (i.e., 10
repetitions for all participants). Unpublished data from Weakley et al. (95). (C) The mean (6SD) velocity from graphs A and
B. Note the maintenance of velocity in the velocity-based training condition compared with the linear loss of velocity in
the percentage condition.
Applying Velocity-Based Training
VOLUME 00 | NUMBER 00 | MONTH 2020
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a given relative intensity (88,89). Conse-
quently, previously well-established
training methods and their periodiza-
tion models can still be implemented.
However, by using velocity to monitor
and guide exercise prescription,
improved individualization and control
of training and subsequent responses
can occur (21,89).
Due to changes in strength across the
training cycle, one issue with
percentage-based prescription is that
therelativeloadprescribedbythe
strength coach may not match the rel-
ative load that is completed during
training. For example, a maximal
strength test from 4 weeks earlier will
not enable accurate prescription of load.
Table 5
Commonly used velocity-based training methods
Method (reference) Load Sets Repetitions Load
Set average velocity (9) The external load is prescribed from the athlete’s LVP. A set and
repetition scheme is prescribed. At the completion of the set,
the average set velocity is required to be within 0.06 m$s
21
of initial prescribed velocity.
If average set velocity 60.06 m$s
21
, external load is
adjusted by 4–5% of 1RM.
Prescribed Prescribed Flexible
Set average velocity +
VL thresholds (9,21)
The external load is prescribed from an LVP. A number of
sets are prescribed with a velocity loss threshold used to
guide when set termination occurs (e.g., 20% velocity
loss). At the completion of the set, the average set
velocity is required to be within a required velocity zone
(e.g., 0.74–0.88 m$s
21
during the back squat).
If average set velocity is not within this zone, the external
load can be manipulated.
Prescribed Flexible Flexible
Targeted velocity
+ VL thresholds
(62,63,88,89)
The athlete is prescribed a starting velocity (e.g., 0.70 m$s
21
)
with the external load being altered to meet this velocity
or velocity range (e.g., 0.70–0.75 m$s
21
) with the external
load being altered to meet this targeted velocity. A
velocity loss threshold (e.g., 10%) is used to guide set
termination.
During subsequent sets, if initial repetition velocity is
greater than 60.06 m$s
21
of targeted velocity, an
additional 30-s recovery is provided. If the following
repetition’s velocity remains outside this range, external
load is adjusted by 4–5% of 1RM.
Prescribed Flexible Flexible
Fixed set + velocity loss
threshold (9)
The external load is prescribed from the athlete’s LVP. A
velocity loss threshold (e.g., 10%) is supplied with the
athlete terminating the set when velocity drops below
the velocity threshold.
Prescribed Flexible Prescribed
Fixed total repetition +
flexible set + velocity
loss threshold (9)
Before the session, a total number of repetitions are
prescribed (e.g., 25 repetitions). A load is prescribed from
the LVP, and a velocity loss threshold is used to guide set
termination. Athletes are allowed as many sets as they
require to complete the prescribed number of repetitions.
Flexible Prescribed Prescribed
Fixed set + velocity
threshold +
repetition cap (9)
Load is prescribed from LVP or targeted velocity, and
a velocity loss threshold is prescribed (e.g., 10%). In
addition, an upper limit of repetitions that can be
completed is prescribed (e.g., 5 repetitions). Athletes
exercise using the prescribed load until repetition velocity
decreases below velocity loss threshold or the repetition
limit is reached.
Prescribed Flexible Prescribed
1RM 5one repetition maximum; Flexible 5an unknown amount that is often dictated by athlete fatigue/readiness (e.g., the athlete will
complete repetitions until barbell velocity drops below a certain threshold); LVP 5load-velocity profile; Prescribed 5dictated before the session
or after set (e.g., 5 sets).
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As a result, external loads that are sup-
plied by practitioners are often too light
or heavy. Established VBT methods can
account for these fluctuations by moni-
toring velocity during the warm-up and
training session (89). Two of the most
common methods use either (I) a tar-
geted training velocity (e.g., an athlete
finds an external load within a given
range that is being targeted that day
[e.g., 0.70 60.05 m$s
21
]) (89) or (II)
a load (as a percentage of 1RM) that
meets a velocity from a previously estab-
lished L-V profile (21). Both these
methods enable reliable and accurate
long-term planning. Furthermore,
within-session alterations in the external
load can be made by the athlete or
coach by simply referring to the MV of
the previous set (21) or the first repeti-
tion of the subsequent set (88,89) to
ensure appropriate loading is occurring
during training. Alternatively, this infor-
mation can be used to guide the termi-
nation of a training session (e.g., if an
athlete consistently cannot meet
requiredvelocitiesatagivenloadthis
may indicate fatigue).
One unique aspect of programming
with VBT is that it allows for “flexible”
or “fixed” set and repetition schemes.
Traditional programming methods
provide rigid programming (i.e., a num-
ber of sets and repetitions are pre-
scribed), but VBT can mitigate the
differences in athletes and their physi-
ological characteristics (89). For exam-
ple, a fixed number of sets may be
applied (e.g., 5 sets) with a flexible rep-
etition scheme (e.g., athletes exercise
until a 20% velocity loss has occurred)
(89). Alternatively, a fixed number of
repetitions could be prescribed (e.g.,
25 repetitions) with a flexible number
of sets (e.g., each set is terminated
Figure 7. Acute and chronic responses to training with smaller or larger velocity loss thresholds. MHC 5myosin heavy chain.
Adapted from (62,63,88,89).
Figure 8. An example of a 6-week daily undulating mesocycle with athletes completing a strength endurance, strength, and power
sessions each week. The bullet point within each connected line signifies the average starting velocity from a given
session (e.g., strength session 1 50.54 m$s
21
). The dotted line indicates the stopping velocity (strength session 1 50.43
m$s
21
). Note the altering starting velocity and changes in velocity loss thresholds. VL 5velocity loss.
Applying Velocity-Based Training
VOLUME 00 | NUMBER 00 | MONTH 2020
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Figure 9. Ten-week block periodization approach to programming the back-squat exercise. The bullet point within each connected
line signifies the average starting mean concentric velocity from a given week (e.g., week 1 50.64 m$s
21
). The dotted
line indicates the average stopping velocity (e.g., week 1 50.45 m$s
21
). Note that the velocity loss threshold reduces
across each mesocycle, while intensity increases. VL 5velocity loss.
Table 6
Example of how velocity-based training for the back-squat exercise can be applied during a training week with one
match
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
1-Match
training wk
Velocity loss
threshold
Rest 30% velocity loss Rest 20% velocity loss 10% velocity loss Rest Match
day
Intensity
a
(;m$s
21
/
% 1RM)
Rest ;0.70 m$s
21
/;65%
1RM
Rest ;0.55m$s
21
/;82%
1RM
;1.00–0.60
m$s
21
/;30–
75% 1RM
Rest Match
day
Volume Rest ;9 repetitions per set Rest ;4–5 repetitions per
set
;2–6 repetitions Rest Match
day
Internal
response
Rest [[[ Metabolic
response &
perception of effort
Rest [[ Metabolic response
and perception of
effort
[Metabolic
response
[4perception
of effort
Rest Match
day
Fatigue
response
Rest [[ Perceived soreness
YY Neuromuscular
function
Rest [Perceived soreness
4Y Neuromuscular
function
4Y Perceived
soreness
[4
Neuromuscular
function
Rest Match
day
Velocity loss thresholds, initial intensity, approximate number of repetitions that will be completed, and estimated internal (during training) and
fatigue responses (24 h following training) are supplied are supplied to assist the practitioner. Information adapted from (6,8,9,88,89).
a
Initial velocity (mean concentric velocity) and relative percentage of 1RM may show slight deviations between athletes.
[[[ 5large increase; [[ 5moderate increase; [5small increase; 45trivial change; 4Y 5trivial to small decrease; YY 5moderate decrease;
1RM 5one repetition maximum.
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when velocity is reduced by 20%, with
athletes implementing as many sets as
necessary to complete the 25 repeti-
tions) (9). With identification of appro-
priate velocity loss cutoffs and their
subsequent fatigue responses, these
flexible programming methods can
account for differing rates of fatigue,
between-athlete heterogeneity, and
daily readiness (89). This is shown in
recent research (89), with flexible pro-
gramming enabling high levels of con-
sistency of both velocity and power
outputs between and within athletes
when compared with regimented set
and repetition schemes based off a per-
centage of an athlete’s previous maxi-
mum (19,95).
T5 Table 5 outlines some of
the most commonly applied methods
of prescribing sets and repetitions
using VBT.
Owing to the ability to accurately pre-
scribe training load and volumes, it is
also feasible to implement VBT in tra-
ditional programming models. Accu-
rate load prescription and velocity
loss thresholds (e.g., 10% vs. 30%) that
induce a desired amount of fatigue can
ensure that specific physical and phys-
iological characteristics can be
targeted. For example, block periodiza-
tion models that use phase potentia-
tion and greater volumes before
heavier loads and lower volumes can
be applied and still follow traditional
concepts (17,57). In a block periodized
model that uses VBT, initial phases
that aim to promote changes in
strength endurance and improvements
in body composition may use 30%
velocity loss thresholds. This could
be followed by a strength mesocycle
that allows for greater loads (i.e., lower
starting velocities) and a smaller veloc-
ity loss threshold (e.g., 20%) that causes
less peripheral fatigue (63,89). Finally,
this could be followed by a strength-
power or tapering mesocycle which
uses a range of initial starting velocities
with a very small velocity loss thresh-
old (e.g., 10%). These smaller thresh-
olds have been shown to minimize
fatigue while also ensuring greater
power outputs during training (89).
These concepts can be applied across
a range of different programming mod-
els (e.g., linear, daily/weekly undulat-
ing, conjugated) and can assist coaches
in applying traditional approaches with
greater control and prescription ( F7 F9Fig-
ures 7–9).
PRACTICAL APPLICATIONS FOR
THE STRENGTH COACH
Maximizing performance through
physical training is the primary goal of
all strength and conditioning professio-
nals. Therefore, applying VBT methods
efficaciously is of great importance. It is
acknowledged that individualization
and greater homogeneity of fatigue re-
sponses can occur when VBT is appro-
priately applied (88,89). However,
strategic implementation can enhance
athlete buy-in and improve outcomes.
Below are practical suggestions that can
assist in the integration of VBT into the
training program.
It has previously been recognized that
providing feedback to athletes as they
train can enhance velocity and power
outputs by up to 10% (92,93,96). Fur-
thermore, because of the naturally
competitive nature of athletes, by al-
lowing individuals of similar ability or
position to train together and observe
each other’s kinematic outputs, greater
competition may occur. However, the
intended purpose of the exercise must
Table 7
Example of how velocity-based training for the back-squat exercise can be applied during a trainingweek with 2 matches
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
2-Match training wk
Velocity loss
threshold
Rest 10% velocity loss Rest Match day 10% velocity loss Rest Match
day
Intensity
a
(;m$s
21
/% 1RM)
Rest ;0.55m$s
21
/;82% 1RM Rest Match day ;0.70m$s
21
/;65% 1RM Rest Match
day
Volume Rest ;2–3 repetitions Rest Match day ;5 repetitions Rest Match
day
Internal response Rest 4Metabolic response
[4perception of effort
Rest Match day [Metabolic response
[4perception of effort
Rest Match
day
Fatigue response Rest 4Y Perceived soreness
[4Neuromuscular
function
Rest Match day 4Y Perceived soreness
[4Neuromuscular
function
Rest Match
day
Velocity loss thresholds, initial intensity, approximate number of repetitions that will be completed, and estimated internal (during training) and
fatigue responses (24 h after training) are supplied to assist the practitioner. Information adapted from (6,8,9,88,89).
a
Initial velocity (mean concentric velocity) and relative percentage of 1RM may show slight deviations between athletes.
[5small increase; [45trivial to small increase; 4trivial change; 4Y 5trivial to small decrease; 1RM 5one repetition maximum.
Applying Velocity-Based Training
VOLUME 00 | NUMBER 00 | MONTH 2020
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also be considered, as the feedback
provided may cause an athlete to sac-
rifice technique for greater velocities.
Although a great amount of publicity
has been given to VBT in recent years,
this has also led to practitioners occa-
sionally attempting to maximize veloc-
ities on exercises that are traditionally
performed for stability and range of
motion development, such as an over-
head squat. When these movements
are performed quickly, they often lose
their intended purpose and benefits.
Consequently, feedback is suggested
to be best applied during exercises with
the greatest force and power outputs
(e.g., Olympic-style lifts, jumps, squats,
and bench press) (1,50,92,93,96).
As athletes participate in much more
than simply strength training, the man-
agement of fatigue is of great importance
for the strength coach. With relative
velocity loss thresholds, one can manage
the accrual of fatigue and cause more
homogenous responses between ath-
letes. It is advised that during the “off-
season” or general preparatory phase,
that greater velocity loss thresholds are
implemented as this period tends to
enable frequent strength training and
residual neuromuscular fatigue is unlikely
to have detrimental effects. Therefore,
20–40% velocity loss thresholds may
be effective in these periods to elicit
greater adaptations in conditioning, lean
body mass, and muscular endurance
(63). Alternatively, in-season, smaller
velocity loss thresholds (,20%) may
be of benefit in reducing fatigue and
ensuring training does not cause substan-
tial reductions in performance (88,89).
These concepts can also be applied
within an athlete’s training mesocycle
(refer to Tables 6–9) T6 T9
with previous
research (88,89) implying that greater
velocity losses (e.g., 30%) be applied at
the start of the week (e.g., match day
[MD] 25), with reductions occurring
as game day draws closer (e.g., 20% at
MD 23 and 10% at MD 22).
Finally, the ability to objectively dictate
load can be of great use for the practi-
tioner (56). Regardless of the method of
implementation, the ability to autoregu-
late loads based off velocity can support
Table 8
Example of how velocity-based training for the back-squat exercise can be applied during preseason
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
Preseason training
wk
Velocity loss
threshold
Rest 30% velocity loss 30% velocity loss Rest 20% velocity loss Rest Match
day
Intensity
a
(;m$s
21
/%
1RM)
Rest ;0.60 m$s
21
/;80% 1RM ;0.70 m$s
21
/65% 1RM Rest ;0.70 m$s
21
/;65% 1RM Rest Match
day
Volume Rest ;9 repetitions per-set ;9 repetitions per-set Rest ;7 repetitions per-set Rest Match
day
Internal response Rest [[[ Metabolic response &
perception of effort
[[[ Metabolic response &
perception of effort
Rest [[ Metabolic response &
perception of effort
Rest Match
day
Fatigue response Rest [[ Perceived soreness
YYY Neuromuscular function
[[ Perceived soreness
YY Neuromuscular function
Rest [Perceived soreness
4Y Neuromuscular function
Rest Match
day
Velocity loss thresholds, initial intensity, approximate number of repetitions that will be completed, and estimated internal (during training) and fatigue responses (24 h following training)
are supplied to assist the practitioner. Information adapted from (6,8,9,88,89).
a
Initial velocity (mean concentric velocity) and relative percentage of 1RM may show slight deviations between athletes.
[[[ 5large increase; [[ 5moderate increase; [5small increase. 45trivial change; 4Y 5trivial to small decrease; YY 5moderate decrease; YYY 5large decrease; 1RM 5one
repetition maximum.
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the management of not only acute-
fatigue responses (e.g., between sets)
but also the accrual of fatigue across
sessions. This can enable practitioners
to be confident in their exercise pre-
scription, even during periods of
congested training or match play. For
example, practitioners are commonly
faced with the issue of athletes coming
straight off the training field and into the
weight room. This often means that the
athlete is fatigued and that the loads
prescribed before the training session
are no longer valid. However, VBTdoes
not face these issues as athletes are pre-
scribed a velocity range rather than
a specific external load. In addition,
because of the many outside stressors
that can impact an athlete (e.g., aca-
demic stress) (54), VBT may F10support
load management (Figure 10) AU7.
CONCLUSIONS
VBTusesexercisevelocitytoinformor
enhance training practice. It can be im-
plemented as a tool that works alongside
traditional percentage-based methods
(e.g., the provision of feedback), or it
canbeusedtoautoregulatethetraining
volume and intensity for each athlete.
From this review, it is advised that:
Table 9
Example of how velocity-based training for the back-squat exercise can be applied during a tapering wk
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
Deload training
wk
Velocity loss
threshold
Rest 10% velocity loss Rest Rest 10% velocity loss Rest Match
day
Intensity
a
(;m$s
21
/%
1RM)
Rest ;0.50 m$s
21
/85% 1RM Rest Rest ;1.00–0.60 m$s
21
/
30–75% 1RM
Rest Match
day
Volume Rest ;2–3 repetitions per-set Rest Rest ;2–6 repetitions Rest Match
day
Internal
response
Rest [Metabolic response &
perception of effort
Rest Rest [Metabolic
response
[4perception of
effort
Rest Match
day
Fatigue
response
Rest 4Y Perceived soreness
[4Neuromuscular
function
Rest Rest 4Y Perceived
soreness
[4
Neuromuscular
function
Rest Match
day
Velocity loss thresholds, initial intensity, approximate number of repetitions that will be completed, and estimated internal (during training) and
fatigue responses (24 h following training) are supplied to assist the practitioner. Adapted from (6,8,9,88,89).
a
Initial velocity (mean concentric velocity) and relative percentage of 1RM may show slight deviations between athletes.
[5small increase; [45trivial to small increase; 4Trivial change; 4Y 5trivial to small decrease; 1RM 5one repetition maximum.
Figure 10. An example of a linear periodization approach to programming the back
squat with a 20% velocity loss threshold applied across a 10-week training
macrocycle. The bullet point within each connected line signifies the
starting velocity from a given week (e.g., week 1 50.82 m$s
21
). The dotted
line indicates the set termination velocity (e.g., week 1 50.66 m$s
21
). Note
that the velocity loss threshold reduces across the macrocycle (emphasized
by the arrows) despite the threshold not changing. This allows for increased
intensity but reduced volumes across time.
Applying Velocity-Based Training
VOLUME 00 | NUMBER 00 | MONTH 2020
16
Copyright © National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
An important consideration for the
practitioner is the validity of the
device that is used to monitor veloc-
ity. Current evidence suggests that
linear position transducers should
be used due to their greater accuracy.
Feedback of performance is provided
either visually or verbally to athletes
as they train. This feedback should
be at frequent intervals (e.g., after
each repetition) and used during
high force and power exercises (i.e.,
primary, multijoint exercises).
For testing performance during bal-
listic exercises with loads that are
#70% of 1RM, PV should be used.
Alternatively, PV or MV could be
used for testing performance .70%.
For the prediction of 1RM ability,
MV should be used. This is due to
smaller differences between different
testing devices, greater linearity of
the L-V relationship, and smaller
between-athlete variation in the
velocity that 1RM occurs.
The “2-point method” has been
shown to be a valid method of cal-
culating the 1RM from the L-V pro-
file during upper-body exercises.
This involves (I) identifying the
exercise-specific V1RM, (II) record-
ing the MV against a light (z45%
1RM) and a heavy load (z85%
1RM), and (III) modeling the indi-
vidual L-V relationship and deter-
mining the 1RM as the load
associated with the V1RM. Coaches
should be aware that the accuracy of
the 2-point method and other
velocity-based 1RM prediction
methods is expected to be lower dur-
ing lower-body exercises.
By quantifying an athlete’s L-V pro-
file and using an accurate velocity
measuring device, practitioners can
equate a given velocity with a per-
centage up to 90% 1RM of an ath-
lete’s maximum capability. By having
this information, differing amounts
of fatigue and rates of adaptation
across athletes can be managed
through accurate daily prescription
of intensities and volume.
Practitioners should consider regu-
larly monitoring velocity (this could
be performed at the start of a training
session) to help objectively monitor
changes in athlete fitness/fatigue. By
monitoring the typical day-to-day
fluctuations in velocity (i.e., the SE)
and applying this to a meaningful
threshold (e.g., change in strength),
practitioners can gain regular objec-
tive insight into the effects of their
training program.
Velocity loss thresholds can account
for between-athlete differences in
muscular endurance and also miti-
gate heterogeneity in short-term
fatigue responses. By altering the
velocity loss threshold, internal and
subsequent fatigue responses
increase or decrease.
Prescription of training using VBT
can occur in many ways. These
methods can fit within traditional
periodization models and can be
used to guide exercise prescription
with greater confidence.
Conflicts of Interest and Source of Funding:
The authors report no conflicts of interest
and no source of funding AU3.
Jonathon
Weakley is a lec-
turer at Austra-
lian Catholic
University,
a research associ-
ate at Leeds
Beckett Univer-
sity, and Sport
Science consul-
tant for the Queensland Reds. AU4
Bryan Mann is
an assistant pro-
fessor at the
University
of Miami.
Harry Banyard
is a lecturer at the
Swinburne Uni-
versity of
Technology.
Shaun
McLaren is
a sport scientist at
England Rugby
League and
research assistant
at Leeds Beckett
University.
Tannath Scott
is a postdoctoral
research fellow at
the University of
New England
and New South
Wales Rugby
League.
Amador
Garcia-Ramos
is a professor at
the University of
Granada and
Universidad Ca-
to
´lica de la San-
´
sima
Concepcio
´n.
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... However, this method becomes very problematic when we consider the day-to-day fluctuations in strength, which have been shown to be as large as 18% above and below the previously tested 1-RM (Flanagan and Jovanović, 2014). Therefore, alternative methods such as velocity-based training (VBT) have been developed to provide accurate and objective data to support the prescription of resistance training (Weakley et al., 2021). This training method can be implemented across various facets of resistance training programming and support the prescription of training loads, sets and the number of repetitions with the help of the velocity variable (Weakley et al., 2021). ...
... Therefore, alternative methods such as velocity-based training (VBT) have been developed to provide accurate and objective data to support the prescription of resistance training (Weakley et al., 2021). This training method can be implemented across various facets of resistance training programming and support the prescription of training loads, sets and the number of repetitions with the help of the velocity variable (Weakley et al., 2021). According to previous research, velocity-based resistance training seems to be an adequate method to improve physical performance in young athletes (González-Badillo et al., 2015). ...
... Following the warm-up and the technique for both the back squat and the hip thrust stated, all participants were assessed for the load-velocity (L-V) profile accordingly with previous recommendations (Weakley et al., 2021). Briefly, athletes completed three repetitions with 20%, 40%, and 60% of 1-RM and one repetition with 80% and 90% of 1-RM. ...
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Complex training consists of a near maximal strength effort followed by a biomechanically similar explosive exercise. One of many complex training methods that have been proposed is the French Contrast Method. The aim of this study was to analyze the effects of the French Contrast Method on maximal strength and power of young female artistic roller skating athletes with the help of velocity-based training to prescribe the intervention program. Eighteen female artistic roller skating athletes, divided into an experimental group (EG) and a control group (CG), participated in this study. The EG performed complex training via the French Contrast Method. The CG did not perform any additional training besides their regular roller skating practices. All participants were tested on the 1-RM back squat and hip thrust, the load-velocity profile assessment of both exercises previously stated, the countermovement jump, and the drop jump. A significant increase in mean concentric velocity (MCV) of the hip thrust exercise from 10 to 60% of 1-RM in the EG was observed. Significant differences between groups were observed for the MCV of the hip thrust from 10 to 90% of 1-RM. There were also significant increases in the 1-RM back squat and 1-RM hip thrust over time in the EG. For the vertical jump variables, there were significant differences between groups for both contact time and the reactive strength index with and without an arm swing. The results of this study suggest that a 6-week training intervention with the use of the French Contrast Method can significantly improve maximal strength and power.
... Velocity-based training (VBT) is a method used to prescribe and monitor resistance training programs based on repetitions' velocity outputs during different exercises. [1][2][3] VBT programming allows coaches to adjust training variables, such as intensity of load (hereby referred to as "load") and volume, to target the desired training stimulus using velocity outputs. 1,3 To illustrate, the nearly perfect linear relationship between load and velocity allows to prescribe training loads according to the full spectrum of the individual loadvelocity curve rather than as percentages derived from a single reference value, such as the 1-repetition maximum (1RM). ...
... [1][2][3] VBT programming allows coaches to adjust training variables, such as intensity of load (hereby referred to as "load") and volume, to target the desired training stimulus using velocity outputs. 1,3 To illustrate, the nearly perfect linear relationship between load and velocity allows to prescribe training loads according to the full spectrum of the individual loadvelocity curve rather than as percentages derived from a single reference value, such as the 1-repetition maximum (1RM). 3,4 The decline in velocity output observed during resistance exercises provides actionable information on the extent of acute neuromuscular fatigue, which accumulates over consecutive repetitions and sets. ...
... 1,3 To illustrate, the nearly perfect linear relationship between load and velocity allows to prescribe training loads according to the full spectrum of the individual loadvelocity curve rather than as percentages derived from a single reference value, such as the 1-repetition maximum (1RM). 3,4 The decline in velocity output observed during resistance exercises provides actionable information on the extent of acute neuromuscular fatigue, which accumulates over consecutive repetitions and sets. 5,6 Moreover, velocity outputs can be used to track and account for the day-to-day variability in performance. ...
Purpose: Velocity-based training is used to prescribe and monitor resistance training based on velocity outputs measured with tracking devices. When tracking devices are unavailable or impractical to use, perceived velocity loss (PVL) can be used as a substitute, assuming sufficient accuracy. Here, we investigated the accuracy of PVL equal to 20% and 40% relative to the first repetition in the bench press exercise. Methods: Following a familiarization session, 26 resistance-trained men performed 4 sets of the bench press exercise using 4 different loads based on their individual load-velocity relationships (∼40%-90% of 1-repetition maximum [1RM]), completed in a randomized order. Participants verbally reported their PVL at 20% and 40% velocity loss during the sets. PVL accuracy was calculated as the absolute difference between the timing of reporting PVL and the actual repetition number corresponding to 20% and 40% velocity loss measured with a linear encoder. Results: Linear mixed-effects model analysis revealed 4 main findings. First, across all conditions, the absolute average PVL error was 1 repetition. Second, the PVL accuracy was not significantly different between the PVL thresholds (β = 0.16, P = .267). Third, greater accuracy was observed in loads corresponding to the mid-portion of the individual load-velocity relationships (∼50%-60% 1RM) compared with lighter (<50% 1RM, β = 0.89, P < .001) and heavier loads (>60% 1RM, 0.63 ≤ β ≤ 0.84, all P values < .001). Fourth, PVL accuracy decreased with consecutive repetitions (β = 0.05, P = .017). Conclusions: PVL can be implemented as a monitoring and prescription method when velocity tracking devices are impractical or absent.
... Commonly used physical quality/athletic performance terms of strength and speed may be easier for an athlete to understand, while referring to a movement as "explosive" is ambiguous (4,11). This term has been used to describe quickness, initial movement, expression of power (7,19,31) and other variations thus making "explosive" more • The greatest amount of force developed in a very brief time period (11). ...
... If this statement is interpreted as a suggestion that training should stimulate the qualities needed in a sport to improve performance, then exercise selections need to be as specific to the physical qualities as possible. One barrier to effective exercise selection and S&C programming development is the overlap of terminology, varied descriptors, and disagreements over terminology that describes power, strength, etc. (1,12,20,27,29,31). Therefore, there is a necessity to consolidate terms for communication consistency and improving S&C planning between sport coaches, SCC, sports medicine team, and athletes. ...
... The variation of these definitions' challenges effective communication between the SCC, sport coaches, and researchers as each may be talking past one another instead of striving to understand the other's perspective (12,31). The use of force in some of the definitions targets the kinetic variable that can be used as a unit of measurement in research, but from an applied perspective, SCC may use pounds (lbs.) or kilograms (kgs.) ...
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The field of strength and conditioning (S&C) has been adulterated with numerous, overlapping terms leading to miscommunication between athletes, sport coaches, strength and conditioning coaches (SCC) and sport scientists. Specifically, the use of various hyphenated terms to describe strength, in combination with contention associated with the proper definition and use of power, warrants the necessity to come to agreement on consistent terminology usage. Considerations should be based on the level of applicability and understanding of those most effected (athlete, sport coach, SCC, sport scientist). Moreover, while the use of kinetic and kinematic variables in describing strength and power related qualities is not incorrect, the population receiving the information must be considered. Athletes and sport coaches may be more influenced by simple cues and descriptors used to create movement intent and overall “buy in” to the S&C plan. Furthermore, SCC may be more concerned with how an exercise or movement will relate to improved sport performance while sport scientists may be more interested with how a specific variable(s) can be measured and quantified. Should the use of ambiguous, overlapping, or complex terminology persist, each of the various populations listed may continue to talk past one another instead of striving to be in agreeance with one another. Additionally, SCC may struggle with exercise selection and muddled programming due to the “paralysis by analysis” phenomenon when attempting to disseminate which exercises and movements to prescribe. Ultimately, the athlete may be most affected due to limited physiological improvement in turn leading to sub-par performance outcomes. Thus, the primary objectives of this article are to advance the field by creating an open discourse between the various individuals involved with the S&C profession while simultaneously shedding light on uncertainty associated with overlapping terms used to describe strength, power and other physical qualities associated with sports performance.
... However, traditional RT loads are usually designed based on individuals' 1RM (onerepetition maximum) before starting an RT session [8]. Such predesigned training loads rarely consider athletes' daily fluctuation in training state or performance [9,10], which may lead to inappropriate training loads, lower training benefits, and even degeneration or injuries [10]. In this context, a series of regulable and flexible RT methods, known as autoregulation methods, were invented to avoid this limitation of traditional RT. ...
... Velocity loss (VL) is a critical index/parameter of velocity-based training (VBT) that is often applied to determine the number of repetitions in a training set [9]. Specifically, a training set stops when the velocity loss exceeds a target value. ...
... Pareja-Blanco, et al. [44] found that VBT with a high velocity loss increased the cross-sectional area of slowtwitch fibers, suggesting a negative impact on maximum strength development. A recent review suggests that the velocity loss of VBT is negatively associated with the IIX (MHC-IIX) percentage and is positively associated with the myosin heavy chain I (MHC-I) percentage [9], which may explain the selective hypertrophy of skeletal muscles in VBT. In other words, a high velocity loss is more beneficial for endurance-related performance instead of for maximum strength. ...
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The velocity loss method is often used in velocity–based training (VBT) to dynamically regulate training loads. However, the effects of velocity loss on maximum strength development and training efficiency are still unclear. Therefore, we conducted a dose–response meta–analysis aiming to fill this research gap. A systematic literature search was performed to identify studies on VBT with the velocity loss method via PubMed, Web of Science, Embase, EBSCO, and Cochrane. Controlled trials that compared the effects of different velocity losses on maximum strength were considered. One–repetition maximum (1RM) gain and 1RM gain per repetition were the selected outcomes to indicate the maximum strength development and its training efficiency. Eventually, nine studies with a total of 336 trained males (training experience/history ≥ 1 year) were included for analysis. We found a non–linear dose–response relationship (reverse U–shaped) between velocity loss and 1RM gain (p dose–response relationship < 0.05, p non–linear relationship < 0.05). Additionally, a negative linear dose–response relationship was observed between velocity loss and 1RM gain per repetition (p dose–response relationship < 0.05, p non–linear relationship = 0.23). Based on our findings, a velocity loss between 20 and 30% may be beneficial for maximum strength development, and a lower velocity loss may be more efficient for developing and maintaining maximum strength. Future research is warranted to focus on female athletes and the interaction of other parameters.
... 17 Briefly, the training target was a proxy of neuromuscular fatigue-the 20% velocity loss-calculated from the first repetition in each set. 18 Between ∼10% and ∼50% of the participants were unable to maintain mean velocity outputs below the 20% velocity loss target across 18 repetitions in the bench press Dello Iacono (antonio.delloiacono@uws.ac.uk) is corresponding author. ...
... Researchers then promptly recalculated and adjusted the load to accommodate the initial velocity before the session could be resumed. 18 Across the total of 156 resistance training sessions completed in this study, this occurred 3 times among 3 different participants. Subjects were asked to refrain from intense training targeting muscle groups involved in the bench press and back squat exercises for at least 48 hours prior to the resistance training sessions, to avoid confounding effects due to muscular fatigue and soreness. ...
Purpose: To compare predetermined and autoregulated resistance training sessions on velocity loss and perceived fatigue. Methods: Twenty-six resistance-trained men completed 3 sessions including the back squat and bench press exercises matched for load (75% of 1-repetition maximum), volume (24 repetitions), and total rest (240 s). Sessions were randomly performed as: traditional-set (TRA), 3 sets of 8 repetitions with 120-second interset rest; cluster interset rest redistribution (IRR), 6 clusters of 4 repetitions with 48-second between-cluster rest; and autoregulation cluster training (ACT), a personalized combination of clusters, repetitions per cluster and between-cluster rest regulated upon a velocity loss threshold. The comparative effects were evaluated on velocity loss outputs measured with a linear encoder and perceived fatigue responses reported using a single-item scale. Results: IRR and ACT induced less velocity loss compared to TRA (b = −2.09, P < .001). ACT also mitigated velocity loss more than IRR (b = −2.31, P < .001). The back squat resulted in greater velocity loss compared to the bench press (b = 1.83, P < .001). Perceived fatigue responses mirrored the pattern observed for the velocity loss outputs (IRR and ACT vs TRA: b = −0.64, P < .001; ACT vs IRR: b = −1.05, P < .001; back squat vs bench press: b = 0.46, P = .005). Conclusions: IRR and ACT reduced neuromuscular and perceived fatigue likely due to their cluster-set structures embedding frequent windows of interset rest. However, the ACT was overall more effective, presumably given its personalized structure.
... Because of the widespread use of sports technology and its exciting practical applications, velocity-based resistance training has made significant progress in the field of strength and conditioning [1,2]. One of the main practical applications consists of recording the load-velocity (L-V) profile for testing and monitoring purposes [3]. For example, the L-V relationship has been recently proposed as a simpler approach to assess the maximal capacities of the muscles to produce force at low (load-axis intercept (L 0 )) and high (velocity-axis intercept (v 0 )) velocities as well as performing work at a maximal rate (area under the L-V relationship line (A line )) [4]. ...
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This study aimed to compare and associate the magnitude of the load–velocity relationship variables between the multiple-point and two-point methods and between the concentric-only and eccentric–concentric prone bench pull (PBP) variants. Twenty-three resistance-trained males completed a preliminary session to determine the concentric-only PBP one-repetition maximum (1 RM) and two experimental sessions that only differed in the PBP variant evaluated. In each experimental session they performed three repetitions against the 14 kg load (L1), two repetitions against the 85% 1 RM load (L4), three repetitions against an equidistant intermediate light load (L2), two repetitions against an equidistant intermediate heavy load (L3), and 1–5 1 RM attempts. The load–velocity relationship variables (i.e., load–axis intercept, velocity–axis intercept, and area under the load–velocity relationship line) were obtained from the multiple-point (L1-L2-L3-L4) and two-point (L1-L4) methods. All load–velocity relationship variables presented greater magnitudes when obtained by the two-point method compared with the multiple-point method (p < 0.001, ESrange = 0.17–0.43), while the load–velocity relationship variables were comparable between both PBP variants (p ≥ 0.148). In addition, the load–velocity relationship variables were highly correlated between both methods (rrange = 0.972–0.995) and PBP variants (rrange = 0.798–0.909). When assessing the load–velocity relationship variables, practitioners should prescribe only two loads, as this maximises the magnitudes of the variables and decreases fatigue.
... Velocity-based training (VBT) is a contemporary method of training prescription that allows individual differences to be taken into account and ensures the maintenance of performance (2,34). Velocity-based training often uses relative velocity loss thresholds (e.g., 5% velocity loss) to guide exercise termination and recovery allotment (26,35,36). ...
Article
The aim of this study was to investigate the effects of traditional, rest redistribution, and velocity-based repeated sprint training methods on repeated sprint performance, perceived effort, heart rate, and changes in force-velocity-power (FVP) profiles in male semiprofessional athletes. In a randomized crossover design, a traditional (2 sets of 6 repetitions [TRAD]), 2 different rest redistribution (4 sets of 3 repetitions [RR4] and 12 sets of 1 repetition [RR12]), and a 5% velocity loss (VL5%) (12 repetitions, with sets terminated when a 5% reduction in mean velocity had occurred) condition were completed. Mean and peak velocity, mean heart rate, and differential ratings of perceived exertion (dRPE) were measured throughout each session, while horizontal FVP profiles were assessed presession and postsession. The RR4 and RR12 conditions allowed the greatest maintenance of velocity, while the RR4, RR12, and VL5% had a moderate, significantly greater mean heart rate than the traditional condition. Trivial, nonsignificant differences between all conditions were observed in dRPE of the legs and breathlessness and FVP profiles. These findings indicate that rest redistribution can allow for greater maintenance of sprint velocity and heart rate, without altering perceived effort during repeated sprint training. In addition, velocity-loss thresholds may be a feasible method of prescription if athletes have diverse physical qualities and reductions in sprint performance during repeated sprint training are undesirable. Practitioners should consider these outcomes when designing repeated sprint training sessions because the strategic use of these methods can alter sprint performance and internal load without changing perceptions of intensity.
... It has been suggested that a more linear approach is viable during the offseason and into the preseason. Contrastingly, a nonlinear periodized approach is more appropriate for team sports during the in-season as there are often fluctuations in biomotor quality emphasis and training volume loads on a daily/weekly basis (157). One of the advantages of this agile approach is that sessions can be individualized and modified to reduce the residual fatigue of weekly competitive matches/training schedules. ...
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Ladies Gaelic football (LGF) is a traditional , amateur Gaelic sport played by female athletes. LGF is an invasion-based field sport involving high-intensity, intermittent match play. There is currently a paucity of research on intercounty (elite level) LGF despite a growing interest in the male version of the game. This article aims to provide strength and conditioning recommendations for LGF with particular focus on the intercounty level of play. Recommendations within this article include a needs analysis, female injury epidemiology , physical and physiological demands, female physiology, strength training, and specific conditioning guidelines based on the sport. Additional recommendations include an LGF-specific testing battery, a proposed periodization cycle, and sports-specific speed and agility development.
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Mendonca, GV, Fitas, A, Santos, P, Gomes, M, and Pezarat-Correia, P. Predictive equations to estimate relative load based on movement velocity in males and females: accuracy of estimation for the Smith machine concentric back squat. J Strength Cond Res XX(X): 000-000, 2022-We sought to determine the validity of using the Smith machine bar velocity to estimate relative load during the concentric back squat performed by adult male and female subjects. Thirty-two subjects (16 men: 23.3 ± 3.8 and 16 women: 26.1 ± 2.7 years) were included. The load-velocity relationship was extracted for all subjects individually. Mean concentric velocity (MCV), combined with sex, was used to develop equations predictive of relative load (% one repetition maximum [1RM]). Prediction accuracy was determined with the mean absolute percent error and Bland-Altman plots. Relative strength was similar between the sexes. However, male subjects exhibited faster concentric MCV at 1RM (p < 0.05). Mean concentric velocity and the sex-by-MCV interaction were both significant predictors of %1RM (p < 0.0001), explaining 89% of its variance. The absolute error was similar between the sexes (men: 9.4 ± 10.0; women: 8.4 ± 10.5, p > 0.05). The mean difference between actual and predicted %1RM in Bland-Altman analysis was nearly zero in both sexes and showed no heteroscedasticity. The limits of agreement in both men and women were of approximately ±15%. Taken together, it can be concluded that sex should be taken into consideration when aiming at accurate prescription of relative load based on movement velocity. Moreover, predicting relative load from MCV and sex provides an error of approximately 10% in assessments of relative load in groups of persons. Finally, when used for individual estimations, these equations may implicate a considerable deviation from the actual relative load, and this may limit their applicability to training conditions in which extreme accuracy is required (i.e., more advanced lifters and athletes).
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Psychologists must be able to test both for the presence of an effect and for the absence of an effect. In addition to testing against zero, researchers can use the two one-sided tests (TOST) procedure to test for equivalence and reject the presence of a smallest effect size of interest (SESOI). The TOST procedure can be used to determine if an observed effect is surprisingly small, given that a true effect at least as extreme as the SESOI exists. We explain a range of approaches to determine the SESOI in psychological science and provide detailed examples of how equivalence tests should be performed and reported. Equivalence tests are an important extension of the statistical tools psychologists currently use and enable researchers to falsify predictions about the presence, and declare the absence, of meaningful effects.
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Psychologists must be able to test both for the presence of an effect and for the absence of an effect. In addition to testing against zero, researchers can use the two one-sided tests (TOST) procedure to test for equivalence and reject the presence of a smallest effect size of interest (SESOI). The TOST procedure can be used to determine if an observed effect is surprisingly small, given that a true effect at least as extreme as the SESOI exists. We explain a range of approaches to determine the SESOI in psychological science and provide detailed examples of how equivalence tests should be performed and reported. Equivalence tests are an important extension of the statistical tools psychologists currently use and enable researchers to falsify predictions about the presence, and declare the absence, of meaningful effects.
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The aim of this study was to assess the criterion validity, interunit reliability (accounting for technological and biological variance), and between-day reliability of a novel optic laser device (FLEX) for quantifying mean concentric velocity. To assess the validity against a three-dimensional motion capture system and interunit reliability with both technological and biological variation, 18 men and women completed repetitions at 20, 40, 60, 80, 90, and 100% of one repetition maximum in the free-weight barbell back squat and bench press. To assess interunit (technological only) reliability, a purpose-built, calibrated rig completed a set protocol with 2 devices. To assess between-day reliability of the technology, the same protocol was repeated 21 days later. Standardized bias, typical error of the estimate (TEE; %), and Pearson's correlation coefficient (r) were used to assess validity, whereas typical error and coefficient of variation (CV%) were calculated for reliability. Overall, TEE (±90 CL) between the FLEX and criterion measure was 0.03 (±0.004) and 0.04 (±0.005) m·s−1 in the back squat and bench press, respectively. For measures of reliability, overall interunit technological variance (CV% [± 90% confidence interval]) was 3.96% (3.83–4.12) but increased to 9.82% (9.31–10.41) and 9.83% (9.17–10.61) in the back squat and bench press, respectively, when biological variance was introduced. Finally, the overall between-day reliability was 3.77% (3.63–3.91). These findings demonstrate that the FLEX provides valid and reliable mean concentric velocity outputs across a range of velocities. Thus, practitioners can confidently implement this device for the monitoring and prescription of resistance training loads.
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Objective: To compare the short-term effect of power- and strength-oriented resistance-training programs on the individualized load-velocity profiles obtained during the squat (SQ) and bench-press (BP) exercises. Methods: Thirty physically active men (age = 23.4 [3.5] y; SQ 1-repetition maximum [1RM] = 126.5 [26.7] kg; BP 1RM = 81.6 [16.7] kg) were randomly assigned to a power- (exercises: countermovement jump and BP throw; sets per exercise: 4-6; repetitions per set: 5-6; load: 40% 1RM) or strength-training group (exercises: SQ and BP; sets per exercise: 4-6; repetitions per set: 2-8; load: 70%-90% 1RM). The training program lasted 4 wk (2 sessions/wk). The individualized load-velocity profiles (ie, velocity associated with the 30%-60%-90% 1RM) were assessed before and after training through an incremental loading test during the SQ and BP exercises. Results: The power-training group moderately increased the velocity associated with the full spectrum of % 1RM for the SQ (effect size [ES] range: 0.70 to 0.93) and with the 30% 1RM for the BP (ES: 0.67), while the strength-training group reported trivial/small changes across the load-velocity spectrum for both the SQ (ES range: 0.00 to 0.35) and BP (ES range: -0.06 to -0.33). The power-training group showed a higher increase in the mean velocity associated with all % 1RM compared with the strength-training group for both the SQ (ES range: 0.54 to 0.63) and BP (ES range: 0.25 to 0.53). Conclusions: The individualized load-velocity profile (ie, velocity associated with different % 1RM) of lower-body and upper-body exercises can be modified after a 4-wk resistance-training program.
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The aim of this study was to investigate the differences and long-term reliability in perceptual, metabolic, and neuromuscular responses to velocity loss resistance training protocols. Using a repeated, counterbalanced, crossover design, twelve team-sport athletes completed 5-sets of barbell back-squats at a load corresponding to a mean concentric velocity of ~0.70 m·s⁻¹. On different days, repetitions were performed until a 10%, 20% or 30% velocity loss was attained, with outcome measures collected after each set. Sessions were repeated after four-weeks. There were substantial between-protocol differences in post-set differential ratings of perceived exertion (dRPE, i.e., breathlessness and leg muscles, AU) and blood lactate concentration (B[La], mmol·L⁻¹), such that 30%>20%>10% by small to large magnitudes. Differences in post-set countermovement jump (CMJ) variables were small for most variables, such that 30%<20%<10%. Standard deviations representing four-week variability of post-set responses to each protocol were: dRPE, 8–11; B[La], 0.8–1.0; CMJ height, 1.6–2.0; CMJ PPO, 1.0–1.8; CMJ PCV, 0.04–0.06; CMJ 100ms-Impulse, 5.7–11.9. Velocity loss thresholds control the magnitude of perceptual, metabolic, and neuromuscular responses to resistance training. For practitioners wanting to reliably prescribe training that can induce a given perceptual, metabolic, or neuromuscular response, it is strongly advised that velocity-based thresholds are implemented.
Article
This study aimed to determine whether the verbal provision of velocity performance feedback during the free-weight bench press (BP) exercise influences (I) the within-session reliability and magnitude of mean concentric velocity (MCV) values recorded against a range of submaximal loads, and (II) the accuracy of the individualized load-velocity profile to estimate the BP 1-repetition maximum (1RM). Fifteen males (BP 1RM relative to body mass = 1.08 ± 0.22) performed an incremental loading test until reaching the 1RM on 2 separate sessions. Subjects received verbal velocity performance feedback in one session (KR) and no KR was provided in another session (Control). A linear velocity transducer was used to collect the MCV against 4 loads (40-55-70-85%1RM), and the BP 1RM was estimated from the individualized load-velocity relationship modeled through the multiple-point (40-55-70-85%1RM) and 2-point methods (40-85%1RM). The KR condition provided a higher reliability (CV: KR = 2.41%, Control = 3.54%; CV ratio = 1.47) and magnitude (P = 0.001; ES = 0.78) of MCV for the 40%1RM, but no significant differences in reliability (CV ratio  1.15) nor in the magnitude (P  0.058; ES range = 0.00-0.32) were observed for higher loads. The accuracy in the estimation of the 1RM was higher for the KR (absolute errors: multiple-point = 3.1  2.3 kg; 2-point = 3.5  2.1 kg) compared to the Control condition (absolute errors: 4.1  1.9 kg for both multiple-point and 2-point methods). These results encourage the provision of verbal velocity performance feedback during BP testing procedures.
Article
This study investigated the return to baseline of movement velocity and maximal strength following a strength-orientated and power-orientated session in the free-weight back squat performed with maximal concentric velocity. Fourteen strength-trained males completed a strength-orientated session (5-sets of 5-repetitions @80% of a one-repetition maximum [1RM]) and a power-orientated session (3-sets of 6-repetitions @50%1RM) in a randomised order over two weeks (e.g. strength week-1, power week-2). The back-squat was then performed with loads of 20%, 40%, 60%, 80%, 90% and 100%1RM at 24, 48, 72 and 96-hours following the strength and power exercise sessions to assess return to baseline of squat velocity and maximal strength. Dependent variables included 1RM, back squat mean velocity (MV) and peak velocity (PV), and countermovement jump peak velocity (CMJ-PV). Meaningful changes ([ES] ≥-0.60) were reported for MV and PV at loads ≥ 60%1RM at 24 and 48-hours after the strength-orientated session. Trivial to small (ES ≤-0.59) differences were reported for squat velocities following the power-orientated session. Only trivial to small ES differences were observed for CMJ-PV, and 1RM at all time points following both sessions. Squat velocity (MV and PV) across the load velocity profile (LVP) had recovered at 72 hours following the strength-orientated session. However, the return to baseline of squat velocity (MV and PV) did not coincide with the return to baseline of 1RM or CMJ-PV. Therefore, measuring and monitoring meaningful changes in velocity may be a more valid and practical alternative in determining full recovery and readiness to train.
Article
Velocity-based training (VBT) requires the monitoring of lift velocity plus the prescribed resistance weight. A validated and reliable device is needed to capture the velocity and power of several exercises. Objectives: The study objectives were to examine the validity and reliability of the Elite Form Training System® (EFTS) for measures of peak velocity (PV), average velocity (AV), peak power (PP), and average power (AP). Design: Validity of the EFTS was assessed by comparing measurements simultaneously obtained via the Qualisys Track Manager software (C-motion, version 3.90.21, Gothenburg, Sweden) utilizing 6 motion capture cameras (Oqus 400, 240 Hz, Gothenburg, Sweden). Methods: Six participants performed 6 resistance exercises in 2 sessions: power clean, dead lift, bench press, back squat, front squat, and jump squat. Results: Simple Pearson correlations indicated the validity of the device (0.982, 0.971, 0.973, and 0.982 for PV, AV, PP, and AP respectively) and ranged from 0.868 to 0.998 for the 6 exercises. The test-retest reliability of the EFTS was shown by lack of significant change in the Pearson correlation (<0.3% for each variable) between the 2 sessions. The multiple count error rate was 2.0% and the missed count error rate was 2.1%. Conclusions: The validity and reliability of the EFTS were classified as excellent across all variables and exercises with only one exercise showing a slight influence by the velocity of the movement.
Article
This study examined the reliability and validity of three methods of estimating the one-repetition maximum (1RM) during the free-weight prone bench pull exercise. Twenty-six men (22 rowers and four weightlifters) performed an incremental loading test until reaching their 1RM, followed by a set of repetitions-to-failure. Eighteen participants were re-tested to conduct the reliability analysis. The 1RM was estimated through the lifts-to-failure equations proposed by Lombardi and O'Connor, general load-velocity (L-V) relationships proposed by Sánchez-Medina and Loturco and the individual L-V relationships modelled using four (multiple-point method) or only two loads (two-point method). The direct method provided the highest reliability (coefficient of variation [CV] = 2.45% and intraclass correlation coefficient [ICC] = 0.97), followed by the Lombardi's equation (CV = 3.44% and ICC = 0.94), and no meaningful differences were observed between the remaining methods (CV range = 4.95-6.89% and ICC range = 0.81-0.91). The lifts-to-failure equations overestimated the 1RM (3.43-4.08%), the general L-V relationship proposed by Sánchez-Medina underestimated the 1RM (-3.77%), and no significant differences were observed for the remaining prediction methods (-0.40-0.86%). The individual L-V relationship could be recommended as the most accurate method for predicting the 1RM during the free-weight prone bench pull exercise.