Sports Med 2005; 35 (3): 213-234
0112-1642/05/0003-0213/$34.95/0
REVIEW ARTICLE
 2005 Adis Data Information BV. All rights reserved.
Challenges in Understanding the
Influence of Maximal Power Training
on Improving Athletic Performance
John Cronin1 and Gord Sleivert2
1
2
New Zealand Institute of Sport and Recreation Research, Auckland University of Technology,
Auckland, New Zealand
Human Performance Laboratory, Faculty of Kinesiology, University of New Brunswick,
Fredericton, New Brunswick, Canada
Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
1. Seminal Training Practice and Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
2. Power-Load Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
2.1 Upper Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
2.2 Lower Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
3. Power and Performance: Cross-Sectional Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
3.1 Upper Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
3.2 Lower Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
3.2.1 Cyclic versus Acyclic Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
3.2.2 Absolute versus Relative Power Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
3.2.3 Maximum Power versus Power Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
3.2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
4. Power and Performance: Training Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
Abstract
The ability to optimise muscular power output is considered fundamental to
successful performance of many athletic and sporting activities. Consequently, a
great deal of research has investigated methods to improve power output and its
transference to athletic performance. One issue that makes comparisons between
studies difficult is the different modes of dynamometry (isometric, isokinetic and
isoinertial) used to measure strength and power. However, it is recognised that
isokinetic and isometric assessment bear little resemblance to the accelerative/
decelerative motion implicit in limb movement during resistance training and
sporting performance. Furthermore, most people who train to increase power
would have limited or no access to isometric and/or isokinetic dynamometry. It is
for these reasons and for the sake of brevity that the findings of isoinertial
(constant gravitational load) research will provide the focus of much of the
discussion in this review.
One variable that is considered important in increasing power and performance
in explosive tasks such as running and jumping is the training load that maximises
214
Cronin & Sleivert
the mechanical power output (Pmax) of muscle. However, there are discrepancies
in the research as to which load maximises power output during various resistance
exercises and whether training at Pmax improves functional performance is
debatable. There is also some evidence suggesting that Pmax is affected by the
training status of the individuals; however, other strength variables could quite
possibly be of greater importance for improving functional performance. If Pmax
is found to be important in improving athletic performance, then each individual’s
Pmax needs to be determined and they then train at this load. The predilection of
research to train all subjects at one load (e.g. 30% one repetition maximum
[1RM]) is fundamentally flawed due to inter-individual Pmax differences, which
may be ascribed to factors such as training status (strength level) and the exercise
(muscle groups) used. Pmax needs to be constantly monitored and adjusted as
research suggests that it is transient. In terms of training studies, experienced
subjects should be used, volume equated and the outcome measures clearly
defined and measured (i.e. mean power and/or peak power). Sport scientists are
urged to formulate research designs that result in meaningful and practical
information that assists coaches and strength and conditioning practitioners in the
development of their athletes.
1. Seminal Training Practice
and Research
Power can be defined as the amount of work
produced per unit time or the product of force and
velocity. The development of power and its transference to performance has been the source of interest
and discussion for years. Initially, coaches and
strength and conditioning practitioners debated the
merits of using various loads for the development of
power. From the literature there appeared two
schools of thought, one that was Western in origin
and espoused the use of lighter loads (<50% one
repetition maximum [1RM]) for improving power
output and athletic performance, whereas Eastern
bloc coaches and trainers proposed that heavier
loads (50–70% 1RM) were superior. For example,
Counsilman[1] a sport scientist and swimming coach,
argued that athletes needed to move light loads at
high speed, as fast movements activated the fast
fibres. Conversely, slow training recruited fibres
with slow contraction characteristics, which was
thought counter-productive to power training. Similarly, Behm,[2] in discussing the use of surgical
tubing for a tennis power programme, suggested that
this should be combined with traditional weight
training incorporating loads of not more than 50%
 2005 Adis Data Information BV. All rights reserved.
1RM. Higher tensions would inhibit the ability of
muscles to move quickly, which was thought a
fundamental prerequisite of power training.
In contrast, some coaches have thought heavier
loads are necessary for improved power production.
Poprawski[3] compared the strength power results of
Edward Sarul, 1983 World Champion in the shotput, to nine well trained shot-putters, which included
>21m throwers. While Sarul was slightly stronger
than the group average in the bench press (1.4%),
snatch (7.9%), power clean (5.3%) and squat
(7.8%), the major differences occurred in tests of
speed and power at heavy loads in those respective
exercises. For example, Sarul’s snatch velocity
ranged from 4.13% faster at 20kg than the average
to 22.13% faster at 80kg. Similarly, his squat velocity was 2.74% greater at 40kg but 25.71% greater at
140kg. These findings led Poprawski[3] to conclude
that movement velocities at higher loads (50–70%
1RM) were critical determinants of athletic success
in athletes and training emphasis should be placed
on moving lighter loads (50% 1RM) quickly, rather
than just striving to lift more weight. Spassov[4] in a
discussion of programme design for athletes, stated
that experts believe loads of 50–70% 1RM performed at a maximal rate, develop explosive power.
Sports Med 2005; 35 (3)
Maximal Power Training and Improving Athletic Performance
Verkhoshansky and Lazarev[5] in a discussion of
Eastern bloc principles for the training of speed and
strength believed also that loads of 50–70% 1RM
were necessary for the development of ‘explosive
strength’. Tidow[6] suggested that heavy loads might
be equally as effective as light loads for stimulating
fast motor unit activity, as the fastest high threshold
units need to be recruited to lift heavy loads. This
dichotomy as to which loads (light vs heavy) best
maximise power development remains topical and is
still the source of much research, but clearly there
was a dichotomy of opinion between Eastern and
Western bloc power training philosophies.
One approach to solving this dichotomy is to
study the relationship between force and velocity,
since power is the product of both these variables. It
is well known that as load increases, the force output
of muscle in concentric contractions increases with a
concomitant decrease in the velocity of shortening.
This phenomenon is known as the force-velocity
relationship of muscle.[7] It is thought that maximum
power output is the product of optimum force and
optimum shortening velocity. For isoinertial contractions, it has been suggested that maximum power output occurs at approximately 30% of maximum
shortening velocity or at approximately 30% of
maximum isometric force.[7,8]
Many researchers have endorsed such loading for
maximising power output[9,10] citing the research of
Edgerton et al.,[11] Faulkner et al.[12] and Kaneko et
al.[13] as support for the utilisation of such loads.
However, closer scrutiny of this research leaves one
thinking that such conclusions are somewhat misleading. For example, Faulkner et al.[12] certainly
reported peak powers at approximately one-third of
maximal shortening velocity; however, they do not
state at what relative force peak power output occurs
(the reader having to extrapolate this information
from the graphs provided). Whether power differs
significantly across the power-force spectrum is,
therefore, unclear. In fact, the power profile of slow
and mixed muscles appears similar across loads of
15–50% maximum isometric force.[12] Furthermore,
whether such findings are applicable to whole muscle or biarticular movement is questionable. For
 2005 Adis Data Information BV. All rights reserved.
215
example, Edgerton et al.,[11] discussing the effects of
muscle architecture, depicted maximum power output (Pmax) occurring at a range of isometric forces
depending whether the knee extensors (45%), knee
flexors (59%), plantar flexors (35%) and dorsiflexors (53%) were studied. These differences were
attributed to different fibre lengths that make determination of Pmax difficult, as Pmax is related to fibre
shortening velocity, which in turn is related to the
number of sarcomeres arranged in series (fibre
length). In fact, Edgerton et al.[11] concluded that the
complication of variable fibre lengths makes any
conclusion regarding power per unit of muscle
weight per muscle group of limited value. It would
seem some of the previous assumptions made by
other authors, based on these research findings, are
misplaced. Additionally, it would seem that studying the power output of whole muscle in vivo would
have greater practical significance to athletes,
coaches and trainers. The power outputs across a
spectrum of loads (power-load spectrum) using dynamic multiarticular exercises similar to those used
during weight training need to be examined, the
results of which should give a greater appreciation
of the load that maximises mechanical power output
in a functional context.
2. Power-Load Spectrum
When studying the power-load relationship, one
must be cautious of extrapolating findings from the
literature since some research has investigated the
power-load relationship indirectly. That is, the relationship between load and power has been investigated, but the load that maximised power output was
not reported. For example, using subjects from a
weight-training class, Mastropaolo[14] measured
power output across loads of 20–100% 1RM. It was
reported that subjects were tested using a benchpress motion, although the figure depicting the exercise appears to be a shoulder-press machine. Nonetheless, power profiles based on this movement are
detailed in graphical form and the authors concluded
(without any apparent statistical support) that the
load maximising power output occurred at 40%
1RM. However, loads from 40% to 60% 1RM apSports Med 2005; 35 (3)
216
Cronin & Sleivert
pear very similar in power output. Making such
interpretations without statistical analysis is problematic.
The findings of another often-cited paper that
investigated the power-load relationship[13] also
need to be interpreted with caution. In this study, 20
male subjects were allotted to four training groups
based on their maximum isometric strength. They
trained their elbow flexors using either isotonic (0%,
30% or 60%) or isometric (100%) contractions.
Peak power of the elbow flexors during concentric
muscle actions was observed at intermediate movement velocities of approximately 30% of maximum
shortening velocity and 30% of maximum isometric
strength.[13] These authors justifiably chose to examine the effects of three loads on Pmax. However,
such a design does not mean that 30% 1RM is the
load that maximises power output. The load that
maximised power output could be anywhere between 30–60% of maximum isometric force, yet
many authors[9,10] continue to cite this study as support for light loads (30%) producing maximal
mechanical power output. Furthermore, uniarticular
motion was examined in this study and untrained
subjects were used, which limits generalisability to
athletic populations. Also, maximal mechanical dynamic power output was reported based on a percentage of maximal isometric force with no estimates of power in relation to the actual dynamic
exercises used in strength training (e.g. squat and
bench press) or the athletic performance itself. It is
quite likely that the force at which Pmax occurs
differs, if expressed relative to a dynamic strength
measure (% 1RM). Based on these observations, it
seems that the assumption of many authors that a
30% 1RM load maximises power output remains
problematic. Investigating the power-load spectrum
using dynamic (isoinertial) multiarticular motion
would appear to have greater practical significance
to strength and conditioning practitioners and sport
scientists alike.
2.1 Upper Body
The upper-body mean and peak power outputs
associated with a spectrum of loads can be observed
 2005 Adis Data Information BV. All rights reserved.
in table I. In all cases, derivatives of the bench press
have been used to study the power-load relationship.
As can be observed from table I, the loads reported
to optimise Pmax are similar irrespective of subject
training status, age and the type of bench-press
motion used. Most studies report a band in which
Pmax occurs, some research also indicating that
loads either side of this band are not significantly
different.[15,16] It would seem then that the majority
of research reports loads of 30–70% 1RM as the
intensities that maximise mean and peak power output.
Three observations from table I appear noteworthy. First, greater power outputs are associated with
the professional and semi-professional rugby league
players, which is no doubt a function of their greater
body mass, training status (maximal strength) and,
therefore, greater relative loads used for the calculation of Pmax. For example, Baker et al.[15] reported
mean body mass and 1RM of 92.0 ± 11.1kg and
129.7 ± 14.3kg, respectively, whereas Cronin et
al.[16] reported 89 ± 2.5kg and 86.3 ± 13.7kg, respectively, for club rugby players. Nonetheless the band
of loads that maximised mean power output was
very similar, although ironically it appears that the
Pmax of the better trained (greater maximal strength)
rugby league players occurred at a lower percentage
of their 1RM. Secondly, it may be that the load that
maximises peak power is slightly greater than the
load that maximises mean power output. The findings for the lower body (see table II) would certainly
support this contention. Thirdly, and related to the
first point, it would appear that Pmax may be transient and is affected by the strength status of the
population being studied. Mayhew et al.[17] reported
that 12 weeks of weight training increased power at
a fixed absolute load (Pmax increased). Presumably,
as the athletes became stronger, the absolute load
became lighter and consequently could be lifted
with greater speed. Thus, the increase in Pmax from
40% to 50% 1RM was due to a 10% increase in
maximal strength. However, this does not seem the
case when relative loads are used.
Baker et al.[15] found that the percentage 1RM
that maximised power output was significantly lowSports Med 2005; 35 (3)
Study
Subjects
Power measure
Maximum power output
(% 1RM or load)
Maximum power output
(W) [mean ± SD]
Baker[18]
22 M professional rugby league players
(NRL)
27 M college-aged players (SRL)
Concentric-only BP throw across loads 40, 50,
60, 70 and 80kg
NRL: 70kg (51% 1RM)
SRL: 60kg (55% 1RM)
NRL: 600 ± 83
SRL: 502 ± 78
Baker et al.[15]
31 professional and semi-professional
rugby league players
Concentric-only BP throw across loads 40, 50,
60, 70 and 80kg
55% 1RM
46–62% 1RMa
598 ± 99
Cronin et al.[16]
27 M club rugby players
Concentric and rebound BP and concentric and
rebound BP throws across loads of 30, 40, 50,
60, 70 and 80% 1RM
50–70% 1RM
211–356
Izquierdo et al.[19]
26 middle-aged (mean age 42y) and 21
elderly M (mean age 65y)
Concentric only and stretch-shorten cycle BP
across loads of 0, 30, 45, 60 and 70% 1RM
30–45% 1RM for both
age groups
237–293
Izquierdo et al.[20]
70 M weightlifters, middle-distance
runners, handball players, cyclists and
controls
Concentric only BP across loads of 30, 40, 50,
60, 70, 80, 90 and 100% 1RM
30–45% 1RM
200–391
2.82–4.86 W/kg
Newton and Wilson[21]
45 M with at least 6mo bench-press
training experience
Rebound BP throws across loads of 10, 20, 30,
40, 50, 60, 70, 80, 90 and 100% 1RM
30–40% 1RM
Newton et al.[9]
17 M exercise science students with
6mo weight training experience
Concentric only and rebound BP throws across
loads of 15, 30, 45, 60, 75 and 90% 1RM
30–45% 1RM
Bemben et al.[22]
31 M college students
Rebound BP across loads of 30, 40, 50, 60, 70
and 80% 1RM
50% 1RM
Cronin et al.[16]
27 M club rugby players
Concentric and rebound BP and concentric and
rebound BP throws across loads of 30, 40, 50,
60, 70 and 80% 1RM
50–60% 1RM
40–70% 1RMa
Mayhew et al.[17]
21 M college students
Rebound BP across loads of 30–80% 1RM
40% 1RM preintervention
50% 1RM after subjects
increased strength
Siegel et al.[23]
25 M college students
BP across loads of 30, 40, 50, 60, 70, 80 and
90% 1RM
40–60% 1RM
Mean power output
560–563
Maximal Power Training and Improving Athletic Performance
 2005 Adis Data Information BV. All rights reserved.
Table I. Loads that maximised mean and peak power output for the upper body
Peak Power Output
Similarly effective to loads that maximised power output.
b
Extrapolated from graph.
1RM = one repetition maximum; BP = bench press; M = male; NRL = national rugby league; SRL = student rugby league.
~500b
217
Sports Med 2005; 35 (3)
a
463–626
218
 2005 Adis Data Information BV. All rights reserved.
Table II. Loads that maximised mean and peak power output for the lower body
Study
Subjects
Power measure
Maximum power output
(% 1RM)
Maximum power output
(W) [mean ± SD]
Baker et al.[24]
32 professional and semi-professional
rugby league players
JS across loads of 40, 60, 80 and 100kg –
system mass
55–59% 1RM
47–63% 1RMa
1851 ± 210
Izquierdo et al.[19]
26 middle-aged M (mean age 42y) and
21 elderly M (mean age 65y)
Concentric only and stretch-shorten cycle halfsquats across loads of 0, 30, 45, 60 and 70%
1RM
60–70% 1RM for both
age groups
391–486
Izquierdo et al.[20]
70 M subjects – weightlifters, middledistance runners, handball players,
cyclists and controls
Concentric only half-squats across loads of 30,
40, 50, 60, 70, 80, 90 and 100% 1RM
45–60% 1RM
385–755
5.5–9.43 W/kg
Sleivert and
Taingahue[25]
30 M rugby, rugby league and
basketball players
SS and TS across loads of 30, 40, 50, 60 and
70% 1RM
SS: 30–60% 1RM
TS: 30–60% 1RM
7.32 ± 1.34 W/kg
7.07 ± 1.25 W/kg
Weiss et al.[26]
31 M fitness-trained lifters
Concentric-only parallel squats across loads of
30, 60 and 90% 1RM
30% 1RM
1011 ± 100
Bourque and Sleivert[27]
16 males (eight power [six volleyball,
two badminton], eight endurance
athletes)
Parallel concentric JS across loads of 0, 30, 40,
50, 60, 70% 1RM
Body mass included
Mean: 14% 1RM
Mode for power
athletes 0% 1RM;
mode for endurance
athletes 30% 1RM
Mean: 5216 ± 1234
Power: 6117 ± 867
Endurance: 4315 ± 808
Esliger and Sleivert[28]
21 (11 M and 10 F) volleyball and
basketball players
Parallel concentric JS across loads of 30, 40,
50, 60, 70 and 80% 1RM
63% 1RM
1766 ± 479
Siegel et al.[23]
25 M college-aged students
Squats across loads of 30, 40, 50, 60, 70, 80
and 90% 1RM
50–70% 1RM
~950b
Sleivert and
Taingahue[25]
30 M rugby, rugby league and
basketball players
SS and TS across loads of 30, 40, 50, 60 and
70% 1RM
SS: 30–60% 1RM
TS: 50–70% 1RM
17.10 ± 3.15 W/kg
17.58 ± 2.85 W/kg
Stone et al.[29]
10 subjects with a range of training
experience (7wk to >15y)
JS and CJ across loads of 10, 20, 30, 40, 50,
60, 70, 80, 90 and 100% 1RM
Weakest subjects: 10%
1RM
Strongest subjects:
40% 1RM
JS: 3482 ± 443
CJ: 3785 ± 376
JS: 5635 ± 2577
CJ: 5391 ± 2566
Thomas et al.[30]
19 untrained F
Double leg-press
56–78% 1RM
404 ± 22
31 M fitness-trained lifters
Concentric only squats (parallel)
60% 1RM
1711 ± 188
Mean power output
Peak power output
Weiss et al.
a
Similarly effective to loads that maximised power output.
b
Extrapolated from graph.
1RM = one repetition maximum; CJ = countermovement jump; F = females; JS = jump-squats; M = males; SS = split jump-squats; TS = traditional jump-squats.
Cronin & Sleivert
Sports Med 2005; 35 (3)
[26]
Maximal Power Training and Improving Athletic Performance
er in professional compared with state and cityleague college-aged rugby league players (see table
I). Reanalysed data from Baker’s previous research
confirmed this finding, the strongest national league
players used significantly lower resistances of 47%
1RM compared with the 54% 1RM of less strong
national league players.[31] It would seem as athletes
become stronger they can produce greater power
outputs with any absolute load, but the ability to
produce power at a given percentage of their 1RM
remains similar as relative resistances increase proportionally to maximum strength levels.
2.2 Lower Body
In terms of the lower body, the mean and peak
power outputs associated with a range of loads can
be observed in table II. The power outputs are
reported for a greater variety of movements compared with the upper body, and female power outputs are also represented. The research of Thomas et
al.[30] reported a higher Pmax (56–78% 1RM) than
most other research, which could be attributed to the
subject’s untrained status, female sex or the different movement used (double leg press). However, in
terms of the squat and its derivatives, no clear trends
are observable between training status, sex, age or
type of exercise used. Most studies report a
‘bandwidth’ of loads that maximise power output.
Also, unlike in upper-body exercises, peak power
has been reported in some instances to occur with no
extra load on the bar (unweighted) or only a light
load (10–20% 1RM).[27] Bourque and Sleivert[27]
recently reported power results much higher and at
lighter relative loads than other studies in the literature that have reported lower peak power to occur at
heavier relative loads. This is because body mass
has been included as part of the resistance the athletes are propelling in the jump-squat. When using
ballistic motion such as jump-squats, it is thought
appropriate to use system mass to calculate loading
intensity, as the subject must propel themselves as
well as the bar. Excluding body mass from the
equation decreases the total mass component of
force, therefore, decreasing total power output. Baker et al.[24] also included body mass in their power
 2005 Adis Data Information BV. All rights reserved.
219
calculation and, consistent with the results of Bourque and Sleivert,[27] their calculated mean power
values are much higher than other mean power
values reported in the literature. The relative load at
which they reported mean power to be maximal was,
however, much higher than that reported in Bourque
and Sleivert’s study and consistent with the other
literature. Nevertheless, it should be noted that Baker et al.[24] did not measure power output at loads
lighter than 40kg on the bar, so it seems likely that
mean power could have been higher at lighter loads
using this method of calculation.
Other studies have used different calculations to
study the load-power relationship. Sleivert and Taingahue[25] calculated net power as force exerted
into the bar by the subject: (bar weight + [bar mass ×
acceleration by subject]) × velocity of bar. It appears
that Izquierdo et al.[20] and Weiss et al.[26] calculated
power output by simply using bar mass relative to
1RM. The variety of methods used to calculate the
power output makes inter-individual comparisons
between studies difficult. Clearly a standard method
for calculating power in resistance training movements needs to be agreed upon. In the meantime,
researchers and practitioners should be aware of the
implications resulting from including or excluding
body mass in power calculations for exercises occurring in the vertical plane. It seems reasonable to
include body mass as part of the resistance athletes
are working against for exercises occurring in the
vertical plane. If body mass is not taken into account
when calculating the load for subjects/athletes, inappropriately heavy loads may be selected for jumpsquats. For upper-body exercises, only a fraction of
body mass is propelled, therefore, the load-power
relation is potentially different and mean and peak
power are likely to occur at higher relative loads.
Besides incorporating body mass into the power
calculations, there are two other reasons why both
Baker et al.[24] and Bourque and Sleivert[27] reported
high power outputs in trained power type athletes.
First, jump-squats were used in both these studies,
and ballistic motion of this type has been shown to
increase power output compared with traditional
weight-training movements as used by Izquierdo et
Sports Med 2005; 35 (3)
220
al.[20] and Weiss et al.[26] where there is no projection
of oneself or the bar.[16,32] Secondly, specific neuromuscular adaptations (higher maximal strength or
maximum velocity of contraction) may have influenced Pmax. In the recent dissertation of Bourque
and Sleivert,[27] the major difference in jump-squat
power between power and endurance athletes was
the ability of the power athletes to increase the
velocity of movement at light loads. Endurance athletes could not increase the velocity of movement
and, therefore, power decreased markedly as load
decreased.
As stated earlier in this section, the method of
calculating Pmax has a large influence on both absolute power values and the power-load relationship.
Additionally, the relative load that maximises peak
power appears higher than the load that maximises
mean power. Furthermore, it seems that these factors may be influenced by the type of exercise
performed.[25,30] The previously discussed results of
Edgerton et al.[11] (see section 1) would support such
a contention given that Pmax occurred at different
isometric forces depending whether the knee extensors (45%), knee flexors (59%), plantar flexors
(35%) or dorsiflexors (53%) were used. Therefore,
different exercises (muscle groups) may conceivably have differential power outputs. However,
Sleivert and Taingahue[25] explain the differences in
maximal peak power between the split and traditional jump-squats reported in their study as being a
result of the different starting position of both exercises. That is, the 1RM for the low start position of
the traditional squat (149.5 ± 22.6kg) was significantly less than for the split squat (206.6 ± 34.4kg).
As a result of the low start position of the traditional
squat, the load was difficult to move initially, but
comparable velocities to the split squat were
achieved later in the lift. Through the range of
movement in which peak power occurred, both
squats had similar bar velocities and absolute loads,
although relative loads were very different. Therefore, traditional squat peak power was maximal at a
higher relative load to that of the split squat. It was
concluded that the prescription of maximal power
training using different exercises and ranges should
 2005 Adis Data Information BV. All rights reserved.
Cronin & Sleivert
detail the loading parameters specific to each individual exercise and that this be based on 1RM
assessment similar to the range of motion being
prescribed for power training. Qualifying this statement, prescription of the load that optimises Pmax
needs to be determined for each individual per exercise. Research reports the mean response (% 1RM =
Pmax) for the population being studied, but the
‘bandwidth’ approach to reporting Pmax adopted by
most research suggests that there is a range of loads
that maximise power output or more likely that there
are large inter-individual differences in Pmax.
3. Power and Performance:
Cross-Sectional Research
Theoretically, the best improvements in athletic
tasks that involve significant power output (jumping, sprint, agility and lunge performance) would be
gained by training at the load that maximised an
individual’s power output using an exercise similar
to their athletic activity. However, this presumes
that power is the best predictor of athletic performance and, therefore, it is training to improve power
output that will best facilitate improved performance. Such an assumption may be misplaced principally because of the diversity of strength/power
measures, flawed methodologies and misrepresented research findings. For example, literature dealing
with the development of power tends to mix terminology using such terms as power, rate of force
development (RFD), explosive strength (maximum
force/time to achieve maximum force) and/or impulse interchangeably. One needs only to read some
reviews in this area to observe the confusion, power
has been associated with the ability to exert great
force in a short amount of time (impulse)[33] and
confused with explosive strength or rate of force
development.[34] Newton and Kraemer,[35] in considering methods to increase muscular power, devote
much of their discussion to the importance and
development of RFD. Sapega and Drillings,[36] in a
discussion of the confusion that abounds concerning
the measurement of power, detail how one group of
authors have calculated peak power by dividing
peak torque by the duration of the contraction and
Sports Med 2005; 35 (3)
Maximal Power Training and Improving Athletic Performance
221
Table III. Intercorrelation matrix between traditional strength and power measures and Zatsiorsky’s measures of explosive strength[40]a
IES
IES
RC
SG
AG
MP
PP
MF
PF
I100
RC
0.84
SG
0.85
0.68
AG
0.80
0.76
0.55
MP
0.75
0.43
0.76
0.54
PP
0.74
0.45
0.73
0.60
0.99
MF
0.80
0.55
0.62
0.52
0.88
0.85
PF
0.86
0.61
0.69
0.63
0.92
0.90
0.99
*
I100
0.80
0.54
0.53
0.51
0.89
0.86
0.99
0.99
*
MS
0.81
0.56
0.69
0.57
0.85
0.83
0.94
0.94
0.94
a
MS
*
*
*
*
*
*
*
*
All correlations are statistically significant at p ≤ 0.05.
AG = acceleration gradient; I100 = impulse at 100ms; IES = index of explosive strength; MF = mean force; MP = mean power; MS =
maximal strength; PF = peak force; PP = peak power; RC = reactivity coefficient; SG = starting gradient; * indicates 100% perfect
correlation.
two other studies have used initial RFD as measures
of power.[36]
Misused terminology and misrepresented research findings are also prevalent in the literature. In
a study of 8–12RM weight training performed at
different execution speeds, Young and Bilby[37] described their two power measures as maximum RFD
and jump height. Neither measure is representative
of power output. Indeed, leg power is generally not
strongly correlated to jump height and there are
limitations in extrapolating results from functional
tests such as vertical jump to reflect power. Additionally, some research findings have been interpreted erroneously and, as such, conclusions about
power development are questionable. For example,
the work of Schmidtbleicher and Buehrle[38] has
been used as justification for the use of high loads
for the development of power.[9,15] However, this
study only measured the changes in maximum force
and RFD, and changes in these strength qualities are
not necessarily representative of changes in power.
Clearly, in each of these cases, different strength
capabilities are being investigated or discussed, each
representative of different regions of the force-time
curve as opposed to power the product of force and
velocity. Each of these strength qualities conceivably needs to be developed in a different manner to
power. For example, the relationship between traditional measures of force and power, and measures of
explosive strength (index of explosive strength, re 2005 Adis Data Information BV. All rights reserved.
activity coefficient, start-gradient and accelerationgradient)[39] can be observed from table III. The
relationship of these measures of explosive strength
to more traditional measures of force and power
suggest that the measures of explosive strength for
the most part measure different strength qualities
than more traditional measures. In particular, the
reactivity coefficient (r = 0.43–0.61) and A-gradient
(r = 0.52–0.63) have less of their variance explained
by force and power.
Progressing this contention, it may be that the
predilection of research and conditioning practice
on improving power may be misplaced. That is,
strength qualities such as impulse, RFD or explosive
strength may better predict athletic performance and
hence it is the development of these qualities that
research and strength training should focus on. Further confounding the understanding of power assessment and development is the practical significance of mean and peak power output. The importance of these two variables, their relation to
different athletic activities and their development is
not well documented and for the most part poorly
understood. The first part of this section will critique
the literature that has investigated the relationship
between power and athletic performance. Thereafter, the relationship between other strength capabilities and athletic performance will be discussed in an
effort to clarify the importance of Pmax in improving
athletic performance.
Sports Med 2005; 35 (3)
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Cronin & Sleivert
3.1 Upper Body
There is a paucity of data concerning the relationship between power measures and upper-body functional performance. This may be a result of the
difficulty of finding a suitable upper-body test that is
as widely accepted as sprinting or jumping. However, tasks such as throwing shot-puts, balls or unloaded barbells have been used in the literature. For
example, Mayhew and colleagues[41-43] have examined the relationship between bench-press power
and shot-put throw in a number of studies. They
found that bench-press power output using an absolute load of 20kg (~40% 1RM) was non-significantly correlated (r = 0.38) to seated shot-put throw for
64 female college athletes.[42] For 40 college football
players, Mayhew et al.[41] found that the seated shotput throw was significantly correlated to benchpress absolute power (r = 0.51) and relative power
output (r = 0.66) assessed using a load of 60% 1RM.
Mayhew et al.[43] also reported that none of the
changes in seated shot-put distance were significantly correlated to increases in bench-press power output (loads 30–80% 1RM) at the conclusion of a
12-week training study. It would seem that the
strength of the relationship between power output
and seated shot-put throw could possibly be influenced by the mass of the bench-press load, the mass
of the shot-put, and the strength and sex of the
subjects.
Baker[31] found that Pmax was significantly related (r = 0.46) to an incline bench-press throw (20kg),
an exercise deemed to indicate upper-body speed
capabilities. Pmax explained only 21% of the variance associated with the so-called functional performance measure. It also should be noted that power outputs associated with absolute loads of 40 and
60kg were significantly related to the performance
measures (r = 0.42–0.50). Cronin and Owen[44] investigated whether chest-pass distance was related
to various strength and power measures of the upper
body as measured by a bench-press throw (10kg) on
a Smith machine. A significant relationship between
maximal strength (r = 0.71), mean power (r = 0.77),
peak power (r = 0.80) and impulse (r = 0.81) and the
chest pass were reported. It seems that impulse is
 2005 Adis Data Information BV. All rights reserved.
equally effective in explaining the shared variance
between strength/power measures and the performance measure. However, it should be noted that Pmax
was not determined in this study but rather the
power outputs associated with a concentric-only
bench-press throw (10kg) were calculated.
3.2 Lower Body
In terms of the lower body, the manner in which
power has been calculated and the variety of exercises used to assess power make comparisons between studies difficult (see table IV). As a result, the
importance of power as a determinant of athletic
performance is difficult to disentangle. One approach to clarify the role of power as a predictor of
athletic performance is to discuss power assessment
within certain frameworks noting thereafter whether
the literature supports such a contention.
3.2.1 Cyclic versus Acyclic Assessment
Theoretically, tests of power output that are cyclic in nature, involve the stretch-shorten cycle (SSC)
and include horizontal as well as vertical motion
should better predict performance in tasks such as
running and agility performance. The MargariaKalamen step test is one of the more widely used
tests used to calculate anaerobic power: power =
(subject mass [kg] × 9.8 N/kg × 1.02 [vertical distance between test stairs])/time taken to ascend test
stairs. Significant correlations (r = –0.43 to –0.71)
were reported between power and sprint and agility
performance using this test, only when power output
was expressed relative to mass (see Mayhew et
al.,[42] table II). The highest correlation was reported
between the 36.5m (40-yard) dash time and the step
test. This makes sense as theoretically the greater the
approach velocity during the test, the greater the
vertical velocity and, therefore, power output. However, there is still a great deal of unexplained variance (50–81%) between the step test and the measures of performance. Some studies have reported
that approach velocity and power output are only
moderately correlated[50] and, therefore, it has been
speculated that something more than vertical velocity accounts for power production. The shared variSports Med 2005; 35 (3)
Subjects
Power measure
Performance measure
Baker and Nance[45]
20 professional rugby
league players
Loaded jump-squats across loads of
40, 60, 80 and 100kg
Absolute (W)
Relative (W/kg)
10m sprint time
40m sprint time
r
–0.02
–0.52
–0.02
–0.52
Chelly and Denis[46]
11 M handball players
Average hopping power
Average treadmill power (W/kg)
Average treadmill power
Maximal track running velocity
0.66*
0.20 (NS)
0.73*
Driss et al.[47]
18 M volleyball players
Anaerobic power (Pmax) on cycle
(6 sec) expressed as W/kg
Vertical Jump
0.75**
Kukolj et al.[48]
24 well conditioned M PE
students
Average leg power per kg of body
mass from continuous jumping
protocol according to Bosco et al.[49]
0–15m time
15–30m time
0.03 (NS)
0.26 (NS)
Mayhew et al.[50]
53 football players
Anaerobic power (W)
Anaerobic power (W/kg)
Margaria-Kalamen step test
9m (10yd) dash time
36.5m (40yd) dash time
Agility – modified Missouri State
agility run
0.16 (NS)
–065*
0.21 (NS)
–071*
0.22 (NS)
–0.43*
Meckel et al.[51]
20 F track athletes
10 recreationally trained F
Wingate anaerobic test (30 sec):
maximum power (W/kg)
100m sprint from standing start
–0.88
Nesser et al.[52]
20 sportsmen
Wingate 10 sec anaerobic test:
anaerobic power (W/kg)
40m sprint time
–0.46*
Sleivert and
Taingahue[25]
30 M rugby league, rugby,
and basketball players
Split squat
Traditional squat
5m time
5m time
MP: –0.68**
PP: –0.65**
MP: –0.64**
PP: –0.66**
Thomas et al.[30]
19 untrained F
Double leg-press peak power output
Vertical jump
Sprint 36.5m (40yd)
0.73 (p < 0.004)
0.14 (p < 0.573)
Young et al.[53]
18 footballers
CMJ (power)
20m straight sprint
4 × 20m sprints bouncing football or
changing direction (agility)
0.66 (NS)
to
to
to
to
–0.08 (NS)
–0.61*
–0.17 (NS)
–0.76*
CMJ = counter-movement jump; F = females; M = males; MP = mean power; NS = not significant; PE = physical education; Pmax = maximal power output; PP = peak power; * p <
0.05; ** p < 0.001.
223
Sports Med 2005; 35 (3)
Study
Maximal Power Training and Improving Athletic Performance
 2005 Adis Data Information BV. All rights reserved.
Table IV. Relationship between measures of power and performance for the lower body
224
Cronin & Sleivert
ance between the power and performance measures
would certainly support such a contention.
Other studies that have used cyclic SSC assessment include Chelly and Denis[46] who used a treadmill sprint test and Kukolj et al.[48] who used continuous jumping protocol. The best single predictor of
maximal running velocity was treadmill forward leg
power (r = 0.73) in Chelly’s study; however, this
only accounted for 53% of the variance. Kukolj et
al.[48] found no significant relationship between their
power measures and sprint times.
Another test of anaerobic power that is cyclic in
nature but does not involve SSC motion is the Wingate cycle test. Three studies[47,51,52] used the power
output from the Wingate test to predict jump and
sprint performance. Once more the diversity in testing procedures is apparent, even when the same test
is used. All three studies calculated power output
over different time periods. Furthermore Driss et
al.[47] determined Pmax and used this measure for
comparisons, Nesser et al.[52] used peak power during their 10-second work bout and Meckel et al.[51]
used the highest work performed during any 5-second work interval. The variety in outcome measures
between studies also makes comparisons difficult
and it is no wonder a wide range of correlations
between power and performance measures were reported (r = –0.46 to –0.88). From the studies represented in table IV it appears that the power outputs
associated with cyclic type motion do not predict
performance any better than acyclic measures.
3.2.2 Absolute versus Relative Power Output
It may be that power needs to be normalised to
body mass for power output to be truly representative of the power needed to run and jump. Once
more, the literature is confusing. Two studies[45,46]
have represented their power outputs in absolute
(W) and relative (W/kg) terms. The absolute power
outputs of Baker and Nance[45] were not significantly related to 10 or 40m sprint performance; however,
when expressed relative to body mass, significant
relationships were reported (r = –0.52 to –0.76),
likely because body mass must be accelerated in
sprinting. However, some of the findings of Chelly
and Denis[46] are the antithesis of Baker and
 2005 Adis Data Information BV. All rights reserved.
Nance[45] with the absolute power values being the
only measures significantly related to maximal velocity in a 40m sprint. Both studies investigated the
relationship between power output and sprint times
of athletes over fairly similar distances, the differences in dynamometry (jump-squats vs treadmill
sprints) being the major difference between studies.
It should be pointed out that in the study of Chelley
and Denis,[46] although relative power was not correlated to maximal running velocity, it was strongly
correlated to sprint acceleration (r = 0.80). Of the
other studies that expressed power output relative to
body mass, the results were mixed with high,[51]
moderate,[47] low[52] or non-significant relationships[48] to measures of performance reported. There
appears to be no clear consensus as to the importance of normalising power output to body mass for
predicting performance. Also, most normalised
power outputs (the exception being Meckel et al.[51])
explained <57% of the common variance associated
with the performance measure. Meckel et al.[51]
studied a very large group (n = 30) of mixed sprint
ability (mean 100m time ranged from 11.1 seconds
in the fastest group [n = 10] to 14.2 seconds in the
slowest group [n = 10]). Heterogeneity of subjects
may have contributed to the high correlations reported.
3.2.3 Maximum Power versus Power Output
The low shared variance between power output
and performance measures reported previously may
be due to the fact that most of the studies have not
determined Pmax for the assessment that is predicting the performance measure. Baker and Nance[45]
reported that Pmax/kg was significantly related to
10m (r = –0.56) and 40m (r = –0.76) sprint performance. However, the strength of these correlations
were very similar to the correlations associated with
other loads and in the case of 10m sprint performance, the correlations between Pmax and speed were
lower than the correlations reported for some of the
other loads. Driss et al.[47] also determined Pmax for a
6-second sprint test on a cycle at different braking
forces. The braking force that maximised Pmax was
recorded and related to functional performance (vertical jump). Unlike Baker and Nance,[45] the other
Sports Med 2005; 35 (3)
Maximal Power Training and Improving Athletic Performance
power outputs associated with various loads and
subsequent relationships were not reported in this
study, therefore, conclusions cannot be made as to
the significance of the load that optimises Pmax in
relation to other loads. In both studies, however,
Pmax accounted for <58% of the shared variance
between the performance measures.
It appears that none of these frameworks clarify
the importance of power output or Pmax as predictors of performance more so than the other. Given
the great variety of assessment techniques, it might
have been expected that one type of assessment may
predict performance to better effect. This was not
the case, the greatest shared variance between a
measure of power and performance being 77% and
most coefficients of determinations approximately
50% or less. It may be that power is not an important
determinant of performance and other strength measures may be of equal or greater importance or
power measurements have not be appropriate for the
performance.
It has been suggested that for activities that require fast force production (100–300ms) such as
throwing, jumping and sprinting,[6] that the rate at
which force is developed is the most important
determinant of athletic success.[54] Wilson et al.[55]
investigated the relationship of a series of isometric,
concentric and SSC rate of force development tests
performed in an upright squat position to sprinting
performance. Of the 20 force-time variables generated using a modified Smith machine over a force
platform, the concentric force at 30ms was the only
measure significantly correlated to sprint performance (r = –0.616) and, in addition to concentric
maximum RFD, were the only measures able to
effectively discriminate between good and poor performers.[55] The authors emphasise the superiority of
concentric RFD tests over and above isometric and
SSC RFD tests and suggest their inclusion in sport
science test batteries. It should be remembered,
however, that concentric RFD and force at 30ms
explained <38% of the variance associated with 30m
sprint performance. Furthermore, this type of methodology does not clarify whether power is a better
predictor of athletic performance, as power was not
 2005 Adis Data Information BV. All rights reserved.
225
measured. Reviewing research that has investigated
the ability of a variety of strength and power measures to predict athletic performance, may give better insight into the importance of power as a determinant of athletic success.
Cronin et al.[40] assessed the strength, power and
explosive strength of the leg musculature on a supine squat and related these measures to lunge performance (lunge foot contact times: 0.354 sec +
0.063). The only significant strength predictors of
lunge performance were all explosive strength measures (index of explosive strength [r = 0.62], reactivity coefficient [r = 0.61], starting gradient [r = 0.69]
and acceleration gradient [r = 0.59]). Maximal
strength, mean and peak power were not significantly related to performance of this movement.[40] In
another study, Sleivert and Taingahue[25] investigated the relationship between sprint start performance
(5m sprint time) and strength and power variables
determined from concentric jump-squats in 30 male
athletes. Both average and peak power expressed
relative to body mass were significantly related to
5m sprint time (r = –0.64 to –0.68). Force (r = 0.59)
and bar velocity (r = 0.40) were also significantly
related to 5m sprint time. However, with the exception of peak force, these relationships were much
less substantial than those demonstrated for average
and peak power. During the sprint start, the body
had to be accelerated rapidly from stationary and the
propulsive impulse was large, so there may have
been a greater reliance on high force production as
opposed to high movement velocity.
Young et al.[56] using a similar methodology to
Wilson et al.[55] investigated the relationship between 27 strength and power measures and the
sprinting performance of 20 elite junior track and
field athletes (11 males and 9 females). There was
no mention whether the pooling of the male and
female subjects were investigated for bipolar distribution (by sex), which can result in artificially high
correlations. Therefore, the magnitude of the correlations need to be interpreted with caution. Young
and colleagues[56] assessed vertical jumping movements utilising purely concentric, SSC and isometric
contractions performed over a force platform, found
Sports Med 2005; 35 (3)
226
Cronin & Sleivert
that concentric strength measures were the best
predictors of sprint performance. The best predictors
of starting performance (time to 2.5m) were all
obtained from a concentric-only jump-squat and
included three force measures (maximum dynamic
strength/weight [r = –0.86], force in 100 ms/weight
[r = –0.73], maximum force [r = –0.72] and one
power measure (average power/weight [r = –0.74]).
The single best predictors of maximum sprinting
speed were the force relative to bodyweight generated after 100ms from the start of the concentric jump
movement (r = –0.80) and maximum force (r =
–0.79). Average power relative to bodyweight was
also strongly related to maximum velocity (r =
–0.79). Together, the findings of these studies suggest that for many types of activities, various rates of
force development and maximal force measures
may be as predictive of performance as measures of
maximal power. The magnitude of these relationships appear to depend upon the kinetics and kinematics of the performance in question, and specificity of neuromuscular demand appears to be a critical
factor in determining the strength or power attribute
that best predicts performance. It should be remembered, however, that just because a strength or power attribute is related to performance, that does not
necessarily indicate that training that particular attribute will enhance performance.
3.2.4 Summary
Implicit in the small number of meaningful correlations and large amount of unexplained variance in
the data of table IV is that sport scientists need to
formulate better methods, models and theories to
contribute significantly to knowledge that is useful
to athletes and their coaches in terms of power and
performance. It has been suggested that sport scientists adopt a specific functional term to denote the
ability to perform high-velocity explosive forcegenerating movements and limit the term ‘power’ to
its correct usage as defined by Newtonian mechanics.[36] Whether this is necessary is debatable. It may
be that the preoccupation of correlational studies to
find the best power predictors of functional performance is fundamentally flawed. First, one power measure cannot adequately express or provide insight
 2005 Adis Data Information BV. All rights reserved.
into all the mechanisms responsible for performance
of a task. Secondly, other factors such as strength
measures, body mass, flexibility and leg length will
have diverse effects on the statistical models. Based
on these results, it is suggested that the sports trainer, sport scientist or clinician should not rely solely
on a single power measurement to predict performance or readiness to return to activity after injury.
Rather, research needs to determine the influence of
these other factors on athletic performance. It may
be that several factors in combination with power
measures will provide the best predictive capabilities of functional performance. Therefore, the challenge is to develop assessment batteries that provide
insights into the key mechanisms responsible for the
performance of a task. It must also be remembered
that a significant relationship to performance does
not imply that a particular strength or power attribute, when loaded during training, will enhance
performance. Only longitudinal data from training
studies can provide information regarding optimal
training methods, if indeed optimal methods exist.
4. Power and Performance:
Training Studies
Research that has investigated the development
of power is typified by a great deal of variation in
the methodological approaches used. The scope of
this variation makes comparisons difficult and
hence definitive conclusions practically impossible.
For example, the vast majority of research has been
relatively short in duration (8–12 weeks) and, therefore, the application of findings to long-term training is questionable as the influence of neural and
morphological mechanisms change with training
duration.[57] Research in this area is also typified by
a wide spectrum of loading parameters that include
differences in:
• volume
• intensity (% 1RM)
• total work output
• tempo of concentric-eccentric contractions
• frequency
• rest/recovery time – density
• type of contractions.
Sports Med 2005; 35 (3)
Maximal Power Training and Improving Athletic Performance
227
Further confounding our understanding in this
area are the modes of dynamometry used and variety
of strength power measures reported. These issues
have been discussed in section 3. Finally, many
different muscle groups have been studied, limiting
the ability to generalise results, especially in the case
of uni- and bi-articular muscles. To discuss each of
these limitations is outside the scope of this article.
However, a brief mention will be made of some
issues that are thought influential to subsequent discussion.
with plyometric training on a variety of performance
measures. Conversely Komi et al.[61] compared the
effect of heavy-load training versus light-load training combined with explosive jump training on various performance measures. Any performance
changes, however, cannot be attributed to a load
effect as one treatment group has incorporated additional plyometric training. Unfortunately this type of
methodology is not uncommon and as such does not
offer insight into specific loading intensities that
affect power and performance.
First, a clear understanding of each research
methodology needs to be realised, so as the interpretation and application of their findings are not misrepresented. For example, the study of Schmidtbleicher and Buerhle[38] compared the effects of
three types of training regimes (maximum load
[90–100% maximal voluntary contraction, MVC],
power load [45% MVC] and hypertrophy load [70%
MVC]) on various neuromechanical and morphological variables. In terms of tracking these changes,
an isolated isometric elbow extension movement
was used. Consequently, power was not assessed as
no movement took place. Furthermore, isometric
and dynamic contractions have been shown to differ
in terms of their physiology and neuromechanics.[58]
Similarly, the findings of Hakkinen et al.[59] are used
to support the use of light-load explosive jump training for power development. However, there were no
other training or control groups cited in this paper
and the changes in performance from the 24 weeks
of training were tracked by measuring isometric
force and various force-time parameters. These articles, however, are often quoted in power training
literature even though power has not been measured.
The application of such findings for improving the
power of multi-articular dynamic motion would appear problematic on a number of counts.
Further confounding our understanding of the
effects of resistance training on the development of
power is the preponderance of research using novice
weight trainers or students as subjects (see table V).
This is due to the accessibility of such subjects to
researchers. Novice subjects and/or student populations are generally easy to access and it is easy to
have a control group in such designs. However, it is
more problematic to find a suitable cohort of subjects from an athletic population and practically
impossible to ask a group of athletes to cease training and act as controls. Nonetheless, it has been
shown that novices respond in a generic manner to a
very broad range of resistance training stimuli.[62,63]
Thus, the validity of generalising findings from novice subjects to athletes with experience in weight
training needs to be done so with caution as the
findings may in fact be compromised by the
trainability of the novice subjects.
Another common methodological problem when
studying the effect of load is that authors combine
training methods, which make the effects of the
independent variable impossible to disentangle. For
example, Lyttle et al.[60] investigated the effects of
maximal power training (30% 1RM) versus heavyload training (6–10RM or 75–83% 1RM) combined
 2005 Adis Data Information BV. All rights reserved.
Volume is commonly expressed as the total product of repetitions, sets and load (% 1RM). Equating
by volume is the most common method by which
research compares the effect of load on various
outcome measures. Alternative methods include
equating with total time under tension as measured
by electromyographic (EMG) activity or total
mechanical work performed.[67] A great deal of research has failed to equate loading between training
protocols in any form[38,65,69,70] and, as a result, the
findings of such research must also be interpreted
with caution especially if the effect of load is being
investigated. For example, Harris et al.[65] studied
the effects of different training protocols on well
trained subjects. One group used heavy-load squats
Sports Med 2005; 35 (3)
228
Cronin & Sleivert
Table V. Studies that have examined the effect of load on power output and performance
Study
Subjects
Cronin et al.[64]
21 F provincial netball players (1) 60% 1RM
with no recent weight-training (2) 80% 1RM
experience
(3) Control
Equivolume training for 10wk
Training loads used
Mean and peak power
Netball throw velocity
Power and performance measures
Harris et al.[65]
51 university football players
with at least 1y of weighttraining experience
(1) 30% 1RM
(2) 80% 1RM
(3) Com 30% and 80% 1RM
Not equivolume – 13wk
Average VJ power
Peak VJ power
Standing long jump
MK step test
Agility test
30m sprint
McBride et al.[66]
26 athletic men with 2–4y
strength-training experience
(1) 30% 1RM
(2) 80% 1RM
(3) Control
Equivolume training for 8wk
Peak power
20m sprint time
Agility T-test
Moss et al.[67]
31 well trained PE students
(1) 15% 1RM
(2) 35% 1RM
(3) 90% 1RM
Equivolume training for 9wk
Power-load spectrum
Scmidtbleicher and
Haralambie[68]
30 M PE students
(1) 30% MVC
(2) 90–100% MVC
(3) Control
Equivolume training for 8wk
No power measures
Maximal speed of push-off movement
Schmidtbleicher and
Buehrle[38]
59 M students
(1) 45% MVC
(2) 70% MVC
(3) 90–100% MVC
Not equivolume – 12wk
No power measures
No performance measures
Wilson et al.[69]
64 subjects with 1y weighttraining experience
(1) 30% 1RM
No measures of power
(2) 6–10RM = 75–83% 1RM
JS Ht
(3) Plyometric
CMJ Ht
(4) Control
Sprint and cycle tests
Not equivolume – 10wk
1RM = one repetition maximum; CMJ = counter-movement jump; Com = combined training; F = females; Ht = height; JS = jump-squat; M =
males; MK = Margaria-Kalamen step test; MVC = maximal voluntary contraction; PE = physical education; VJ = vertical jump.
of 80–85% 1RM (high force [HF]), one with lighter
load squats of approximately 30% 1RM (high power
[HP]), and one with a combination (COM) of training methods. Subjects were instructed to perform all
lifts as explosively as possible although not actually
jump. Pre- and post-training tests included: 1RM
squat, 1RM one-quarter squat, 1RM mid-thigh pull
and various tests of speed and jump height. After 9
weeks of training it was observed that performance
changes reflected contraction force specificity. That
is, the COM and HF groups increased 1RM strength
significantly more than the HP group (24.7%, 21.9%
and 9.6% for COM and HF and HP, respectively,
average increase across squat and one-quarter squat
1RM). However, for some low-force, high-velocity
measures of functional performance such as the
 2005 Adis Data Information BV. All rights reserved.
standing long jump, significantly greater increases
in the HP (3.45%) group than COM (1.63%) or HF
(1.29%) groups were observed. These differential
training effects are interesting; however, it may be
that the noted training effects were not due to the
different kinematics and kinetics characteristics of
the various training protocols, but rather the different training volumes of each group.
Wilson et al.[69] is an often-quoted study that
experiences the same problem. These researchers
examined the effects of three different training
modes on strength and power outputs over the
course of 10 weeks of training. Previously trained
subjects were divided into three subject groups. One
trained with traditional ‘heavy’ (6–10RM) squats
(TR), one with jump-squats at Pmax (around 30% of
Sports Med 2005; 35 (3)
Maximal Power Training and Improving Athletic Performance
maximum isometric force [MP]) and one with plyometric unloaded depth jumps (0.2–0.8m [PL]).
Tests before and after the training period included
sprint time, vertical jump height and peak power in a
6-second cycle test. The results indicated that the
MP training group demonstrated the best overall
improvements in functional performance. Increases
in countermovement jumps (17.6%) and jumpsquats (15.2%) were significantly greater than the
TR (4.8% and 6.3%) and PL (10.3% and 6.5%)
groups. They concluded after a training study that
compared heavy weight training (6–10RM), plyometric training (drop jumps from 0.2–0.8m) and
maximum power training (30% 1RM) that the Pmax
training produced the best overall results.[69] Once
more, intensity and volume have not been equated
and, therefore, it is impossible to disentangle the
training effects. That is, the results from such studies
are difficult to interpret as the reported differences
between various training protocols may in fact be
contaminated by differences in training volume,
rather than the specific kinematic and kinetic characteristics of the different loading intensities. Making conclusions about the efficacy and/or adaptations of various training protocols that are not equated in some manner would appear highly
questionable.
Further complicating our understanding in this
area is that changes in performance may be related
to the movement pattern used rather than differences
in loading intensity. For example, in the Wilson et
al.[69] article, it may be that the superior performance
of the maximum power groups was not due to training at the ‘optimal load’ but rather that jump-squat
training (ballistic training) offers greater movement
pattern specificity than more traditional strengthtraining methods. Traditional strength-training techniques in which a bar is held at the completion of the
motion have been criticised due to large decelerations during the concentric phase, proportional to
the load and, therefore, velocity of movement.[32,71]
Strength training that allows the projection of the
load avoids this problem by allowing the athlete to
accelerate the bar throughout a greater range of
movement. Such training has been described as ‘bal 2005 Adis Data Information BV. All rights reserved.
229
listic’ strength training[35] and is thought superior to
traditional strength-training methods as the velocity
and acceleration/deceleration profiles better approximate the explosive movements used in athletic performance.[21,32]
Careful consideration also needs to be given to
the type of isoinertial assessment used in training
studies, as assessments must balance between being
specific to the functional task whilst being sufficiently different from training so that it does not
advantage one of the training groups. For example,
it can reasonably be expected that a group training
with heavy loads would achieve greater improvements in 1RM than a group training with lighter
loads. Therefore, assessments should either include
tests across a range of loads or at a ‘neutral’ load that
will not bias the results for any particular group.
Finally, all the papers cited in table V experience
a basic but fundamental problem if the effect of
training at Pmax is to be established. No paper has
established Pmax for their respective subjects, the
training load selected has been based on previous
research. The perils of such an approach have been
discussed throughout this paper with many findings
such as Edgerton et al.,[11] Faulkner et al.,[12] Kaneko
et al.,[13] Schmidtbleicher and Buehrle,[38] misinterpreted and/or misrepresented. The dangers of such
an approach are further reinforced if tables I and II
are observed, that is which load (% 1RM) should be
used as Pmax for a training study. If the effects of
Pmax training are to be understood, Pmax needs to be
determined specific to the population and training
exercise used. The power loads selected for study
have mostly been based on research (e.g. Kaneko et
al.[13]) that cites the superiority of lighter loads
(30–45% 1RM). Tables I and II and previous discussions of research such as that of Kaneko et al.[13]
would suggest that this is not advisable practice.
Only one study[64] based their selection of Pmax on
their own previous research using similar athletes
and assessment equipment to their original research.[16] In this study, the effects of heavy-load
training (80% 1RM) and maximum power training
(60% 1RM) on the power and chest-pass performance of semi-elite netball players were compared.
Sports Med 2005; 35 (3)
230
Cronin & Sleivert
Significantly greater mean and peak power outputs
(40% 1RM bench-press throw) were observed for
the heavy-load group; however, no significant differences in chest-pass throw velocity between training groups were found. However, it should be noted
that the players had little or no weight-training experience.
The types of problems described in this section
are symptomatic of research in this area, the reader
having to be discerning in their choice of literature
to shape their knowledge and practice. To gain a true
appreciation of the effect of load on power development, research methodologies need to equate the
load lifted in some manner. Ideally, well trained
subjects should be trained at different loads (%
1RM), the effects of which need to be reported as
changes in mean and/or peak power and/or changes
in functional performance. Unfortunately, the number of studies that have adopted such an approach
are few (see table V) and, as mentioned previously,
none of these studies determined or trained at Pmax.
Therefore, the importance of Pmax remains a mystery. Nonetheless, two studies seem to have resolved
some of the limitations highlighted in previous sections and may offer insights into optimising the load
selected during resistance training strategies to develop maximal power and enhance performance.
[67]
Moss et al. had three groups of subjects train
the elbow flexors of the non-dominant arm while the
dominant arm served as a control. The three groups
trained at 90% 1RM for two repetitions (G90), at
35% 1RM for seven repetitions (G35) and at 15%
1RM for 15 repetitions (G15), respectively. All
groups trained with three to five sets and were
equated for total time under tension based on EMG
activity (muscle activation). Subjects were encouraged to perform each lift as fast as possible, but
the weight was held rather than projected. Measurements before and after the study included 1RM and
various force and power outputs. Power was tested
across a range of loads from 2.5kg to 90% 1RM and
increased for all loads tested in G90 and G35. An
increase in a lighter range of loads from 15% to 50%
1RM was observed for G15. The increases in power
output were not significantly different between the
 2005 Adis Data Information BV. All rights reserved.
three groups at loads ≤50% 1RM but for the higher
loads (70% and 90% 1RM) the increase was significantly larger for G90 and G35 compared with G15.
In essence, the findings illustrate loads of 35% and
90% 1RM were equally effective in improving power output across a spectrum of loads.
Another study investigating the effects of heavy(80% 1RM) versus light-load (30% 1RM) training
on strength, power and speed in experienced weight
trainers was conducted by McBride et al.[66] Jumpsquats were used for both training and testing, and
groups were equated for volume using sets × repetitions × load (%RM). After 8 weeks of training, both
groups increased 1RM significantly (10.17% and
8.23% for 80% 1RM and 30% 1RM groups, respectively) with no significant difference between
groups. Similarly, there were no significant between-group differences in peak force, peak power
or jump height pre- and post-training. Of the three
sprint (5, 10 and 20m sprint time) and agility (T-test
time) measures, the 30% 1RM training proved superior to 80% 1RM training on only one measure (10m
sprint time). The results of these two studies suggest
that there is very little difference in the effects of
heavy- and light-load training in terms of power and
performance.
The lack of a differential training effect in these
studies using quite different loads may be explained
if training velocity and actual movement velocity of
a task are compared. For example, the velocities
attained during a concentric-only bench-press throw
and a rebound bench-press throw on a modified
Smith-Press machine can be observed from figure
1.[64] Such motion has been reported to more closely
simulate the velocity and acceleration profiles associated with throwing.[32] The velocity profiles obtained from these 27 male athletes are similar to
those recorded in other research of this kind.[9,32]
For sports-specific motion that uses muscles similar to the bench press the comparison of actual
movement velocity to training velocity make for
interesting analysis. Ritzdorf[72] details release velocities of 13 m/sec for the shot-put motion. Average
release velocities of 11.98 m/sec for the chest pass
of semi-elite female netball players have also been
Sports Med 2005; 35 (3)
Maximal Power Training and Improving Athletic Performance
231
1.1
Concentric bench-press throw
Rebound bench-press throw
1.0
Average velocity (m/sec)
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
30
40
50
60
70
80
Percentage 1RM
Fig. 1. Average velocities and standard deviations associated with the concentric bench-press throw and the rebound bench-press throw
over a range of loading intensities (30–80% one repetition maximum [1RM]).
reported.[64] Furthermore, other studies (as reported
by Ritzdorf[72]) have reported that the segment velocities (shoulder, elbow and hand) combined to
produce racquet head velocities of approximately 30
m/sec for the tennis serve. It would seem that there
are clear differences between the velocities associated with the most common strength-training loading
intensities (30–80% 1RM) and the actual movement
velocity of a sport-specific task. There is no doubt
that resistance strength training can improve functional performance. However, the importance of
load in terms of velocity specificity would appear
questionable, due to the disparity between the actual
movement velocities of most athletic tasks and the
training velocities achieved during weight training.
It may be that load is not as important as many think
and other factors may be more important to better
develop and explain improvement to functional performance. For example, as suggested in section 3, it
may be that rate of force development rather than
power output is a more important determinant of
athletic success. Newton and Kraemer[35] stated that
heavy loading (70–120% 1RM) does not improve
the maximum rate of force development, rather exercises such as explosive jump training using resistances of 30–60% 1RM best increase the ability to
 2005 Adis Data Information BV. All rights reserved.
rapidly develop force. This assertion must be interpreted cautiously as Schmidtbleicher and Buehrle[38]
found the greatest improvements in RFD resulted
from maximum load training (90–100% MVC) as
opposed to maximum power (45% MVC) training
(34% and 11% improvements, respectively). It
should be remembered that rate of force development can be independent of external movement velocity. In addition, maximal rate of force development and motor unit activation in a maximum effort
contraction is relatively constant for an individual
and is generally not influenced by the external load
or speed of movement.[73] In Schmidtbleicher and
Buehrle’s study[38] both groups were instructed to
develop their contractions as fast as possible. It may
be that irrespective of load and limb velocity, the
repeated intent to move ‘explosively’ is the important stimulus for muscular adaptation. The findings
of Behm and Sale[74] support such a notion, as they
found that regardless of the actual velocity of movement (isometric versus isokinetic), it was the intention to execute a high-velocity movement, which
resulted in high-velocity adaptation and substantial
increases (26%) in rate of force development. Along
these lines, it has been recently shown with surface
electromyography, that at loads between 30–80%
Sports Med 2005; 35 (3)
232
Cronin & Sleivert
1RM, power, force and velocity values differ, but
muscular activation is similar during maximal effort
ballistic jump-squats.[19] Thus, when the intent to
move is maximal, any differences in training effects
due to loading differences are likely due to differences in the mechanical stimuli at the muscle, not
activation differences.
5. Conclusions
Many problems have been magnified throughout
this article that currently exist within this field of
study. From the use of fundamental definitions and
terminology to training studies, the literature appears hewn with confusion and methodological
problems. It is hoped that this article has eliminated
some of this confusion and clarified the type of
research needed if we are to advance our knowledge
and practice on the importance of maximising power
output and its transference to athletic performance.
Sport scientists are urged to formulate research designs that result in meaningful and practical information that assists coaches and strength and conditioning practitioners in the development of their
athletes.
In summary, it is apparent there is a need for a
great deal more research on the importance of training at Pmax and whether it advantages athletic performance over and above other loading intensities.
First, the importance of Pmax to athletic performance
needs to be established. It must be remembered that
power is only one aspect that affects performance
and it is quite likely that other strength measures
may be equally if not more important for determining the success of certain tasks. The coach and/or
sport scientist must be aware of this and identify
those strength qualities that are critical determinants
of their athletic activity and thereafter devise appropriate assessment strategies and training programmes so as these determinants are improved in a
systematic fashion. It is suggested that instead of
using a correlational analysis that a regression approach be used to find predictor models that include
other anthropometric and physiological (including
other strength qualities) measures. As such, a battery
of two or more tests may predict athletic perform 2005 Adis Data Information BV. All rights reserved.
ance to better effect and direct assessment and conditioning practice in a more efficient and systematic
fashion.
If Pmax is found to be important, then each individual’s Pmax needs to be determined and they then
train at this load. The predilection of research to
train all subjects at one load (e.g. 30% 1RM) is
fundamentally flawed due to inter-individual Pmax
differences, which may be ascribed to factors such
as training status (strength level) and the exercise
(muscle groups) used. Pmax needs to be constantly
monitored and adjusted as research suggests that it is
transient. In terms of training studies, experienced
subjects should be used, volume equated and the
outcome measures clearly defined and measured
(i.e. mean power and/or peak power).
Until training studies address the limitations discussed throughout this paper, the best and safest
course of action for those interested in improving
the power output of muscle may be to use a mixed
training strategy using both heavy and light loads.
Realising that all human movement is an integration
of force and velocity, such an approach is intuitively
appealing. That is, most sports involve a mixture of
activities that span the force-velocity capability of
muscle. For example, the shot-putter has to drive
their quite sizeable mass through the circle before
throwing a relatively light shot-put. Rugby players
not only have to wrestle and tackle each other but
also kick and throw a ball. Intuitively, it would seem
prudent to continuously adjust the resistances used
for power training, as athletic performance is typified by many force-velocity characteristics. Furthermore, as one of the principles of training is variation
(periodisation) this approach would seem most logical.
Acknowledgements
No sources of funding were used to assist in the preparation of this review. The authors have no conflicts of interest
that are directly relevant to the content of this review.
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Correspondence and offprints: Assoc. Prof. John Cronin,
New Zealand Institute of Sport and Recreation Research,
Auckland University of Technology, Private Bag 92006,
Auckland 1020, New Zealand.
E-mail: [email protected]
Sports Med 2005; 35 (3)