The Relationship Between Anthropometric Characteristics, Chronological Age, and Training Age with Speed, Agility, and Explosive Power in Handball Players

Abstract

(1) Background: Research examining the combined influence of anthropometric characteristics, chronological age, and training age on motor performance in handball is limited. Given the sport’s demands and the participation of both adolescent and adult athletes, understanding these relationships is essential for talent identification, personalized training, and long-term athlete development. This study aimed to explore how these variables affect motor performance indicators such as speed, agility, and explosive power. (2) Methods: A cross-sectional study was conducted involving 29 male handball players. Anthropometric data (height and body weight), chronological age, and training age were collected. Motor performance was assessed using a 30 m sprint, a vertical jump test, and an agility test. (3) Results: Chronological age showed a strong positive correlation with training age (r = 0.819), and moderate correlations with height, body weight, vertical jump, agility, and sprint time. Training age was moderately correlated with vertical jump (r = 0.465) and agility (r = 0.439). Height and body weight were positively associated with sprint time. BMI exhibited low but consistent correlations with all motor tests. Regression analysis revealed that height significantly predicted sprint performance (β = 0.401, p = 0.033), while BMI was not a significant predictor. No significant regression models were found for agility or vertical jump performance. (4) Conclusions: The results suggest that both chronological and training age influence certain aspects of motor performance in handball players. Height may serve as a useful predictor of sprint ability, but anthropometric indicators such as BMI appear insufficient for explaining performance in agility or explosive power tasks. These findings support the use of multidimensional and individualized approaches in athletic assessment and training design.

1. Introduction

Handball is a sport characterized by high-intensity physical activities, rapid directional changes, and explosive movements, all of which emphasize the critical role of motor capacity in athletic performance [1]. In this context, attributes such as speed, agility, and explosive power directly influence a player’s effectiveness on the court [2,3]. However, the relationship between these fundamental motor qualities and individual variables—particularly anthropometric characteristics, chronological age, and training age—remains insufficiently clarified in the current literature.

Anthropometric structure plays a crucial role in determining athletes’ physical performance capacities [4]. Anthropometric parameters such as body composition, limb proportions, and segment lengths can significantly influence physical abilities, particularly speed and agility [5,6,7,8]. Nonetheless, physical structure alone is not a comprehensive predictor. Time-related factors such as chronological age and training age also exert substantial effects on performance [6,9]. Chronological age represents physiological maturity and natural development, while training age reflects the athlete’s sport-specific adaptations and accumulated motor skill experience [10,11]. The combined effects of these two factors on athletic performance have rarely been examined holistically.

Lolli et al. [12] reported that physical performance metrics such as sprinting and countermovement jump exhibit a steady but nonlinear increase with chronological age, plateauing after approximately age 16. They also noted that maximal aerobic speed significantly increases until around age 14.5, after which it stabilizes. Furthermore, Hammami et al. [6] found that athletes in the U17 and U18 age groups outperformed their younger counterparts (U14–U16) in all physical tests, identifying age as the strongest predictor of sprint and jump performance.

Despite these findings, the specific impact of training age (i.e., athletic experience) has not been delineated. Matthys et al. [13] found that lower performance in sport-specific skills among handball players was primarily due to a lack of training, suggesting that training age may directly influence performance. In light of this evidence, both chronological and training age emerge as influential parameters in athletic development.

Given the current body of literature, there is a clear need for studies that concurrently examine anthropometric structure, chronological age, and training age. In sports such as handball, where both youth and adult athletes compete, understanding the relationship between these variables and key performance indicators like speed, agility, and explosive power is essential for talent identification, the individualization of training programs, and long-term athlete development. Therefore, this study aims to investigate the relationships among anthropometric characteristics, chronological age, and training age with speed, agility, and explosive power in handball players, thereby elucidating their effects on motor performance through empirical evidence.

2. Materials and Methods

2.1. Participants

The minimum sample size required for the study was determined using the G*Power statistical analysis software (version 3.1.9.3, Heinrich Heine University, Düsseldorf, Germany). For the correlation analysis, a power analysis was conducted with a Type I error rate (α) of 0.05, a statistical power (1 − β) of 0.80, and an effect size (H1) of 0.50. The analysis indicated that a minimum of 29 participants would be sufficient, and the study was completed with 29 volunteer male handball players.

Inclusion criteria required participants to be between the ages of 16 and 35, to have been actively training as licensed handball players for at least three years (with a minimum training frequency of three days per week), and to participate in the study voluntarily. Exclusion criteria included having experienced a serious sports injury within the past six months, having a chronic health condition or a history of neurological disease, or providing incomplete or inaccurate information during the data collection process.

The study was carried out during the competitive season, during which all participants were part of the same team and followed a standardized training program under the supervision of the same coaching staff. The training sessions were held at the same time and on the same days for all athletes, ensuring consistency in training volume, intensity, and content. This approach minimized variability in motor performance related to training factors and helped maintain a homogeneous sample group.

Descriptive statistics of the participants are presented in

Table 1. Descriptive characteristics of participants.

Variables	n	Minimum	Maximum	Mean	Std. Deviation
Age (years)	29	17.00	32.00	19.5172	3.05451
Training Age (years)	29	5.00	22.00	9.5517	3.14627
Height (cm)	29	176.00	198.00	185.0000	5.74456
Body Weight (kg)	29	60.30	104.70	79.4900	10.2300
BMI (kg/m2)	29	18.41	31.29	23.19	2.476
Vertical Jump (cm)	29	22.67	45.63	33.8428	6.28836
Agility (s)	29	8.95	11.05	9.9703	0.39460
30-Meter Sprint Time (s)	29	4.08	4.68	4.3617	0.16222

.

The average age of the 29 handball players participating in the study was 19.52 ± 3.05 years. Their training age (i.e., years of active participation in handball) averaged 9.55 ± 3.15 years, ranging from 5 to 22 years. The mean height was 185.00 ± 5.74 cm, body weight was 79.49 ± 10.23 kg, and the BMI was 23.19 ± 2.47 kg/m². Regarding performance assessments, the average vertical jump height was 33.84 ± 6.29 cm, and the average agility test result was 9.97 ± 0.39 s. Additionally, the mean 30 m sprint time was calculated as 4.36 ± 0.16 s.

2.2. Research Design

This study was conducted using a cross-sectional research design. The data collection process was initiated following the approval of the Düzce University Ethics Committee (Approval No: 138, dated 25 April 2024). In the first session, the purpose and scope of the study were explained to the participants in detail, and informed consent forms were signed. In the second session, anthropometric measurements were taken, including height and body weight, and Body Mass Index (BMI) values were calculated. Additionally, calendar age (chronological age) and sport age (the duration of regular participation in sports) were recorded. In the third session, motor performance assessments were conducted, including a 30 m sprint speed test, vertical jump test, and agility test. All measurements were carried out on the same day. Height, body weight, BMI, calendar age, and sport age data were collected in the morning (between 09:00 and 09:30), while motor performance tests were performed in the afternoon (between 14:00 and 15:00). The performance tests were administered in the following order: 30 m sprint speed, vertical jump, and agility test, with a five-minute rest period between each test. During the measurements, appropriate rest and preparation conditions were provided to ensure optimal physical performance of the participants. The flow of the research process is presented in Figure 1.

2.3. Anthropometric Measurements

Height and body weight measurements were conducted following the standard protocols of the International Society for the Advancement of Kinanthropometry (ISAK) [14]. Height was measured using a Seca 213 Portable Stadiometer (Seca GmbH & Co. KG, Hamburg, Germany) while participants stood barefoot with their head aligned in the Frankfurt horizontal plane, chin parallel to the floor, and body in an upright posture. Measurements were recorded in centimeters (cm). Body weight was measured using a Tanita SC-330 Body Composition Analyzer (Tanita Corporation, Tokyo, Japan), employing the foot-to-foot bioelectrical impedance analysis (BIA) method. The measurements were recorded in kilograms (kg).

2.4. Thirty-Meter Sprint Test

For the sprint speed test, the start and finish lines were marked with tape. The participants were instructed to stand behind the starting line and to begin running at maximum effort upon hearing a whistle signal. The timing commenced when the participants passed through the first photoelectric gate and stopped when they crossed the second gate at the finish line. The Brower Timing Systems Wireless Sprint System (Brower Timing Systems, Draper, UT, USA) was used for all measurements. The test was performed twice, with a 4 min rest interval between trials, and the best result was recorded in seconds (s) [15].

2.5. Illinois Agility Test

Agility performance was assessed using the Illinois Change of Direction Test. The test setup consisted of a 10 m long and 5 m wide course marked with eight cones. The participants were shown the running direction in advance, and the testing protocol was clearly explained. The timing started when the participant triggered the photoelectric gate at the beginning of the test and stopped when they crossed the finish gate. The same Brower Timing Systems device was used (Brower Timing Systems, Draper, UT, USA). The test was conducted twice with a 4 min rest between attempts, and the best result was recorded in seconds (s) [15].

2.6. Vertical Jump Test

To assess explosive power, the squat jump test was employed [16]. The participants squatted to approximately 90 degrees of knee flexion and, after holding this position for three seconds, performed a maximal vertical jump with their hands placed on their hips. Jump height was measured using the Optojump Next system (Microgate, Bolzano, Italy). The vertical jump height was recorded via a sensor attached to a belt worn at the waist, and the results were expressed in centimeters (cm). The test was performed twice, with a 4 min rest interval between trials, and the highest value was used for analysis.

2.7. Data Analysis

Statistical analyses in this study were conducted using two different software programs. Initially, the distribution characteristics of the variables were assessed using the Kolmogorov–Smirnov test via GraphPad Prism (version 9.5.1; GraphPad Software, San Diego, CA, USA). Descriptive statistics, including maximum, minimum, mean, and standard deviation values, were also calculated for all variables. Since the data did not follow a normal distribution, the relationships among the variables were examined using the non-parametric Spearman’s correlation analysis. A significance level of p < 0.05 was adopted for all statistical tests. Regression analyses were conducted using IBM SPSS Statistics software (version 26.0; IBM Corp., Armonk, NY, USA). Given the non-normal distribution of certain variables, logarithmic transformations were applied where necessary to meet the assumptions of normality, linearity, and multicollinearity. Variables that met the regression assumptions were included in multiple regression analyses, and the results were reported accordingly. The statistical significance was set at p < 0.05, and 95% confidence intervals (CI) were reported.

3. Results

The results of the Spearman correlation analysis among chronological age, training age, anthropometric variables, and motor performance parameters are presented in

Table 2. Spearman correlation analysis results.

	Age (years)	Training Age (years)	Height (cm)	Body Weight (kg)	BMI (kg/m2)	Vertical Jump (cm)	Agility (s)
Training Age (years)	0.819 **
Height (cm)	0.474 **	0.244
Body Weight (kg)	0.434 *	0.315	0.657
BMI (kg/m2)	0.211	0.245	0.189	0.832 *
Vertical Jump (cm)	0.519 **	0.465 *	0.066	0.210	0.222
Agility (s)	0.476 **	0.439 *	0.208	0.165	0.148	0.351
Thirty-Meter Sprint Time (s)	0.526 **	0.283	0.438 *	0.324	0.162	−0.012	0.233

* p < 0.05; ** p < 0.01.

.

As presented in Table 2, chronological age was strongly positively correlated with training age (r = 0.819, p = 0.0057) and moderately correlated with height (r = 0.474, p = 0.009), body weight (r = 0.434, p = 0.018), vertical jump (r = 0.519, p = 0.003), agility time (r = 0.476, p = 0.008), and 30 m sprint time (r = 0.526, p = 0.003). A low positive correlation was observed between age and BMI (r = 0.211, p = 0.271). Training age showed moderate positive correlations with vertical jump (r = 0.465, p = 0.010) and agility time (r = 0.439, p = 0.016) and low correlations with height (r = 0.244, p = 0.200), body weight (r = 0.315, p = 0.095), BMI (r = 0.245, p = 0.198), and sprint time (r = 0.283, p = 0.136). Height was strongly correlated with body weight (r = 0.657, p = 0.072), while its correlations with BMI (r = 0.189, p = 0.323), vertical jump (r = 0.066, p = 0.731), agility (r = 0.208, p = 0.277), and sprint time (r = 0.438, p = 0.017) ranged from low to moderate. Body weight exhibited a strong correlation with BMI (r = 0.832, p = 0.022) and low correlations with vertical jump (r = 0.210, p = 0.274), agility time (r = 0.165, p = 0.389), and sprint time (r = 0.324, p = 0.085). Similarly, BMI showed low positive correlations with all three motor performance variables: vertical jump (r = 0.222, p = 0.246), agility (r = 0.148, p = 0.440), and sprint (r = 0.162, p = 0.399). Vertical jump and agility time were weakly correlated (r = 0.351, p = 0.061), while the correlation between vertical jump and sprint time was negative but negligible (r = −0.012, p = 0.947). Agility time and 30 m sprint time were weakly positively correlated (r = 0.233, p = 0.222).

All correlations are visually represented in the heatmap provided in Figure 2.

The color scale in Figure 2 represents correlation coefficients ranging from −1 to +1. Dark blue areas indicate strong positive correlations between variables, while light blue areas represent weaker positive relationships.

Multiple linear regression analyses were conducted to predict performance outcomes in speed, agility, and jumping. Separate regression models were constructed for each dependent variable, with height and body mass index (BMI) included as predictor variables (

Table 3. Results of regression analysis on log-transformed performance indicators.

Dependent Variable	Predictor	B	Std. Error	β (Beta)	t	p	95% CI Lower	95% CI Upper
Thirty-Meter Sprint Time (s)	Constant	−0.524	0.481	–	−1.09	0.286	−1.51	0.46
	Height (cm)	0.483	0.214	0.401	2.26	0.033 *	0.04	0.92
	BMI (kg/m2)	0.050	0.065	0.138	0.78	0.444	−0.08	0.18
Agility (s)	Constant	0.220	0.552	–	0.40	0.694	−0.92	1.36
	Height (cm)	0.347	0.246	0.270	1.41	0.169	−0.16	0.85
	BMI (kg/m2)	−0.006	0.074	−0.017	−0.09	0.931	−0.16	0.15
Vertical Jump (cm)	Constant	0.003	2.686	–	0.001	0.999	−5.52	5.52
	Height (cm)	0.474	1.196	0.077	0.40	0.695	−1.98	2.93
	BMI (kg/m2)	0.326	0.362	0.175	0.90	0.377	−0.42	1.07

* p < 0.05.

). Although body weight was initially considered for inclusion in the models, it was excluded due to multicollinearity issues (

Table 4. Predictor excluded from the regression models: Body weight (kg).

Dependent Variable	Predictor	β (Beta)	t	p	Partial r	Tolerance	VIF
Thirty-Meter Sprint Time (s)	Body Weight (kg)	−192.09	−1.18	0.248	−0.230	1.16 × 10−6	865,268.665
Agility (s)	Body Weight (kg)	−222.22	−1.28	0.212	−0.248	1.16 × 10−6	865,268.665
Vertical Jump (cm)	Body Weight (kg)	−327.94	−1.93	0.065	−0.360	1.16 × 10−6	865,268.665

).

As shown in Table 3, the results of the regression analysis for the speed variable indicated that the model approached statistical significance (F(2, 26) = 3.158, p = 0.059). The model explained 19.5% of the variance in speed (R² = 0.195, adjusted R² = 0.134). Among the predictors, height was found to be a significant predictor (β = 0.401, p = 0.033), whereas BMI was not statistically significant (β = 0.138, p = 0.444). The regression analysis for the agility variable did not yield statistically significant results (F(2, 26) = 1.005, p = 0.380). The model’s explanatory power was R² = 0.072, with an adjusted R² of 0.000. Neither height (β = 0.270, p = 0.169) nor BMI (β = –0.017, p = 0.931) emerged as significant predictors. Similarly, the regression model for the jumping variable was also non-significant (F(2, 26) = 0.544, p = 0.587), with an R² of 0.040 and an adjusted R² of –0.034. Neither height (β = 0.077, p = 0.695) nor BMI (β = 0.175, p = 0.377) significantly predicted jumping performance.

As presented in Table 4, the variable Body Weight (kg) was excluded from all regression models. This decision was based on the presence of severe multicollinearity, indicated by an extremely low tolerance value (1.16 × 10⁻⁶) and an excessively high Variance Inflation Factor (VIF = 865,268.665). Additionally, Body Weight (kg) was not a statistically significant predictor in any of the models (low t-values, p > 0.05). Therefore, to maintain the statistical validity and interpretability of the models, this variable was excluded from the final analyses.

4. Discussion

The primary aim of this study was to examine the influence of chronological age, training age, and anthropometric variables (height, body weight, and body mass index—BMI) on key motor performance components such as sprinting, agility, and vertical jump among handball athletes. The findings revealed that both age and training duration were significantly associated with certain motor performance parameters. In contrast, structural variables such as BMI did not exhibit a meaningful effect on performance outcomes. While height emerged as a significant factor in sprint performance, more complex motor abilities such as agility and vertical jump appear to be shaped by a wider range of physiological and neurological factors.

A strong correlation was observed between chronological age and training age, indicating that as athletes become older, they are more likely to accumulate greater sports experience, which may, in turn, contribute to improved motor performance. This result is consistent with previous findings highlighting the combined influence of age and athletic experience on the development of motor skills [17,18,19]. Therefore, chronological age should not be regarded solely as an indicator of biological maturity but also as a determinant of athletic development.

Moreover, the moderate correlations observed between chronological age and height, body weight, vertical jump, agility, and sprint time suggest that physical capacity may increase with age [20,21]. However, this increase is not uniform across individuals and appears to be moderated by personal factors. Thus, the impact of age on motor performance should be interpreted in relation to genetic background, training methodology, and environmental influences.

The significant relationships found between training age and variables such as agility and vertical jump highlight the importance of consistent training in the development of these motor skills. These findings align with prior research indicating that motor learning and neuromuscular adaptation are closely linked to training history [6,13,22]. Hence, training age plays a critical role in enhancing agility and explosive strength.

The regression analysis of sprint performance identified height as the only significant predictor, whereas BMI did not contribute meaningfully. This finding supports the literature suggesting that sprint ability is associated with dynamic components such as technical skill [23], muscle–tendon elasticity [24], and the direction of ground reaction forces [16,25]. Consequently, sprinting should be analyzed not only through structural metrics but also through biomechanical and technical dimensions.

The regression model for agility performance was not statistically significant, and structural variables such as height and BMI were not found to be effective predictors. This outcome indicates that agility is not solely a physical attribute but also includes cognitive and coordinative components such as decision-making, directional changes, and balance [26,27,28]. Therefore, agility should be assessed as a multifactorial skill integrating both physical and cognitive domains.

Similarly, the regression analysis for vertical jump performance was not statistically significant, with neither height nor BMI contributing as meaningful predictors. Vertical jump ability, as reported in the literature, is more closely related to relative strength, muscle fiber composition, and technical proficiency [23,29]. As such, assessments of explosive power should be grounded in functional and technical indicators rather than purely structural ones.

The BMI variable did not show significant associations with any of the motor performance components in either correlation or regression analyses. This suggests that BMI is an inadequate indicator for performance assessment and that more detailed measures of body composition, such as fat percentage and lean muscle mass, would be more appropriate [29,30,31]. Accordingly, performance evaluations in athletic populations should prioritize indicators such as lean mass, muscle distribution, and segmental body composition over generalized metrics like BMI.

Taken together, these findings underscore that motor performance cannot be comprehensively explained by limited variables such as age, height, or BMI. Abilities like agility, sprinting, and jumping are shaped through a dynamic interaction of genetic predispositions, body composition, training content, technical skill, and neuromuscular control [32,33,34]. Therefore, adopting a holistic and multidimensional approach in performance analysis is essential for accurately assessing and supporting athlete development.

Limitations

While this study offers valuable insights into the effects of age, training experience, and anthropometric characteristics on motor performance, several limitations should be acknowledged when interpreting the findings.

First, the sample was limited to male handball players within a specific age range, which restricts the generalizability of the results to broader populations, such as female athletes, younger age groups, or players at different competitive levels. Additionally, due to the relatively small sample size, subgroup analyses based on player positions could not be conducted. Although all participants were from the same team and trained under the supervision of the same coaching staff following a standardized training program, this homogeneity does not allow for comparisons across positional roles or playing levels. Second, the cross-sectional design of the study limits the ability to draw causal inferences. While relationships between variables were identified, longitudinal research is needed to better understand how motor performance evolves in relation to age, training background, and anthropometric changes. Third, only basic anthropometric data (height, body weight, and BMI) were included in the analysis. More detailed body composition variables, such as body fat percentage and lean body mass, were not assessed due to logistical constraints. This limited the ability to explore physiological explanations for performance outcomes in greater depth. Fourth, psychological and cognitive factors—such as motivation, decision-making ability, and mental fatigue—were not considered in this study. These factors may influence motor tasks, particularly agility and explosive power, and should be integrated into future research for a more comprehensive understanding of performance. Finally, the study included only male participants, which limits the applicability of the findings to female athletes. Future studies should aim to include more diverse samples that account for gender differences and provide comparative insights across sexes, age groups, and competition levels.

In light of these limitations, future research is encouraged to adopt longitudinal designs, incorporate more detailed physiological and psychological assessments, and recruit larger, more heterogeneous samples to enhance the generalizability and depth of findings in the field of sports performance.

5. Conclusions

The findings of this study underscore the multidimensional nature of motor performance and the complex interplay among structural, experiential, and physiological factors that influence it. While physical attributes, training history, and individual characteristics are shown to affect performance, the degree and nature of these effects vary depending on the specific motor component in question. In particular, skills such as sprinting, agility, and vertical jumping cannot be fully explained by structural indicators alone; rather, they are more profoundly shaped by neuromuscular adaptations, technical execution, muscle–tendon function, and psychomotor competence. Accordingly, performance evaluation must move beyond superficial metrics and engage with a more holistic analysis of the athlete’s physiological and functional capacity. Furthermore, the results call into question the continued reliance on conventional indices, such as body mass index (BMI), as valid predictors of athletic performance. These findings suggest a need to reconsider the contextual relevance of such metrics within contemporary performance diagnostics. It is also evident that the development of motor performance cannot be fully understood in terms of chronological age alone; instead, the quality and duration of training experience must be incorporated into assessments of athletic progression. These conclusions collectively advocate for the design of training programs that are tailored to the individual, as standardized loading models may offer limited effectiveness in addressing the complexity of motor skill development. Ultimately, athlete monitoring and development should adopt a dynamic, multidimensional framework that integrates both scientific principles and personalized approaches.