ANTHROPOMETRY AS A PREDICTOR OF VERTICAL
JUMP HEIGHTS DERIVED FROM AN INSTRUMENTED
PLATFORM
JOHN F. CARUSO,1 JEREMY S. DAILY,2 MELISSA L. MASON,1 CATHERINE M. SHEPHERD,1
JESSICA R. MCLAGAN,1 MALLORY R. MARSHALL,1 RON H. WALKER,1 AND JASON O. WEST1
1
2
Exercise Physiology Laboratory, Exercise and Sports Science Program, The University of Tulsa, Tulsa, Oklahoma; and
Department of Mechanical Engineering, The University of Tulsa, Tulsa, Oklahoma
ABSTRACT
Caruso, JF, Daily, JS, Mason, ML, Shepherd, CM, McLagan, JR,
Marshall, MR, Walker, RH, and West, JO. Anthropometry as
a predictor of vertical jump heights derived from an instrumented platform. J Strength Cond Res 26(1): 284–292,
2012—The current study purpose examined the vertical heightanthropometry relationship with jump data obtained from an
instrumented platform. Our methods required college-aged (n
= 177) subjects to make 3 visits to our laboratory to measure
the following anthropometric variables: height, body mass,
upper arm length (UAL), lower arm length, upper leg length, and
lower leg length. Per jump, maximum height was measured in 3
ways: from the subjects’ takeoff, hang times, and as they landed
on the platform. Standard multivariate regression assessed how
well anthropometry predicted the criterion variance per gender
(men, women, pooled) and jump height method (takeoff, hang
time, landing) combination. Z-scores indicated that small
amounts of the total data were outliers. The results showed
that the majority of outliers were from jump heights calculated
as women landed on the platform. With the genders pooled,
anthropometry predicted a significant (p , 0.05) amount of
variance from jump heights calculated from both takeoff and
hang time. The anthropometry-vertical jump relationship was
not significant from heights calculated as subjects landed on
the platform, likely due to the female outliers. Yet anthropometric data of men did predict a significant amount of variance
from heights calculated when they landed on the platform;
univariate correlations of men’s data revealed that UAL was the
best predictor. It was concluded that the large sample of men’s
data led to greater data heterogeneity and a higher univariate
correlation. Because of our sample size and data heterogeneity,
practical applications suggest that coaches may find our results
Address correspondence to Dr. John Caruso, [email protected].
26(1)/284–292
Journal of Strength and Conditioning Research
Ó 2012 National Strength and Conditioning Association
284
the
best predict performance for a variety of college-aged athletes
and vertical jump enthusiasts.
KEY WORDS standard multivariate regression, upper arm
length, data heterogeneity
INTRODUCTION
M
aximum vertical jump height foretells success
for many athletic endeavors (3). The prediction of maximum heights attained from
vertical jumps is dependent upon multiple
variables. Historically, anthropometry, or the measurement
of body dimensions, was a good predictor of vertical jump
heights (18,21). Generally speaking, anthropometric variables have yielded positive correlations to vertical jump
performances, because persons who possessed greater body
and limb measurements (such as athletes) achieved higher
maximum heights (13). Many studies that examined the
vertical jump-anthropometry relationship measured maximum heights with a Vertec (19,20,25). However, jump
heights measured with a Vertec inherently possess some
variability (4). The Vertec is equipped with a series of
slapsticks set 1.27 cm apart. Yet, when athletes use a Vertec to
determine their maximum jump performance, their actual
vertical height attained may reside between slapsticks, and
thus, measurements may entail some error that skews the
vertical jump-anthropometry relationship. Given the importance of the vertical jump in athletics (17,21), a more accurate
method to measure maximum heights is warranted.
More accurate methods to quantify maximum vertical
jump heights include a newly created instrumented platform
(4). The platform is shaped like an isosceles triangle with
a calibrated load cell embedded into each corner. To measure
jump heights, the platform operates in a manner similar to
that of a commercial force plate but is far less expensive to
fabricate. One platform feature not possessed by commercial
force plates is its ability to capture an independent signal with
each of its 3 load cells. When the signals are combined with
the geometry of the platform, a person’s center of mass may
be detected and provides insights into the jumper’s technique
TM
Journal of Strength and Conditioning Research
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
the
TM
Journal of Strength and Conditioning Research
as they begin their takeoff. As subjects perform vertical
jumps, they takeoff from and land upon the platform. On-line
data collection produces a characteristic waveform or jump
signature associated with each subject’s effort and offers the
athlete instant visual feedback on their performance. For each
jump, the instrumented platform measures the maximum
vertical height attained in 3 ways: as subjects take off from the
platform, the amount of time spent in the air (hang times),
and when they land on the device. High levels of data
reliability and reproducibility were previously verified for
jump heights measured by using the platform (4,5).
Given its level of technology and sophistication, the
platform (Figure 1) may yield more accurate vertical jump
height values than a Vertec or a commercial force plate does.
In turn, a more accurate measurement device such as the
platform may alter what we know about the vertical jumpanthropometry relationship and yield more precise prediction equations. Another weakness with prior studies that
examined the jump height-anthropometry relationship is
a sample that comprised only one gender or a small size
(1,22,24). Results from such studies, and inferences that can
| www.nsca-jscr.org
be drawn, are limited by a lack of data heterogeneity.
Because of the importance of inclusion of data from both
genders, the purpose of this study is to assess the vertical
jump height-anthropometry relationship with instrumented
platform data obtained from men and women. The results
may thus reflect a more accurate relationship among
criterion and predictor variables. Current criterion variables
included 3 different height measurement (from takeoff,
hang times, and landing) methods and gender (men,
women, pooled) combinations. Six (height, body mass,
UAL, lower arm length [LAL], upper and lower leg lengths
[ULL, LLL]) anthropometric indices served as our predictor variables. We hypothesize that results will yield
significant correlations for the vertical jump heightanthropometry relationship.
METHODS
Experimental Approach to the Problem
To assess the vertical jump height-anthropometry relationship from instrumented platform data, the subjects (n = 177)
made 3 visits to our laboratory. Per subject, they completed
data collection within 3 weeks
of starting the project. Each
subject’s 3 visits took place at
the same time of the day. All
data were obtained from the
Fall and Spring academic semesters. For greater knowledge
of the vertical jump heightanthropometry
relationship,
we included a larger and more
heterogeneous sample than did
prior research in this area
(1,24).
Subjects
Figure 1. Superior (top) and inferior (below) illustrations of the triangular-shaped platform. The top illustration
depicts load cell placement; the bottom illustration shows the length of each side of the platform.
Before the testing sessions, all
the subjects gave informed
written consent for their project
participation. The project first
received approval for the use of
human subjects from a university-based Institutional Review
Board. Subjects were college
aged and in good health. Regarding their training backgrounds, 103 (55 men, 48
women) were varsity student
athletes, 40 (20 men, 20 women)
regularly engaged in some form
of exercise, and 34 (24 men,
10 women) were sedentary.
Subjects’ first laboratory visit
entailed anthropometric data
collection and familiarization
VOLUME 26 | NUMBER 1 | JANUARY 2012 |
285
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Anthropometry and the Vertical Jump
with vertical jumps done on the platform. For the second and
third visits, the subjects performed a series of vertical jumps, and
their data were used for current multivariate regression
analyses, on the platform. Thus, our experimental approach
enabled the assessment of the vertical jump height-anthropometry relationship.
Procedures
To assess the ability of anthropometry to predict the variance
in jump heights derived from the instrumented platform,
healthy college-aged subjects (99 men, 78 women) attended
3 jump sessions each spaced 2–7 days apart.
Platform Design and Instrumentation
The platform uses 3 shear beam load cells, bolted to a steel
reinforced triangular frame, to measure compressive forces.
The frame includes 2 metal c-channel beams, separated
by 6.4 cm, which run parallel along each of the 3 sides to
give the platform its triangular shape. Each side of the
triangular frame has a length and depth of 98 and 11 cm,
respectively. A 1.9-cm-thick sheet of plywood covers the
frame and adheres to the metal substructure with polyurethane glues. This fabrication technique yields a stiff
platform that naturally dampens and limits unwanted
vibration as the subjects land. At each corner of the triangle,
between the parallel beams, resides a 114-kg capacity strain
gage–based load cell (NTEP grade III) that measures
changes in resistance as jumpers’ take off from, and land
on, the platform. Resistance changes are converted into
voltage as the bridge circuit is excited. Per load cell,
a 4-channel, 24-bit full bridge analog input board (model
NI9237; National Instruments, Austin, TX, USA) with
internal excitation receives the
voltage. Analog input boards
are mounted on a CompactDAQ USB chassis (cDAQ9172; National Instruments)
and collected by software
(Labview 8.6; Austin, TX,
USA) at 5,000 Hz to convert
waveform signals into numerical indices of jump height.
Load cell calibration was routinely verified with an object of
a known mass throughout data
collection. Software amplified
jump waveforms (Figure 2), so
they were captured within a
0–5 V range per load cell and
shown online to offer visual
feedback of the data.
Data Collection Procedures
Subjects’ first visits familiarized
them with the operation of the
platform and did not entail the
286
the
collection of vertical jump data. Rather, the purpose of the initial
session was to accustom the subjects to jumping on the platform.
Those sessions included a 5-minute stationary cycle ergometer
warm-up against 9.8 N at a self-selected velocity, followed by 3–5
vertical jumps performed at a submaximal level of effort. The
initial sessions also afforded research technicians practice in the
capture of real-time waveforms from the platform, because
online data collection permitted only a 3-second ‘‘window’’ to
obtain and assess subjects’ jump technique. If technicians felt that
more data collection practice was needed to accurately capture
a particular subject’s technique, more jumps were performed.
In addition to platform familiarization, the subject’s first
laboratory visits entailed anthropometric data collection. The
subjects stood in an upright posture as data collection
commenced with the measurement of their height. They
assumed the same posture when their weight was recorded
on a calibrated (Model 339, Detecto Scales, Webb City, MO,
USA) scale. In addition to height and weight determinations,
the subjects submitted to lower and upper limb length
measurements specific to the body segments engaged in the
vertical jump. The rationale to measure limb lengths is from
work that showed more high-speed variance is explained by
such variables (9). Limb length data were collected with
a cloth measurement tape from the right side of their bodies
as the subjects stood in a relaxed upright position. The ULL
was measured from the anterior superior iliac spine to the
patella base. The LLL equaled the distance between the head
of the fibula and lateral malleolus. Upper arm length (UAL)
was measured from the acromion-clavicular joint to the
ulna’s olecranon process. Lower arm length (LAL) equaled
the distance between the ulna’s olecranon and styloid
Figure 2. A time history of a performed by 1 of our subjects shows the 3 distinct phases: takeoff, hang time, and
landing. Forces to the platform approximated 1,000 N as the subject stood motionless. Hatched areas under the
curves represent the takeoff and landing impulses.
TM
Journal of Strength and Conditioning Research
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
the
TM
Journal of Strength and Conditioning Research
processes. Per subject, limb lengths were measured in
triplicate and averaged prior to statistical analyses.
Per subject, the final 2 laboratory visits entailed vertical
jump height data collection. Preceded by a cycle ergometer
warm-up identical to that of their first laboratory visit, jumps
began as the subjects stood motionless on the platform. Upon
a vocal command from a research technician, the subjects
rapidly hyperextended their shoulders as they concurrently
flexed their ankle, knee, and hip and lumbar vertebral joints.
This rapid motion comprised a vertical jump countermovement that prestretched the engaged musculature and increased the maximal height attained (1,3,13,19). Immediately
after the countermovement, in one continuous motion, the
subjects exerted ankle, knee, and hip and lumbar extensor
torque to elevate their body as high as possible, as they
concurrently flexed one of their shoulder joints to raise one
arm overhead to its highest point. The subjects were
instructed to make contact with both their feet simultaneously on the platform upon descent. Per session, the
subjects performed 3–5 maximal-effort jumps to conclude
their final 2 laboratory visits.
To use force to measure jump height, Newton’s Second
Law of Motion states
Z
h¼
Z
| www.nsca-jscr.org
2
F dt =m
=ð2g Þ;
where m equals mass. Takeoff and landing impulses are
theoretically equal. Figure 3 shows the time derivatives of
displacement graphs describing the kinematics of uniform
acceleration. To measure peak displacement based on hang
time, it is recognized that the peak height occurs at half the
hang time as the jumper’s velocity equals zero. The common
uniform acceleration becomes h = ½ a(t/2)2 = gt2/8. In
addition to the above formulas, kinematic relationships,
which represent the hang time derivatives of displacement,
enabled us to calculate the maximum jump height attained by
using the platform. The platform software calculated
maximum jump height in real-time based upon data collected
from the subject’s takeoff, hang time, and as they landed.
Statistical Analyses
All data were first examined for the presence of statistical
outliers with Z-scores. The outliers were omitted from
further analyses. This study examined the vertical jump
height-anthropometry relationship with 9 criterion variables. Those variables included maximum platform jump
F ðt Þdt ¼ mD v;
R
where F(t)dt was the impulse
and mD 2 v was the momentum change. The impulse was
calculated from the numerical
integration of the net force
(measured force 2 body mass)
with the trapezoidal rule,
mass equaled weight divided
by gravity, and 2Dv was the
velocity change. Since jumps
occurred in an upright posture,
the velocity change is the takeoff velocity. Once Dv was
known, jump heights were calculated. Because the only external force on the subjects is
gravity while they are airborne,
jump heights used the kinematic
relationship for constant acceleration, namely,
vf2 ¼ v02 þ 2ah;
where the final velocity at the
apex was vf = 0, v0 = Dv, which
was the takeoff speed, and
a = g = 29.8 ms22 was the
acceleration due to gravity.
Combining these relationships
and solving for h yielded the
following:
Figure 3. Kinematic relationships for height (displacement), velocity, and acceleration.
VOLUME 26 | NUMBER 1 | JANUARY 2012 |
287
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Anthropometry and the Vertical Jump
TABLE 1. Descriptive (mean 6 SD) anthropometric and vertical jump height data.*
Women
UAL (cm)
LAL (cm)
ULL (cm)
LLL (cm)
Height (m)
Weight (kg)
Jump height measured from takeoff (cm)
Jump height measured from landing (cm)
Jump height measured from hang time (cm)
35.5
23.6
51.8
40.7
1.71
68.4
31.1
30.7
29.3
Men
6 3.1
6 2.4
6 3.8
6 4.0
6 0.08
6 10.2
6 7.5
6 8.0
6 8.0
38.0 6
25.6 6
54.6 6
43.3 6
1.83 6
86.5 6
45.2 6
44.6 6
41.5 6
Genders pooled
3.4
3.0
4.2
3.6
0.08
15.5
8.1
9.9
10.8
36.9 6 3.5
24.7 6 3.0
53.3 6 4.3
42.2 6 3.9
1.78 6 0.10
78.5 6 16.2
39.0 6 10.5
37.3 6 11.3
36.2 6 11.4
*UAL = upper arm length; LAL = lower arm length; ULL = upper leg length; LLL = lower leg length.
TABLE 2. Univariate correlations, ANOVA table, r, r2, SEME, and prediction equation with jump heights calculated from
takeoff (genders pooled) as the criterion.*†
UAL
LAL
ULL
LLL
Height
Body mass
Jump height from takeoff
Source
Regression
Residual
Total
UAL
LAL
ULL
LLL
Height
1
20.02
0.50
0.33
0.63
0.47
0.32
SS
5,686.2
16,476.7
22,162.8
1
0.39
0.46
0.45
0.41
0.23
df
6
170
176
1
0.28
0.68
0.48
0.29
MS
947.7
96.9
1
0.62
0.51
0.21
F
9.8
1
0.69
0.46
p
0.0001
Body mass
Jump height from takeoff‡
1
0.45
1
*UAL = upper arm length; LAL = lower arm length; ULL = upper leg length; LLL = lower leg length; ANOVA = analysis of variance;
SEME = standard error of multiple estimates.
†r = 0.51, r2 = 0.26, SEME = 9.7.
‡Jump heights from takeoff’ = 225.9 + 0.08(UAL) + 0.08(LAL) 2 0.10(ULL) 2 0.18(LLL) + 0.34(height) + 0.29(body mass).
heights calculated with different 3 measurements (from
takeoff, hang time, and landing) and gender (men, women,
pooled) combinations. Per criterion measure, 6 predictor
(height, body mass, ULL, LLL, UAL, and LAL) variables
attempted to explain the vertical jump height variance.
An a , 0.05 denoted statistical significance. Per significant
criterion measure, univariate correlations, r2, standard
error of multiple estimates and prediction equations were
provided.
RESULTS
Z-score analyses identified jump height outliers. With
heights calculated 3 ways per jump, our sample (n = 177)
led to a 531-item data set. Z-scores revealed 48 (9%) items as
outliers. Nearly all the outliers occurred from jump heights
288
the
measured when female subjects landed on the platform.
Recent research with the platform observed a similar effect
(4), yet this study has a smaller percentage of outliers than
the prior trial had (4). Table 1 shows the jump heights
obtained in this study. Our predictor variables explained
statistically significant amounts of variance for 3 of the 9
criterion indices examined. With genders pooled, the
vertical jump-anthropometry correlations from heights
measured as the subjects landed on the platform were not
significant. Yet, for jump heights assessed from takeoff
(Table 2) and hang time (Table 3), anthropometry predicted
a significant amount of variance. In Tables 2 and 3,
univariate correlations show that height and body mass
were the best predictors of jump height, while limb lengths
had a weaker relationship.
TM
Journal of Strength and Conditioning Research
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
the
TM
Journal of Strength and Conditioning Research
| www.nsca-jscr.org
TABLE 3. Univariate correlations, ANOVA table, r, r2, SEME, and prediction equation with jump heights calculated from
hang time (genders pooled) as the criterion.*†
UAL
LAL
ULL
LLL
Height
Body mass
Jump height from hang time‡
1
20.02
0.50
0.33
0.63
0.47
0.26
SS
1
0.39
0.46
0.45
0.41
0.15
df
1
0.28
0.68
0.48
0.20
MS
1
0.62
0.51
0.26
F
1
0.69
0.36
p
1
0.29
1
Regression
Residual
3,490.2
22,019.4
6
170
581.7
129.5
4.5
0.0003
Total
25,509.6
176
UAL
LAL
ULL
LLL
Height
Body mass
Jump height from hang time
Source
*UAL = upper arm length; LAL = lower arm length; ULL = upper leg length; LLL = lower leg length; ANOVA = analysis of variance;
SEME = standard error of multiple estimates.
†r = 0.37, r2 = 0.14, SEME = 11.2.
‡Jump heights from hang time = 233.7 2 0.003(UAL) 2 0.02(LAL) 2 0.07(ULL) + 0.05(LLL) + 0.33(height) + 0.08(body mass).
TABLE 4. Univariate correlations, ANOVA table, r, r2, SEME, and prediction equation with jump heights calculated from
landing (male data only) as the criterion.*†
UAL
LAL
ULL
LLL
Height
Body mass
Jump height from landing‡
1
0.03
0.47
0.19
0.56
0.34
0.49
SS
1
0.25
0.06
0.24
0.12
0.02
df
1
0.05
0.60
0.38
0.06
MS
1
0.47
0.49
0.03
F
1
0.58
0.17
p
1
0.25
1
Regression
Residual
8,668.5
17,334.1
6
71
1,444.7
244.1
5.9
0.0001
Total
26,002.6
77
UAL
LAL
ULL
LLL
Height
Body mass
Jump height from landing
Source
*UAL = upper arm length; LAL = lower arm length; ULL = upper leg length; LLL = lower leg length; SEME = standard error of multiple
estimates.
†r = 0.58, r2 = 0.33, SEME = 15.0.
‡Jump heights of men from landing = 13.5 + 0.61(UAL) + 0.08(LAL) 2 0.29(ULL) 2 0.17(LLL)20.09(height) + 0.28(body mass).
Analyses with data partitioned by gender provided novel
results. Female vertical jump-anthropometry relationships,
for heights calculated from takeoff, hang time and landing,
were not significant. Yet data analyses for men showed
a significant correlation because heights were measured from
landing on the platform (Table 4). Inter-gender discrepancies
were likely in part due to sample size differences, since most
of the omitted jump heights measured by landing on the
platform came from women. Table 4 shows anthropometry
explained for the largest amount (r2 = 0.33) of jump height
variance. In Table 4, univariate results show that UAL was
a good predictor (r = 0.49) of jump height variance of men,
measured from landing (8), nearly twice that of the body
mass (r = 0.25), the next best predictor.
VOLUME 26 | NUMBER 1 | JANUARY 2012 |
289
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Anthropometry and the Vertical Jump
DISCUSSION
Our results include somewhat new and novel outcomes not
seen in similar studies. With data from both genders pooled, in
Tables 2 and 3, univariate correlations show that jump
heights calculated from takeoff and hang time were each
strongly related to the subject’s body heights and masses.
Other studies noted similar effects. Multivariate regression
analysis of vertical jump data from 52 healthy college-aged
subjects revealed that body mass was the best predictor of the
criterion measure variance for the independent variables
examined (6). A study of male college athletes assessed
compared countermovement vertical jump performance
with indices of isometric and dynamic multijoint strength.
The results showed that absolute strength measures were
weakly correlated to jump performance (21). In contrast,
multijoint dynamic strength tests expressed relative to the
subject’s body mass had the strongest univariate correlations
to countermovement jump prowess (21). It was implied
measures expressed relative to body mass may better
predictors since, with their body mass as resistance; the
vertical jump is a dynamic skill that requires subjects to
engage multiple limb segments in a rapidly coordinated effort
to achieve maximum heights.
Anthropometry was examined as a correlate to the
maximum heights attained with various types of jumps
performed by 21 national-level volleyball players (25). Like
another study (21), predictor variables quantified relative to
body mass usually explained more jump height variance. Like
current results, in addition to body mass, Pearson coefficients
showed subjects’ heights correlated significantly to absolute
and relative countermovement depth and vertical jump
heights (25). Given these outcomes (21,25), perhaps current
univariate correlations may have been higher had predictor
variables been expressed relative to the subject’s body mass.
Finally, it should be noted that a recent study found height
and body mass to be weak predictors of vertical jumps
performed with a single leg (18). The reasons why this
outcome was unlike those for the current and prior (6,21,25)
studies is likely because of the type of subjects examined and
the procedures employed. The subjects may have been
unaccustomed to single-leg jumps, because data were
obtained from physical education students who did not
receive familiarization sessions before data collection, which
could have improved the correlation anthropometry had
with the performance measures (18).
Our study calculated maximum heights from 3 distinct
vertical jump (takeoff, hang time, landing) phases. With
multivariate regression and data from both genders, heights
calculated from subjects’ landing not only did not reach
significance but also produced the most statistical outliers.
Removal of the outliers and the associated anthropometric
data reduced our sample size and made it more difficult to
achieve significance. Outliers resulted in part from the impact
forces incurred by the platform as subjects landed, which may
290
the
have also compromised the jump height measurement.
Although measured in a manner similar to that of the heights
assessed from takeoff, a waveform (Figure 2) from one of our
heavier (.100 kg) subjects shows the peak impact to the
platform approximated 4,500 N. A prior study noted
somewhat similar impact forces (22). With 2 men and
women (mean body mass 75.5 6 6.6 kg), the participants
jumped from a 45-cm height onto a force platform (22).
Predicted peak forces ranged from 2,790 to 5,160 N and were
5–7 times the subject’s body mass (22). Given the differences
in body mass and the distance covered before and when
landing occurred, the current study impact forces are
comparable with the force platform results (22). For both
the current and prior studies, the impact forces upon landing
were a function of subjects’ body masses and the maximum
height they attained before landing. Unlike in the prior (22)
study, the subjects of this study landed on the platform from
a distance equal to their maximum jump height. In addition,
our subjects’ body masses were more varied than those of the
prior (22) study. Thus, the magnitude of the current impact
forces likely compromised the vertical height measurement
as subjects landed, and led this jump phase to have the only
nonsignificant relationship with anthropometry.
Due to their lighter body mass versus our male subjects, as
current study women landed on the platform it was presumed
they would evoke smaller impact forces. Yet, Z-score analyses
of jump heights measured as subjects landed showed that
women’s data had more outliers, often because some of their
jumps produced very high impact forces. Many factors,
including gender-based anatomical and biomechanical differences, may be responsible for both the outliers and high
ground reaction forces. Valgus lower limb alignments and
increased Q angles occur more often in women (2,11,12).
Although such anatomical measurements were not made in
this study, it is possible that they added to the number of
women’s outliers. In support of this statement, a study that
tracked vertical jump performance in men and women both
before and after puberty assessed how maturation elicits
anatomical and biomechanical gender differences that
impact jump prowess (23).
Force plate measurements showed with maturation males,
but not females, improved their vertical jump (23). Maturation also led to lower ground reaction forces in men as they
landed but not in women (23). Women also had more
dominant-to-nondominant leg disparities, which compromised synchronous motor unit activity and led to asymmetrical ground contact patterns (23). Similar effects (higher
ground reaction forces, leg asymmetrical landings) may have
occurred in this study to increase the prevalence of female
outlier data. A more extended knee angle is more common to
women as they land, which leads to a lack of mitigation in
ground reaction forces and imposes a greater deceleration
challenge as they land (7,10,14,23). Such a posture also causes
more knee stress and raises the risk of anterior cruciate
ligament injury (7,14). Finally, although conflicting data exist
TM
Journal of Strength and Conditioning Research
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
the
TM
Journal of Strength and Conditioning Research
on greater Q angles and anterior cruciate ligament injury risk
in women (11,12,23), it is possible that the former may have
also led to the higher outlier rates of this study. Yet, this is
purely speculative; more data are needed to confirm the
relationship between Q angle and outlier rates from jump
heights measured as subjects landed on the platform. Thus,
anatomical and biomechanical gender differences may have
each led to the higher prevalence of female outliers in this study.
Current female outliers led to a non-significant anthropometry-vertical jump relationship for heights measured
from landing. In contrast, a similar analysis with the data of
our male subjects was significant (Table 4). Univariate
correlations show that the best predictor of the variance in
men’s jump heights measured from landing was UAL
(r = 0.49), whereas those for height (r = 0.17) and weight
(r = 0.25) were far less. Table 4 shows that UAL had some
multicollinearity with height and weight, particularly the
former. Although prior research shows that arm swing
contributes up to 10% to maximum height, presumably by
creating more vertical acceleration and upward momentum
(13,16), little is known about UAL as a correlate to jump
performance. A recent study assessed relationships between
upper extremity anthropometry and jump height (24). With
a smaller (9 men, 8 women) sample, bivariate results showed
LAL (r = 0.51), but not UAL (r = 0.22), correlated
significantly to jump height (24). Yet, unlike the current
results, the prediction equation formulated from the LALjump height bivariate correlation was not significant (24).
With little data in support of a UAL-jump height correlation,
it appears that the best explanation for our current study
outcome was our sample size, which included greater
amounts of data heterogeneity.
Both the power and the ability to detect real and significant
effects is a function of sample size (17). Current jump heights
for men measured from landing also demonstrate considerable heterogeneity. In addition, in our Table 1, UAL data
show more heterogeneity than do similar measurements
(28.04 6 2.14 cm) collected by Reeves et al. (24). Analyses
with a smaller range of predictor and criterion variable scores
denote less heterogeneity and may underestimate the actual
correlation for the population under investigation (15). Thus,
our male subjects (n = 99) provided the current study with
more heterogeneity and in turn led to the strong UAL-jump
height univariate correlation in Table 4. More heterogeneity
raises the prediction capacity of statistical correlations by
providing a more representative sample of the population
from which the subjects were drawn. Thus, the sample size
and heterogeneity of our data pertaining to men offers the
most probable cause for the UAL-jump height univariate
correlation. However, multivariate regression results, such as
those for this study, should be interpreted with caution.
A limitation of this statistical tool is that it merely quantifies
the level of association among the predictor and criterion
variables and fails to offer a direct ‘‘cause and effect’’ for their
relationship. Future research in this area should examine
| www.nsca-jscr.org
other predictor variables, because the results in Tables 2–4
only explain a modest (r2 = 0.14–0.33) amount of variance for
the criterion measures.
PRACTICAL APPLICATIONS
The instrumented platform provides much of the information
that a commercial force plate offers but at a far smaller cost. In
addition, the instrumented platform has the ability to capture
up to 3 independent signals. Because of its low cost, the
instrumented platform of this study is a viable alternative, for
vertical jump training and testing, to commercial force plates.
One of the investigators of this study, whose expertise is in
mechanical engineering, created the instrumented platform
used in this study. Most of the materials used to construct the
platform were bought at a hardware store; the remainder
were purchased online and delivered to our university’s
mechanical engineering laboratory for fabrication. Thus, with
the purchase of materials with a rather modest cost and their
interface as described within our Methods section, the
instrumented platform may be replicated for use in
gymnasiums and training centers.
Prior research noted a high degree of vertical jump height
reliability between platform and Vertec values (4). In addition,
data derived from the instrumented platform over multiple
jump sessions were also deemed to have a high level of
reproducibility (5). The purpose of this study was to examine
the amount of maximum vertical jump height variance
accounted for by anthropometry. The current hypothesis
was affirmed, because anthropometry explained a significant
amount of criterion variable variance. The current results
may prove useful in the identification of athletes who have
the potential to excel in jumping, a skill critical to success in
many sports. Given the ages of our subjects and the data
heterogeneity, coaches and trainers may find that our results
best predict performance for a variety of college-aged
athletes and vertical jump enthusiasts.
ACKNOWLEDGMENTS
The authors wish to thank the subjects for their participation.
Support was in part provided through The University of Tulsa
Faculty Development Summer Fellowship Program. J.F.C.,
J.S.D., M.L.M., C.M.S., and J.R.M. participated in The
University of Tulsa’s Tulsa Undergraduate Research Challenge program.
REFERENCES
1. Aragon-Vargas, LF. Evaluation of four vertical jump tests: Methodology, reliability, validity, and accuracy. Meas Phys Ed Exerc Sci 4:
215–228, 2000.
2. Berns, GS, Hull, ML, and Patterson, HA. Strain in the anteromedial
bundle of the anterior cruciate ligament under combination loading.
J Orthop Res 10: 167–176, 1992.
3. Carlock, JM, Smith, SL, Hartman, MJ, Morris, RT, Ciroslan, DA,
Pierce, KC, Newton, RU, Harman, EA, Sands, WA, and Stone, MH.
The relationship between vertical jump power estimates and
weightlifting ability: A field-test approach. J Strength Cond Res 18:
534–539, 2004.
VOLUME 26 | NUMBER 1 | JANUARY 2012 |
291
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Anthropometry and the Vertical Jump
4. Caruso, JF, Daily, JS, McLagan, JR, Shepherd, CM, Olson, NM,
Marshall, MR, and Taylor, ST. Data reliability from an instrumented
vertical jump platform. J Strength Cond Res 24: 2799–2808, 2010.
5. Caruso, JF, Daily, JS, Olson, NM, McLagan, JR, Shepherd, CM,
Drummond, JL, Walker, RH, and West, JO. Vertical jump data
reproducibility from an instrumented platform. Isok Exerc Sci, in press.
6. Caruso, JF, Hernandez, DA, Schweickert, T, Saito, K, Hamill, JL, and
DeGarmo, N. A multivariate approach to predicting knee extensor
performance. J Strength Cond Res 17: 608–613, 2003.
7. Chappell, JD, Yu, B, Kirkendall, DT, and Garrett, WE. A comparison
of knee kinetics between male and female recreational athletes in
stop-jump tasks. Am J Sports Med 30: 261–267, 2002.
8. Cohen, J. Statistical Power Analysis (2nd ed.). Hillsdale, NJ: Erlbaum, 1988.
9. Dawson, MN, Nevill, ME, Lakomy, HK, Nevill, AM, and Hazeldine, RJ.
Modeling the relationship between isokinetic muscle strength and
sprint performance. J Sports Sci 16: 257–265, 1998.
10. Dufek, JS and Bates, BT. The evaluation and prediction of impact
forces during landings. Med Sci Sports Exerc 22: 370–377, 1990.
15. Keppel, G, Saufley, WH, and Tokunaga, H. Introduction to Design and
Analysis (2nd ed.).New York, NY: W.H. Freeman and Company, 1992.
16. Luhtanen, P and Komi, PV. Segmental contribution to forces in
vertical jump. Eur J Appl Physiol Occup Physiol 38: 181–188, 1978.
17. Markovic, G. Does plyometric training improve vertical jump height?
A meta-analytical review. Br J Sports Med 41: 349–355, 2007.
18. Meylan, C, McMaster, T, Cronin, J, Mohammad, NI, Rogers, C, and
deKlerk, M. Single-leg lateral, horizontal and vertical jump
assessment: Reliability, interrelationships, and ability to predict
sprint and change-of-direction performance. J Strength Cond Res 23:
1140–1147, 2009.
19. Moir, G, Button, C, Glaister, M, and Stone, MH. Influence of
familiarization on the reliability of vertical jump and acceleration
sprinting performance in physically active men. J Strength Cond Res
18: 276–280, 2004.
20. Moir, G, Shastri, P, and Connaboy, C. Intersession reliability of
vertical jump height in women and men. J Strength Cond Res 22:
1779–1784, 2008.
11. Gray, J, Taunton, JE, McKenzie, DC, Clement, DB, McConkey, JP,
and Davidson, RG. A survey of injuries to the anterior cruciate
ligament of the knee in female basketball players. Int J Sports Med 6:
314–316, 1985.
21. Nuzzo, JL, McBride, JM, Cormie, P, and McCaulley, GO.
Relationship between countermovement jump performance and
mulitjoint isometric and dynamic tests of strength. J Strength Cond
Res 22: 699–707, 2008.
12. Griffin, LY, Agel, J, Albohm, MJ, Arendt, EA, Dick, RW, Garrett, WE,
Hewett, TE, Huston, L, Ireland, L, Johnson, RJ, Kibler, B, Lephart, S,
Lewis, JL, Lindenfeld, TN, Mandelbaum, BR, Marchak, P, Teitz, CC,
and Wojyts, EM. Noncontact anterior cruciate ligament injuries:
Risk factors and prevention strategies. J Am Acad Othop Surg 8:
141–150, 2000.
22. Özgüven, HN and Berme, N. An experimental and analytical study of
impact forces during human jumping. J Biomech 21: 1061–1066, 1998.
13. Harman, EA, Rosenstein, MT, Frykman, PN, and Rosenstein, RM.
The effects of arm and countermovement on vertical jumping. Med
Sci Sports Exerc 22: 825–833, 1990.
14. Huston, LJ, Vibert, B, Ashton-Miller, JA, and Wojyts, EM. Gender
difference in knee angle when landing from a drop-jump. Am J Knee
Surg 14: 215–220, 2001.
292
the
23. Quatman, CE, Ford, KR, Myer, GD, and Hewett, TE. Maturation
leads to gender differences in landing force and vertical jump
performance. Am J Sports Med 34: 806–813, 2006.
24. Reeves, RA, Hicks, OD, and Navalta, JW. The relationship between
upper arm anthropometrical measures and vertical jump displacement. Int J Exerc Sci 1: 22–29, 2008.
25. Sheppard, JM, Cronin, JB, Gabbett, TJ, McGuigan, MR, Etxebarria,
N, and Newton, RU. Relative importance of strength, power and
anthropometric measures to jump performance in elite volleyball
players. J Strength Cond Res 22: 758–765, 2008.
TM
Journal of Strength and Conditioning Research
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.