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Original Research

Structural and Functional Predictors of Drop Vertical Jump

Ferreira, Lucas C; Weiss, Lawrence W; Hammond, Kelley G; Schilling, Brian K

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Journal of Strength and Conditioning Research: September 2010 - Volume 24 - Issue 9 - p 2456-2467
doi: 10.1519/JSC.0b013e3181e3484b
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Vertical jump (VJ) may be a decisive factor in sports such as volleyball, basketball, and selected track and field events. It may also contribute to success in soccer, baseball, tennis, and in other sports. Moreover, it has been found to be one of the best performance predictors in football and weightlifting (3,7,28) and is positively correlated with horizontal running speed and agility (4,21,37). Because jumping capability appears to be an integral component of successful participation in numerous activities, the identification of independent variables contributing to it might be useful for individualizing training or for talent identification.

Leg power and rate of force development appear to be positively related to jumping ability (2,25,30-32,34,35). Furthermore, expressing power relative to body weight (BW) appears to enhance this relationship (2,34,35). Although body fat percentage (BF%) intuitively appears to be inversely related to jumping performance, equivocal findings have emerged in this regard. Weiss et al. (34) and Davis et al. (9) both reported significant negative correlations between BF% and VJ performance. However, Weiss et al. (33) and Ashley and Weiss (2) found negative but nonsignificant correlations. In the latter 2 studies, small BF% variability among the subjects (subject homogeneity) may have contributed to its attenuated association.

Although progress has been made in identifying VJ predictors, only roughly 50% of its variability has been accounted for (9,26,30,32,34-36). Clearly, other variables are responsible for the remainder of the variability, and it may be that morphologic variables other than BF% account for some of it. Variables that conceivably might increase the explained variability in VJ performance include BW, talocrural (ankle) joint range of motion (ROM), various body segment lengths or ratios, and Q-angle (Figure 1) (8,9,13). For example, a higher BW would require greater force to overcome the effects of gravity, especially if that weight were nonmuscular tissue or skeletal muscle not contributing to the jumping action. In addition, longer leg segments would result in greater displacement over which force could be applied during jumping. Furthermore, greater Q-angles as are typically found in women might accentuate lateral pull on the patella sufficiently to decrease mechanical efficiency of the quadriceps femoris muscles pulling on the lower leg at the tibial tuberosity. Several of these variables have not been previously examined because they are essentially nontrainable.

Figure 1
Figure 1:
Depiction of quadriceps angle (Q-angle).

Therefore, the purpose of this study was to determine the relationship between VJ and both muscle-mechanical and morphologic variables. If progress is made in accounting for additional variability in VJ performance, then the specified variables could be used to denote individual training needs or for talent identification.


Experimental Approach to the Problem

To identify variables that were capable of explaining variations in drop vertical jump (DVJ) performance, we designed a multiple regression study. Independent variables evaluated for potential inclusion in one or more regression equations were both strength related and structural. All were selected after careful examination of the current literature on the topic and their apparent logical contribution to the VJ action. Men and women subjects were used in the study to increase variability in both dependent and independent variables.


Subjects were apparently healthy men (n = 25) and women (n = 27), who were recruited from a university campus. Their physical characteristics are shown in Table 1. All had at least 3 months of previous systematic lower-body resistance training experience, including the squat exercise, and were able to perform a barbell back squat with a minimum external load no less than their own BW (1 repetition maximum [1RM] ≥ BW). Furthermore, potential subjects reported no previous knee or back injuries that might predispose them to injury during this study. Subjects had to be sufficiently lean for investigators to be able to identify the anterior superior iliac spine (ASIS) of the pelvis for Q-angle determination. Outside of this constraint, we endeavored to introduce as much subject variability as possible in body fat, body dimensions, and jumping ability, satisfying the minimum training requirements described above.

Table 1
Table 1:
Descriptive statistics for subjects' anthropometric characteristics.*


Subjects were required to report to the laboratory for 5 different sessions. Institutional review board-approved written informed consent was obtained before any data collection. For all testing sessions, subjects wore the same type of clothes and shoes, and data were collected by the same investigators at the same time of the day.

Subsequent to informed consent and the acquisition of a brief medical history, the 1RM for the barbell back squat was determined (11) to screen potential subjects and to serve as a basis for loading in subsequent testing sessions. The minimum standard for continued participation was a 1RM (external load) that was no less than his or her own BW. Before testing, the depth corresponding to the subject's 90° knee angle was determined and duplicated during testing using a taut rubber cord as a mechanical marker (Figure 2). Heel placement was also controlled so that squat depth was consistent across trials. An attempt was considered successful only if the subject attained a 90° knee angle and returned to the initial position without external help.

Figure 2
Figure 2:
Squat rack setup for 1 repetition maximum. Heels were situated adjacent to horizontal line on floor. Height of the “mechanical marker” (tight elastic cord) for the squat bottom position was adjusted to 90° of knee flexion.

During their second visit to the laboratory, subjects underwent gross morphologic evaluation including height, BW, BF%, sitting height, and Q-angle (Figure 3). After the standardized warm-up (Table 2), unloaded ankle ROM was assessed (Figure 4). After a 3- to 5-minute rest, subjects performed practice trials of the DVJ and both countermovement jump (CMJ) and non-countermovement (squat jump [SJ]) loaded jumps. The order of the tests was counterbalanced for each subject, and the same order was used during practice and testing. During the third session, subjects underwent a repeat assessment of Q-angle, and after the standardized warm-up repeated the ankle ROM test and practice trials for DVJ and load-spectrum jump squats. Repeated assessments of Q-angle and ankle ROM were carried out to establish the stability reliability and precision of these measures.

Table 2
Table 2:
Standardized warm-up used before 1RM, ankle ROM, DVJ, and CMJ and SJ testing.*†
Figure 3
Figure 3:
Quadriceps angle (Q-angle) measurement. Subject with the anatomical landmarks identified A) and technician performing the measurement B).
Figure 4
Figure 4:
Ankle range of motion measurement. Subject performing A) a dorsiflexion, and B) a plantar flexion, with the Leighton Flexometer attached to the right foot.

During the following week, subjects returned to the laboratory to be tested for their DVJ height and for their CMJ and SJ force and power performances. The session began with the standardized warm-up, followed by a 5-minute rest. Thereafter, subjects performed all tests in duplicate (with a 2-minute rest between attempts). The trial yielding the highest PP output (loaded jumps) and displacement (DVJ) for each duplicate was used for further analysis. Subjects returned to the laboratory approximately 48 hours after the previous session at the same time of the day to repeat the DVJ and load-spectrum jump squat tests. Although the test order was counterbalanced between subjects, the test order for individuals was the same as in the previous testing session.

Drop Vertical Jump

For the DVJ, the subject stepped off of a stable box, landed with both feet simultaneously on the force plate, and then rebounded upward as quickly and as high as possible. During the test, subjects held a plastic pipe on their shoulders to restrict arm movement (Figure 5). The box height for the DVJ (40 cm) was similar to the optimum height reported by Ruan and Li (27) and matches the one previously reported by Bobbert et al. (5) as the best box height for a standing drop jump. Force data were collected using a uniaxial force plate (Roughdeck, Rice Lake Weighing Systems, Rice Lake, WI, USA), channeled through a signal conditioner/amplifier (TMO-2; Transducer Techniques, Temecula, CA, USA) interfaced to a PC via a 12-bit analog-digital converter (PCI-DAS1200, Measurement Computing, Middleboro, MA, USA), and sampled at 500 Hz. Datapac 5 (v0.5.0, Mission Viejo, CA, USA) was used for data extraction. Data were low-pass filtered (first-order order, zero-lag Butterworth) with a cutoff frequency of 30 Hz. Drop-jump height was estimated based on flight time, according to procedures described previously (1).

Figure 5
Figure 5:
Drop vertical jump. Subject is holding the PVC pipe and stepping off the box.

Loaded Jump Squats

For the jump squats, external loads were 20, 30, and 40% of the previously measured 1RM. These loads were chosen based on previous studies from this laboratory (31,32), in which the 30% 1RM load had the greatest relationship with DVJ. Other loads examined in these studies were 60 and 90% of 1RM. Consequently, efforts were made to ascertain if the 30% 1RM external load or either of 2 loads bracketing it was more predictive of jumping ability.

Subjects performed both concentric-only (non-countermovement) SJ squats and CMJ squats. For the CMJ, subjects performed a regular CMJ squat while holding the loaded bar on their shoulders. The SJ test was the same except that subjects descended to 90° of knee flexion and held that position for 3 seconds, at which time the technician gave a signal to the subject to jump as high as possible with no further countermovement. In both cases, the depth of the dip was controlled by using a horizontally oriented elastic cord, insuring subjects descended consistently to the appropriate depth (Figure 6). Bouncing off or stretching the elastic cord, not descending to it, or failure to jump off the ground constituted a failed lift, in which case subjects were required to repeat that trial.

Figure 6
Figure 6:
Subject holding at 90° of knee flexion for the static jump squat.

Force and velocity were measured directly. Ground reaction force was measured using a uniaxial force plate (Roughdeck, Rice Lake Weighing Systems, Rice Lake, WI, USA), whereas bar velocity was measured using a linear velocity transducer (Unimeasure, Corvallis, OR, USA). Data processing for force with the addition of velocity were the same as noted previously for the DVJ. Power was calculated via inverse dynamics by multiplying the synchronized force and velocity measures.

Morphologic Measures

A calibrated electronic platform scale (Sterling) was used to obtain BW. Height was measured by using a weighing scale with built-in stadiometer (Detecto). Sitting height was measured using the same scale, but with the subject seated on the platform (24). The difference between the standing and sitting heights was operationally defined as leg length (LL).

Body fat % was estimated via a 3-site skinfold approach (chest, abdomen, and thigh for men and triceps, supra-iliac, and thigh for women), according to procedures described previously (19,20). Ankle ROM was measured with a Leighton Flexometer (Leighton Flexometer, Spokane, WA, USA) with subjects in a supine position (Figure 4). To zero the flexometer, subjects were asked to fully dorsiflex their right foot. Immediately thereafter, subjects fully plantar flexed, and an ROM reading was obtained. Each subject performed 3 consecutive trials, with the 1 yielding the greater flexibility being recorded for further analysis. All subjects were tested on their right ankle.

Quadriceps angle was measured as the superior angle formed by 2 intersecting lines at the knee: (a) ASIS to the center of the patella, and (b) the center of the patella to the center of the tibial tuberosity (Figure 1). With the subject supine, the technician first identified and marked the landmarks. The subject's feet were then strapped together with a belt to assure consistency in foot positioning. The subject was asked to perform an isometric contraction of the quadriceps so that the patella was in a position similar to that existing during the last stage of the take-off phase of a VJ. The angle was measured with a modified goniometer (fixed arm was lengthened), by placing the fixed arm over the subject's ASIS, the axis on the surface of the patella at its centermost point, and the mobile arm above the centermost point of the tibial tuberosity (Figure 3). As with the ankle ROM, all subjects were assessed on their right side.

Statistical Analyses

Variables considered as potential independent predictors of DVJ were initially evaluated for stability reliability and precision (with the exception of height, weight, LL, and BF%). Minimally acceptable statistical indicators for these variables were previously established as follows: stability reliability (intraclass coefficient of correlation [ICC], 2-way random model; r ≥ 0.70) and precision (coefficient of variation [CV%] ≤ 15) (14,15,29). Table 3 includes a listing of variables that were examined for stability reliability and precision in this investigation.

Table 3
Table 3:
List of possible DVJ predictors that were evaluated for stability reliability and precision.*

Several multiple regression models for men and women combined were estimated (SPSS, Release 17.0) using forced entry. Combining men and women subjects increased both sample size and variability in the dependent and independent variables, likely resulting in increased explained variance for the generated models. Regressions were also generated separately for each gender with the expectation that explained variance would be less because of increased homogeneity of the respective samples. In addition, a maximum of 2 independent variables were used for the gender-based models because of the sample size of each (men = 25, women = 27). Variable selection for inclusion in these models was based on the correlation matrix, statistical independence (absence of multicollinearity), and logic. For those variables that were assessed in 2 different sessions, an average was calculated and used in the regression models. Multicollinearity between independent variables was evaluated using variance inflation factor (<3.00) and tolerance (>0.20). Statistical significance was set at p ≤ 0.05.


For all subjects (n = 52), DVJ displacement averaged 28.1 ± 6.64 cm (mean ± SD). When gender was considered, DVJ displacement averaged 33.0 ± 5.34 cm for men (n = 25) and 23.4 ± 3.79 cm for women (n = 27).

Two of the morphologic variables, ankle ROM and Q-angle, were relatively novel, and so stability reliability and precision were determined. For ankle ROM, ICC was 0.94 and CV was 3.9%, whereas for Q-angle, ICC was 0.90 and CV was 10.1%, with both meeting the previously established criteria. Results for the strength-related variables are presented in Table 4. Reliability and precision for all of these variables met the minimum criteria set a priori (ICC ≥ 0.86 and CV ≤ 7.1%).

Table 4
Table 4:
Descriptive statistics for strength-related variables.*

Bivariate correlations between the independent variables and jumping displacement are presented in Table 5. Among all variables examined, CMJ squat PP at 30% 1RM (CMJ30PP) and SJ squat PP at 20% 1RM (SJS0PP) had the highest correlations with vertical displacement (both were r = 0.84, p < 0.001). Concurrently, all other strength-related variables were also related to jumping displacement (p < 0.001). Among the morphologic variables, height, BW, ankle ROM, Q-angle, BF%, and LL also had significant relationships with vertical displacement (p ≤ 0.005), although some correlations were modest. Gender was also significantly related (r = −0.728, p < 0.001) to jumping performance, although men were generally better jumpers than the women.

Table 5
Table 5:
Bivariate correlations between independent variables and DVJ displacement.*

Jumping displacement was regressed for all subjects, men only, and women only while using the same independent variables for the respective equations. The best prediction equation (presented in Table 6, model 1) included CMJ30PP and BW, and accounted for 83% of the variability in DVJ displacement for men and women combined (Figure 7). The 2 variables were independent of each other as multicollinearity was not detected (variance inflation factor = 2.76, tolerance = 0.36). Additionally, regression results indicated that each variable entered in the equation had a significant unique influence on displacement (p < 0.001). In order of importance, CMJ30PP had the greatest influence (β = 1.312) followed by BW (β = −0.599). The addition of BF% to this model was not possible, because of extreme multicollinearity between it and BW. Adding other morphologic variables did not increase the explained variance obtained with this model. Furthermore, the inclusion of other strength-related variables was not possible because of extreme multicollinearity between them and CMJ30PP. When CMJ30PP and BW were used in regressions for men and women separately, the explained variance was 68 and 64%, respectively (Table 6), without multicollinearity.

Table 6
Table 6:
Regression models.
Figure 7
Figure 7:
Scatter plot for observed vs. predicted drop vertical jump displacement for model 1 involving both men and women.

A second regression model (Table 6, model 2) included CMJ30PP and BF%. When men and women were combined, this model accounted for 80% of the displacement variability (Figure 8). Multicollinearity was not present for the 2 independent variables with both having significant influences on displacement (p < 0.001). In order of importance, CMJ30PP had the greatest influence (β = 0.597) followed by BF% (β = −0.398). As with model 1, adding BW or other strength-related variables to this model was not possible because of extreme multicollinearity, and adding other morphologic variables did not increase the amount of explained variance. When these same 2 variables were used for separate samples of men and women, both equations explained 60% of the variability in DVJ performance.

Figure 8
Figure 8:
Scatter plot for observed vs. predicted drop vertical jump displacement for model 2 involving both men and women.

Regression models including SJ20PP were also developed, and those also accounted for a large portion of variability in DVJ displacement, although slightly less than when CMJ30PP was used. Model 3 (Table 6) included SJ20PP and BW. For combined men and women, this model accounted for 80% of the variability in DVJ displacement (Figure 9) without multicollinearity. Both variables had significant unique influences on displacement (p < 0.001). In order of importance, SJ20PP had the greatest influence on DVJ displacement (β = 1.211), followed by BW (β = −0.482). Adding BF% or other strength-related variables to this model was not possible because of extreme multicollinearity, and adding other morphologic variables did not increase its predictive ability. When SJ20PP and BW were used for separate equations for men and women, the explained variance was 59 and 73%, respectively (Table 6) without multicollinearity. It is noteworthy that for the gender-specific models, this one had the greatest predictive ability for our women subjects.

Figure 9
Figure 9:
Scatter plot for observed vs. predicted drop vertical jump displacement for model 3 involving both men and women.

The last regression model reported (Table 6, model 4) included SJ20PP and BF% and accounted for 78% of the variability in displacement for combined men and women (Figure 10) without multicollinearity. Both SJ20PP and BF% had significant influences on displacement (p < 0.001). In order of importance, SJ20PP had the greatest influence in DVJ performance (β = 0.606), followed by BF% (β = −0.373). Adding BW or other strength-related variables to this model was not possible because of extreme multicollinearity. As with all other models reported, adding other morphologic variables did not increase the amount of explained variance. When SJ20PP and BF% were used for separate equations for men and women, the explained variance was 55 and 58%, respectively (Table 6.), without multicollinearity.

Figure 10
Figure 10:
Scatter plot for observed vs. predicted drop vertical jump displacement for model 4 involving both men and women.


The current findings corroborate those from previous investigations indicating a positive relationship between DVJ performance and power output (2,32,34). For both CMJ and SJ at all 3 relative loads, the correlation with DVJ was greater for PP than for peak force. Furthermore, PP during SJ squats at the 20% 1RM load and during CMJ squats at 30% 1RM load were the best predictors of DVJ performance in the present investigation.

Peak power during CMJ squats at the 30% load elicited the best regression model. This supports previous reports identifying the same loading (31,32). However, based upon the load-velocity relationship, the 20% 1RM load would be expected to elicit a faster squat velocity that more nearly matches that used during jumping and thereby produce variables more related to jumping performance. In the current investigation, this level of specificity was not borne out. In fact, SJ20PP and CMJ30PP were similarly related (r = 0.84) to DVJ. Although other performance variables were also related to DVJ, none were as highly related as the 2 just noted. Furthermore, when either of 2 morphologic variables, BW or BF%, was incorporated into regression models including either of the 2 PP variables, the explained variance was greater, slightly more so when CMJ30PP and BW were used.

The current findings also support the existence of an inverse relationship between BF% and jumping performance, as was reported previously (9,34). Body fat % was independently related to displacement in 2 models (models 2 and 4). Although some previous studies found no significant correlations between BF% and displacement, subject homogeneity for BF% in those investigations may have attenuated the relationship (2,33). With a normal distribution of body fat levels, BF% and vertical displacement would be expected to be negatively related because fat tissue is noncontractile, and vertical jumping involves elevating the body's center of mass against the pull of gravity. The development of separate regression equations for men and women, especially if either group has relatively low body fat, would be more likely to exclude BF% as a predictor because of increased subject homogeneity.

Although BF% and BW were not highly related to each other (r = −0.22), once either was incorporated in the regression, the addition of the other failed to substantially improve the explained variance. Therefore, the inclusion of either was done to the exclusion of the other. That being said, when BW was included in the regression models instead of BF%, the explained variance was slightly greater. That consideration together with the greater simplicity for obtaining BW provides a good basis for selecting this morphologic variable over BF% when predicting DVJ displacement. However, the importance of BF% for VJ should not be overlooked, because manipulating BW without affecting PP would likely require a manipulation in BF%.

The inclusion of other morphologic variables in the regression equations did not increase the amount of explained variance in the current investigation. It appears that only 1 study previously examined the relationship between body segment lengths and VJ (8), and none were reported as being strong predictors of jumping performance. It could be argued that longer leg segments should provide a longer time for force production during the take-off phase. However, that is not true for angular motion, as is the case with knee and hip extension during VJ. Therefore, it was no surprise that LL did not increase the amount of explained variance in any of the reported models, supporting the findings of Davis et al. (8).

Quadriceps angle has previously received attention because of its apparent positive relationship with knee injury (10,12). The knee joint is part of the kinetic chain involved in jumping (16,23), and an increase in the lateral pull of the quadriceps femoris muscle, a characteristic of greater Q-angles, would be expected to have a negative impact on force output at this joint and vertical displacement. This, however, was not supported by our findings, as adding Q-angle to any of the reported models did not help to explain any additional variability in DVJ performance. It is likely that any negative effect from excessive Q-angles would likewise affect other performance variables during load-spectrum jump squats, so that changes in Q-angle might lack independence with regard to predicting jumping performance. It warrants note that we measured Q-angle based on surface anatomic landmarks. Although our results demonstrated satisfactory ICC and CV%, it is possible that methods using internal imaging, such as X-rays, while simultaneously contracting the quadriceps femoris muscle group, may measure Q-angle with greater accuracy. (Note: A relaxed quadriceps femoris muscle group during Q-angle acquisition will alter the obtained value as the patella is positioned more medially during relaxation as opposed to during an isometric contraction.) At this time, the contribution of the Q-angle to VJ performance remains unresolved.

Because improvements in flexibility have been reported to be positively correlated with improvements in strength and velocity of contraction (17,22,38), it seemed reasonable to suspect that specific joint flexibility could influence DVJ performance. However, although influenced by training, flexibility also has a genetic component, which might play a bigger role in ROM than training. When considering the hips, knees, and ankles, only the ankles would ordinarily move through anything close to a full ROM during a DVJ. However, adding ankle ROM to any of the reported models did not increase the amount of explained variance. This is consistent with the findings of Davis et al. (9). It may be that for populations with a normal ROM, ankle flexibility has little discernable effect on DVJ performance. On the other hand, it may be a very different scenario in situations in which ROM is severely limited, as might be the case subsequent to injury, immobilization, or various arthritic conditions. Furthermore, if ankle ROM directly influences strength and velocity, as with Q-angle, it could be that its contribution to DVJ is included in the strength-related variables examined in the current study.

The set of variables used in all models reported in the present study accounted for a larger amount of variability in jump performance than has been reported previously (9,26,30,32,34-36). We believe a couple of factors might have helped in adding explained variance to our data. Most VJ studies conducted to date investigated its relationship with isokinetic movements (2,9,18,25,34,35). However, Iossifidou et al. (18) and Tsiokanos et al. (30) previously stated that isokinetic tests of strength might have a limited relationship with VJ. In isokinetic tests, the velocity is constant throughout the movement, and during a VJ, acceleration up to the point of take-off is likely. Using an isoinertial movement instead of an isokinetic, one may have contributed to the greater variability explained by the current models.

In addition, we believe that our use of relatively light jump squats rather than single-joint movements or other forms of squat jumps may have contributed to the higher explained variance for the current regressions. The use of strength-related variables obtained during loaded isoinertial jump squats appears to have enabled us to account for a greater variation in DVJ performance than previous studies.

Both loaded SJ and loaded CMJ were used in the current study because it was unclear if variables obtained during the latter approach would be sufficiently reliable and precise. Fortunately, force and power data for both types of jump squat, SJ and CMJ, met minimum criteria. Furthermore, although the correlation between DVJ and both SJ20PP and CMJ30PP were similar, when either %fat or BW was added to CMJ30PP in the regression models for combined men and women, the amount of explained variability was slightly greater.

Separate regressions for men and women were developed using the same independent variables. For all 4 models, the explained variance was less using the gender-specific approach. We attributed this phenomenon to reduced subject heterogeneity for both the dependent and independent variables. However, if a need exists to use a gender-specific equation, the first and third models explain the greatest amount of variability in DVJ displacement for men and women, respectively. CMJ30PP and BW are incorporated into the men's best model, whereas SJ20PP and BW are used in the women's best model. As the CMJ incorporates a stretch-shortening cycle and the SJ does not suggest that musculotendinous stiffness or elasticity at relevant joints may play a somewhat greater role in men's performance than in women's and warrants closer future examination. In fact, Bojsen-Moller et al. (6) reported that musculotendinous stiffness appeared to be highly correlated with rapid muscle force exertion characteristics during CMJ and squat jumps in trained men. Although this work did not address the issue of gender differences, it would be useful to establish if and to what extent they might exist.

Practical Applications

It appears that PP output during either SJ or CMJ squats using relatively light loads may be useful in indicating DVJ capability. Body weight and percent fat appear to be 2 options as a secondary predictor. These variables are trainable, so interventions designed to both enhance power in the involved musculature and decrease BW would be expected to concurrently accentuate DVJ. Generally speaking, decreasing body fat without appreciably losing musculature in areas of the body contributing to jumping appears to be a reasonable goal for those having excess body fat. Appreciable muscle mass in areas of the body not contributing to VJ would also be expected to attenuate performance because of the weight that must be moved against gravity.

Although we were interested in identifying additional morphologic variables that might further explain DVJ displacement variability, it appears that none of the ones investigated herein accomplished this task in the presence of either CMJ30PP or SJ20PP and either BW or BF%. Perhaps future examination of musculotendinous elasticity and/or stiffness might further explain DVJ performance and may at least partially explain the gender difference typically seen in jumping ability.

At some point, training studies need to be conducted to ascertain if changes in the trainable predictors actually change jumping performance. If the predictors are changed in the appropriate direction, then either jump performance will be augmented or the apparent relationships may be spurious.


The authors wish to thank John Trepanowski, Marvin Grindle, Douglas Landrum, Paul Partain, and Michael Bledsoe for their help in data collection.


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jump performance; muscular strength; muscular power; morphologic characteristics

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