The Relationship Between Horizontal Jumping and Sprinting Ability Across Sexes in Young Active Adults

Abstract

Purpose: The purpose of this research was to investigate the relationship between horizontal jump distance, 10 m time, and 30–40 m time in multi-sport athletes separated by sex and sprint speed. Methods: A total of 1352 athletes (742 males and 610 females) performed 40 m sprints, standing broad jumps (SBJs), and standing triple jumps (STJs). Data were separated by sex and then grouped as fast, average, and slow using the K-Means algorithm in three conditions (acceleration, max speed, and combined). Results: Regression models explained 84.01% of the variance (F(7,757) = 574.5, p < 0.001) for the 10 m times with mass, speed group, and sex as significant predictors and 88.51% of the variance (F = (7,757 = 841.6, p < 0.001) for the 30–40 m times with SBJ, STJ, speed group, sex, and the interaction of sex and group as significant predictors. Conclusions: These results suggest that when examining general athlete physical performance, horizontal jump tests and max speed sprint times can be used equivalently to stratify athletes. However, it is important to group athletes by speed and sex before being able to predict sprint ability from horizontal jump tests. Further, athlete mass is a significant factor in the prediction of acceleration ability but not maximum speed, and horizontal jumps were significant factors in the prediction of max speed but not acceleration.

1. Introduction

Physical performance assessments are essential for identifying underlying physical abilities that can be developed into talent, monitoring athlete progress, and guiding training decisions [1,2,3]. These assessments often include maximal and submaximal strength testing for both upper and lower body, as well as measures of aerobic and anaerobic power and capacity across various forms of locomotion [4,5]. Sprint testing, in particular, is widely used in sports such as athletics, rugby, football, and soccer—where competitive success depends on the ability to cover horizontal distance quickly—to assess lower limb power [6,7,8]. Sprint assessments have proven effective at distinguishing between starters and non-starters in the rugby league and predicting future success in sports like rugby union and American football [4,9,10]. Sprint tests over various distances, such as the NFL combine’s 36.58 m sprint, are designed to capture an athlete’s maximal sprint speed and are often assessed in segments. Acceleration ability is measured from a static start, while maximum speed is typically evaluated in “flying” sprints or through split times within a trial. For example, in rugby union, athletes might complete a 40 m sprint with split times from 0 to 10 m used to assess acceleration and times from 30 to 40 m to gauge maximum speed [11]. This segmentation reflects the kinetic and kinematic differences between acceleration and maximum speed phases; during acceleration, athletes exhibit greater forward lean and longer ground contact times to maximize horizontal force, whereas at maximum speed, force is directed more vertically with shorter ground contact times and high rates of force development [6,7,11,12,13,14,15,16,17].

Sprint Speed Assessments are often collected in tandem with horizontal and vertical jumps to develop a comprehensive physical profile [12,13,18,19,20,21]. Jumping and sprinting share similar mechanical characteristics such as the magnitude and direction of force application and relatively higher velocities when compared to most gym-based measures [22]. As such, there are significant associations between horizontal jumping ability and sprinting ability, and these associations appear to be similar among non-sprint-trained students and field sport athletes [12,14,15,18,23,24]. However, sprint times in slower field-based athletes display larger correlations with jump performance than their faster teammates [18]. This may suggest that at the highest speeds, sprint and jump performance may provide different performance insights. While much of this research has been conducted in small samples of similar athlete cohorts, there has yet to be an analysis of a large cohort multi-sport population across a wide spectrum of sprint and jumping abilities. Further, as sprint and jumping ability are different in males and females, with males often faster and able to jump further than their female counterparts [25], it is important to establish the relationships between sprint ability and horizontal jump performance, utilizing a large cohort of athletes separated by sex and ability to provide insight into the shared and independent value of sprint and jump performance tests to support athlete testing and athletic development. Further, an analysis using a large group of multi-sport athletes will likely reflect the characteristics of a wider population, reduce the chance of spurious correlations through a large sample size and wide spectrum of abilities, and increase the generalizability of the research. Therefore, the purpose of this research was to investigate the relationship between horizontal jump distance, acceleration, and max sprinting time from a large group of multi-sport athletes separated by sex and sprint speed.

2. Materials and Methods

2.1. Experimental Design

A cross-sectional correlational study design was implemented to assess the relationship between horizontal jumping and sprinting abilities. Participants were grouped by sex and classified as fast, average, or slow using a K-Means clustering algorithm, with 0–10 m and 30–40 m split times as the criteria for grouping. Sprint ability was defined as the dependent variable given its frequent association with athletic performance. The independent variables included body mass (m), standing height (h), jump distances (standing broad jump (SBJ) and standing triple jump (STJ)), as well as the interaction between sex and group to predict sprint times, $t_{\mathrm{sprint}}$ (either 0–10 m time or 30–40 m time).

2.2. Participants

One thousand three hundred and fifty-two multi-sport athletes were sampled from a talent identification program run by a partnership between a national sports institute and a private organization between 2016 and 2019. This talent identification program sought to identify new talent for and facilitate talent transfers across Olympic-eligible national sports organizations. More specifically, 742 participants were male (mean ± SD: age = 19.08 ± 2.93 years, height = 180.78 ± 8.08 cm, body mass = 77.40 ± 12.49 kg) and 610 were female (mean ± SD: age = 18.21 ± 3.13 years, height = 168.60 ± 7.81 cm, body mass = 63.80 ± 10.61 kg). Other descriptive information such as previous sporting experience was not provided with the data set and was not viewed as applicable to achieve the research’s aims. The event gave informed consent to utilize secondary anonymized data, and ethical approval for the study was obtained from the University of Victoria’s Human Research Ethics Board and complied with the principles outlined in the Declaration of Helsinki.

2.3. Methods

The tests were conducted indoors on a rubberized surface to limit the impact of environmental factors. Participants were given the option of following a warm-up prescribed by a National Strength and Conditioning Association Certified Strength and Conditioning Specialist or an individual warm-up to maximize their performance. Participants wore athletic clothing and running shoes, and no track spikes were permitted. Tests were conducted in the order reported below, on the same day, between the hours of 08:00 and 16:00.

Anthropometrics. Height was measured to the nearest 0.1 cm using a Seca stadiometer (Hamburg, Germany) following the ISAK stretch stature method. Body mass was recorded to the nearest 0.01 kg with athletes unshod and wearing athletic clothing. Measurements were taken on a firm surface using an FG-150K calibrated scale (A&D, Tokyo, Japan), which was zeroed prior to each test.

Horizontal Jump Assessment. Jump distance for both the SBJ and STJ was collected using previously published methods [18]. Briefly, participants started with both feet parallel and toes positioned behind the 0.00 m mark on the measuring tape (Lufkin, Apex Tool Group, Sparks, MD, USA), set on a hard rubberized surface. Participants were instructed to jump as far as possible, with an arm swing permitted. If a participant was unable to land without falling backward or their feet shifted upon landing, no score was recorded for that attempt. Each jump type allowed a maximum of three attempts with full recovery between jumps (at least two minutes between attempts). Jump distance was measured from the 0.00 m mark to the nearest heel, recorded to the nearest centimeter. During the STJ, participants were instructed to maintain forward momentum and ensure continuity through the first two of the three jumps. The longest successful jump was recorded for analysis. The data collected following this protocol demonstrated good reliability, with an intra-class correlation coefficient (Two-Way Random, single measure, absolute agreement) of 0.95 (95% CI: 0.90 to 0.98) and TEM of 0.07 m for the SBJ distance and 0.97 (95% CI: 0.93 to 0.99) and TEM of 0.20 m for the STJ distance.

Sprint Speed Assessment. Speed data were collected following previously published methods [18]. Briefly, the 40 m sprint time was measured using the Brower Timing TC-System (Draper, UT, USA), with timing gates set at 0 m, 10 m, 30 m, and 40 m. Participants began from a two-point stance, positioning the middle of their front foot 0.75 m behind the initial timing gate on a hard rubberized surface. Each participant was allowed a maximum of three attempts, with full recovery between trials (at least five minutes between attempts). The best 0–40 m time was recorded for analysis, along with corresponding 0–10 m and 30–40 m splits. This protocol demonstrated good reliability, with an intra-class correlation coefficient (Two-Way Random, single measure, absolute agreement) of 0.88 (95% CI: 0.84 to 0.91) and TEM of 0.05 s for the 0–10 m split and 0.91 (95% CI: 0.90 to 0.95) and TEM of 0.03 s for the 30–40 m split.

2.4. Statistical Analysis

The data were divided by sex and checked for normalcy. Participant data were separated by sex and then grouped as fast, average, and slow using the K-Means algorithm in 3 conditions (acceleration [0–10 m time], max speed [30–40 m time], and combined [0–10 m time and 30–40 m time]). A confusion matrix was performed to assess the accuracy, sensitivity (true positive rate), specificity (true negative rate), and precision (positive predictive value) between acceleration and max speed and the combined group.

To determine whether horizontal jumping distances were able to predict sprint times, multiple regression models were conducted with fast and slow athletes ($\alpha$ set at 0.05) using R (version 4.4.1, Vienna, Austria). The average group was removed to highlight the difference in the extreme groups (i.e., fast and slow). A sample size of 10 provided adequate power (0.8) to detect a difference of 0.23 s in 10 m time, 0.27 s in 30–40 m time, 0.93 s in 40 m time, 0.50 m in SBJ distance, and 1.67 m in STJ distance. We included over 1300 participants to enhance the generalizability and robustness of our findings. This larger sample size improves the precision of effect estimates, stabilizes the K-Means clustering results used to define performance categories, and reduces the impact of outliers. Additionally, it allows for reliable subgroup analyses, providing a more comprehensive understanding of sprint performance across diverse demographics. The models contained the independent variables of body mass, standing height, jump distances (SBJ and STJ), and the interaction of sex and group to predict sprint times, $t_{sprint}$ (0–10 m time or 30–40 m time), see Equation (1). Note that “$ϵ$” refers to the error term, which is sometimes referred to as “noise” in the equation:

$$t_{sprint}={\beta }_0+{\beta }_1·m+{\beta }_2·h+{\beta }_3·SBJ+{\beta }_4·STJ+{\beta }_5·sex+{\beta }_6·group+{\beta }_7·sex·group$$

(1)

The coefficients ${\beta }_1,{\beta }_2,\cdots ,{\beta }_7$ quantify the effect of each predictor on $t_{sprint}$, assuming all other predictors remain constant. Specifically, each ${\beta }_i$ coefficient indicates the average change in $t_{sprint}$ associated with a one-unit increase in the predictor, holding other variables constant. The intercept ${\beta }_0$ represents the expected value of $t_{sprint}$ when all predictors are zero. The error term $ϵ$ accounts for the variation in $t_{sprint}$ not explained by the predictors.

3. Results

The confusion matrix yielded that a combined grouping (acceleration and max speed) showed a significant accuracy of 76.9% and 79.51%, respectively (both p < 0.001), to classify athletes into fast, average, and slow speed groups with clustering. Sensitivity, specificity, and precision for separate groupings (acceleration or max speed) are presented in

Table 1. Groupings’ sensitivity, specificity, and precision.

	Acceleration		Max Speed
	Fast	Slow	Fast	Slow
Sensitivity	0.79	0.81	0.84	0.84
Specificity	0.91	0.92	0.87	0.96
Precision	0.84	0.71	0.79	0.84

. The high values in all confusion matrix metrics showed that a combined group is a good representation of both the acceleration and max speed groups, and therefore the combined grouping was used for further analysis. Figure 1 shows a distinct demarcation of the fast, average, and slow athletes for both males and females. Descriptive statistics of the sex and speed groups are presented in

Table 2. Descriptive statistics (mean ± SD) for the fast, average, and slow groups.

		n	Height (cm)	Weight (kg)	10 m (s)	30–40 m (s)	40 m (s)	SBJ (m)	STJ (m)
Female	Fast (1)	214	167.49 ± 6.63	61.56 ± 7.83	1.82 ± 0.06	1.23 ± 0.06	5.64 ± 0.16	2.30 ± 0.18	7.09 ± 0.55
	Average (2)	269	168.41 ± 7.89	63.26 ± 10.48	1.92 ± 0.06	1.35 ± 0.06	6.03 ± 0.16	2.13 ± 0.18	6.49 ± 0.55
	Slow (3)	127	170.87 ± 8.97	68.65 ± 13.18	2.05 ± 0.08	1.50 ± 0.08	6.57 ± 0.26	1.93 ± 0.18	5.85 ± 0.54
Male	Fast (1)	300	179.84 ± 7.17	76.73 ± 9.75	1.66 ± 0.06	1.08 ± 0.05	5.06 ± 0.14	2.84 ± 0.21	8.86 ± 0.70
	Average (2)	318	180.76 ± 7.70	77.49 ± 12.49	1.77 ± 0.05	1.17 ± 0.05	5.40 ± 0.14	2.62 ± 0.22	8.11 ± 0.70
	Slow (3)	124	183.09 ± 10.40	78.78 ± 17.42	1.88 ± 0.06	1.30 ± 0.06	5.84 ± 0.18	2.34 ± 0.20	7.19 ± 0.58

.

Significant regression models explained 84.01% of the variance for the 10 m sprint times (F(7,757) = 574.5, p < 0.001) (see Equation (2)) and 88.51% of the variance for the 30–40 m sprint times (F = (7,757 = 841.6, p < 0.001) (see Equation (3)) including sex, group and jump distances, and accounting for height and mass. For the 10 m sprint times, the variables of mass, speed group, and sex were significant predictors. For the 30–40 m sprint time, the variables of SBJ, STJ, speed group, sex, and the interaction of sex and group (p < 0.001) were significant predictors:

$$\begin{array}{c}\hfill t_{10m}=1.8428+0.0011·mass+0.0003·height-0.0303·SBJ-0.0102·STJ\\ \hfill -0.1464·se{x}_{male}+0.1967·grou{p}_{slow}\end{array}$$

(2)

$$\begin{array}{c}\hfill t_{30--40m}=1.4639+0.0001·mass+0.0004·height-0.0857·SBJ-0.0151·STJ\\ \hfill -0.0799·se{x}_{male}+0.2205·grou{p}_{slow}-0.0759·se{x}_{male}·grou{p}_{slow}\end{array}$$

(3)

4. Discussion

This is the first study to assess the relationships between horizontal jump and sprint metrics for a large population of male and female multi-sport athletes across a wide spectrum of sprinting and jumping abilities. Overall, we have five major findings from this inquiry. First, we found that athletes, when separated by sex, were equivalently classified into fast, average, and slow groups using either acceleration, max speed ability, or both variables, suggesting a common relationship between these metrics for this cohort. Second, speed grouping was found to be a significant factor in the prediction of both the 10 m sprint time and the 30–40 m time. Third, athlete mass was a significant factor in the 10 m sprint time but not in the 30–40 m time. Fourth, across sexes and speed groups, horizontal jumps were revealed as significant predictors of max speed but not acceleration ability. Finally, in this athlete cohort, the prediction of maximum speed is dependent on the interaction of speed group and sex, such that both variables need to be considered together to evaluate the relationship between jump and sprint ability.

In the present study, we used clustering to group athletes and found that fast, average, and slow groups were equivalently classified whether using acceleration ability, max speed ability, or both. This would suggest that the athletes in this cohort, of both sexes, performed similarly relative to one another during acceleration and max speed testing and might support a relationship between acceleration and max speed ability. Consistent with this finding, Salaj and Markovic examined multiple jumping, sprinting, and change in direction abilities and found that all sprint measures (5 m, 10 m, 20 m, and flying 20 m times) shared common factor loading on the same principal component [24]. This finding suggests that while acceleration and max speed abilities are reliant on different mechanical and physiological factors, these metrics are not independent. Further, while the groupings in our study were similar, despite the grouping metric used, we found that speed groups are a significant factor in the prediction of acceleration and max speed abilities. There is evidence of similar associations between sprint and jump metrics for both acceleration and sprint ability, despite speed groupings, for physical education students and field sport athletes [11,18]. However, in studies of elite sprinters, the association between sprint and horizontal jumping ability is not significant, suggesting that speed grouping is an important factor for prediction [20,21]. Agar-Newman et al. and Barr et al. found differential relationships between fast and slow athletes and jump ability in studies on rugby athletes [15,18]. The present findings taken alongside those found in the literature support the need to properly classify athlete speed ability to evaluate the relationship of jump distance to acceleration and max speed ability.

In this investigation, an increase in athlete mass significantly contributed to an increase in acceleration time (i.e., resulted in slower times, which may have implications in a talent identification setting) but not for max speed time, which may suggest that inertia has an impact on acceleration over the first 10 m but is no longer a significant factor once an athlete has sufficient momentum at higher speeds. This would be consistent with the greater horizontal forces required during acceleration as compared to max speed, with greater forces relative to body mass required to equivalently accelerate heavier athletes (Newton’s second law of motion). Additionally, we found that horizontal jumps were significant factors in the prediction of max speed but not acceleration. Taken together, our results suggest that during the acceleration phase of sprinting, athlete mass has a more significant impact on athlete performance than technical and physical characteristics related to horizontal jumping tasks. This might suggest that when assessing acceleration ability between athletes, athlete mass needs to be considered and the use of relative measures employed to compare athlete ability. In the present study, cohort max speed is related to horizontal jumping tasks and not significantly affected by athlete mass.

The significant relationship between horizontal jump and max speed ability observed in this study is interesting from a biomechanics performance assessment perspective. Our results suggest that horizontal jump tests (SBJ and STJ) and 30–40 m sprint times can be used as proxies for one another when examining general athlete physical performance and that these relationships remain in both sex cohorts. Thus, for practitioners assessing general athlete capability, sprint 30–40 m time and horizontal jump tests may be used alone or together when comparing athletes across a broad level of ability and speeds. Similarities in the direction of motion, lower limb inter- and intra-muscle coordination, and ballistic muscle action have been given as a rationale for the association between horizontal jump and sprint ability [18]. While Kale et al. showed no associations between horizontal jump metrics and max velocity or 100 m sprint time, they found that horizontal jumps were negatively associated with stride frequency and positively associated with flight time during maximum speed sprints and related this to similarities between stretch-shortening cycle activity and muscle activation between horizontal jumps and sprint running [20]. Further, it is suggested that horizontal jump training can be used to improve sprint performance through improvement in horizontal force production during stretch-shortening cycles and increased strength across a greater joint range of motion [26]. These findings support greater investigations into the biomechanical and training relationships between horizontal jump and sprinting ability.

In this study, the difference in the maximum speed sprint time between the speed groups is dependent on sex. This interaction effect is best observed by examining Figure 2 and the regression Equation (3). As can be seen in Figure 2, there is an overlap between the fast female group and the slow male group. This gives an opportunity to compare the relationship between jumping ability and max sprint ability between the sexes at the same speeds. It is therefore important to group athletes based both on sex and speed group before being able to predict sprint ability in terms of 30–40 m time from horizontal jump tests. Perhaps this would require sprint testing and jump testing at different intervals where these tests cannot be consistently repeated due to the availability of equipment and facilities. This finding of an interaction of speed group and sex also highlights the differences between sex grouping for athlete assessment and the need for sex group-based assessments. It is possible that a targeted analysis of separate sex groups may result in the emergence of salient differences in the relationships between jump variables and sprint speed ability and could be related to biomechanical differences between the sexes in the production of lower body power during jumping and sprinting. For example, previous research on the higher prevalence of anterior cruciate ligament injuries in female versus male athletes has shown that females use different movement patterns in athletic movements such as sprinting [27]. Further, it has been shown that males and females experience different forces because of subtly different plyometric movement strategies [28]. Therefore, it is possible that males and females utilize different strategies to achieve sprint and jump performances, which could further influence the associations and relationships between these two abilities. This area requires further research to better understand the mechanisms through which both sprint and jump performances are achieved in athletes of different sexes. A limitation of this study and potential criticism is the inability to investigate the impact of sporting background on the relationship between horizontal jumping ability and sprinting speed as this information was not provided with the data set. However, numerous smaller studies have already examined the associations and relationships between jumping performance and sprinting speed in specific cohorts of athletes [14,15,18].

5. Conclusions

Overall, this study examined predictive relationships between horizontal jump ability and max speed ability between sexes and speed groups and has implications for the administration and tracking of performance tests. We found that athlete mass is a significant factor in the prediction of acceleration ability but not maximum speed, and horizontal jumps were significant factors in the prediction of max speed but not acceleration. Therefore, when assessing acceleration ability between athletes, athlete mass needs to be accounted for as it has a more significant impact on athlete performance than technical characteristics related to horizontal jumping tasks. When assessing differences between athlete ability, our results suggest that horizontal jump tests (SBJ and STJ) and max speed sprint times can be used equivalently to stratify general athlete physical performance. However, it is important to group athletes by speed and sex before being able to predict sprint ability in terms of max speed time from horizontal jump tests. The present findings also highlight the important differences between sexes regarding the production of lower limb power as males and females may utilize different strategies to achieve sprint and jump performances, which could further influence the associations and relationships between these two abilities.