INTRODUCTION

In tennis, the serve is the only stroke entirely under the player's control, unlike other strokes that are responses to the opponent. This stroke represents a challenge due to the complexity of its biomechanics and is considered the most complicated stroke in tennis^1,2. It requires the activation of multiple muscles in different body segments, coordinated to generate and combine vertical and horizontal forces, allowing the player to achieve an optimal serve in terms of ball speed and accuracy^3,4. For its analysis, a model has been developed that divides the serve into three phases and eight stages⁵. This model helps coaches, physical conditioning specialists, and sports physicians design specific exercises to improve performance and reduce the frequency of injuries in players^5,6.

Given the importance of the serve in modern tennis for winning a game, numerous studies have attempted to identify predictors of serve speed (SS). These studies evaluate physical performance through tests such as the overhead medicine ball throw (MBT) and the countermovement jump (CMJ), as well as anthropometric characteristics such as height, weight, body mass index, and the length of the upper and lower limbs, including the arm, forearm, thigh, and leg^6,7. Although the overhead medicine ball throw is the most studied upper body strength test for estimating serve speed, it is crucial to note that a possible limitation of this test is the difference in kinetics compared to the serve stroke^6,8,9.

Regarding anthropometric measurements, several studies have identified significant associations between player height and serve speed. One of the hypotheses proposed to explain this result is that greater upper and lower limb lengths could contribute to developing greater kinetic force in the serve, which is directly related to ball speed. Therefore, various studies measure the length of arms, forearms, thighs, and legs^10,11. In relation to the individual's weight and body mass index, their influence on serve speed has been associated with greater torque force in the muscle activation necessary for the rotation of a body segment^12,13. There is little evidence in the international context and no publications at the national level. In the absence of studies in sports medicine in the area of tennis, this study aims to evaluate for the first time in the national context, the relationship between the anthropometric profile and serve speed in male tennis players at a sports center in Lima, Peru during the years 2023 and 2024.

METHODOLOGY

RESULTS

Seventy-nine non-professional tennis players who regularly participate in national tournaments were analyzed. The mean age of the tennis players was 26.33 (5.47) years, with an average of 20.42 (2.05) weekly playing hours and 12.59 (5.21) years of experience. Regarding anthropometric characteristics, the average height of the players was 174.61 (4.17) cm, and the average weight was 72.20 (6.97) kg. The average BMI was 23.67 (2.04) kg/m². Body segment lengths were 33.22 (1.49) cm for the arm, 26.18 (1.04) cm for the forearm, 40.98 (1.48) cm for the thigh, and 41.63 (1.22) cm for the leg. The average serve speed was 94.38 (9.98) km/h. (Table 1).

Correlation tests revealed several significant relationships between the studied variables. Height showed a very strong positive correlation with serve speed (r = 0.796, p < 0.001). Weight also presented a significant correlation (r=0.533, p < 0.001), as did arm circumference (r=0.583, p < 0.001), forearm (r=0.627, p < 0.001), thigh (r=0.436, p < 0.001), and leg (r=0.585, p < 0.001). Weekly playing hours (r=0.611, p < 0.001) and years of experience (r=0.435, p < 0.001) also positively correlated with serve speed. However, BMI (r=0.161, p=0.158) and age (r=0.192, p=0.090) did not show significant correlations. These results suggest that both physical characteristics and the time dedicated to training are important for serve performance (Table 2).

The linear regression analysis identified several significant predictors of serve speed among the studied tennis players. Height (B = 1.91, p < 0.001), weight (B=0.76, p < 0.001), arm circumference (B=3.88, p < 0.001), forearm (B=6.00, p < 0.001), thigh (B=2.94, p < 0.006), and leg (B=4.79, p < 0.001) were significantly associated with higher serve speed. Additionally, weekly playing hours (B=2.97, p < 0.001) and years of experience (B=0.83, p < 0.001) also proved to be important predictors. On the other hand, BMI and age did not show a significant association in this model. These findings highlight the importance of specific physical dimensions and experience in serve performance among non-professional tennis players, emphasizing the need for personalized approaches in training to enhance performance (Table 3).

DISCUSSION

The study analyzed 79 male tennis players, finding that height (r = 0.796) and weight (r = 0.533) are significant predictors of serve speed. Other anthropometric measures, such as arm and forearm length, also showed significant correlations. Weekly playing hours and years of experience correlated positively with serve speed. Linear regression analysis showed that height (B = 1.91, p << 0.001), weight (B=0.76, p << 0.001), and forearm length (B=6.00, p << 0.001) are significant predictors of serve speed. Weekly playing hours (B=2.97, p << 0.001) and years of experience (B=0.83, p << 0.001) also positively influenced.

Villouta et al. studied the anthropometric characteristics of elite young tennis players in Chile, finding that in males, the average weight was 64.3 kg, height was 1.74 m, and body fat percentage was 16.6%. In our study with Peruvian tennis players, the average weight was 72.20 kg, height was 174.61 cm, and the average BMI was 23.67. Additionally, we found significant correlations between height, weight, and limb dimensions with serve speed (¹⁶).

Sanchez-Pay found that body mass (r = 0.934), height (r = 0.914), and BMI (r = 0.330) had significant correlations with serve speed in national tennis players. Similarly, arm (r = 0.818), forearm (r = 0.858), and leg (r = 0.773) measurements showed positive correlations. These results are consistent with previous findings on the relevance of height and limb measurements for serve speed. However, they differ regarding the correlation of BMI, as our results did not find a significant relationship for this variable (⁶).

Vaverka et al. identified that body height had a significant correlation with the fastest serve speed (r = 0.52), the average first serve (r = 0.55), and the average second serve (r = 0.37). Similarly, the results of this study also support the importance of height in serve speed (10). Wong et al. reported a positive correlation between height (r = 0.542) and BMI (r = 0.577) with serve speed, with the BMI correlation being significant. Similar to what Wong et al. observed, this study identified height as a significant factor in serve speed. However, in contrast, this study did not find a significant correlation for BMI, highlighting a notable discrepancy in findings related to this variable (¹⁷).

Hayes et al. found a significant correlation between body weight (r = 0.68) and serve speed but a non-significant correlation for BMI (r = 0.31). Consistent with these findings, the present study also identified body weight as a significant factor, and similar to Hayes et al., did not find a significant correlation for BMI (³).

Fett et al. reported positive and significant correlations for height (r = 0.31), body mass (r = 0.44), BMI (r = 0.40), and the medicine ball throw (r = 0.52) with serve speed. While the results of this study agree on the importance of height and body mass, there is a notable discrepancy regarding BMI, as this study did not find a significant correlation for this variable (¹⁸). Bonato et al. found significant correlations between height and the speed of the first (r = 0.79) and second serve (r = 0.80), supporting the findings of this study on the importance of height in serve speed (¹⁹). Finally, Baiget et al. found that height had significant correlations with the speed of the first (r = 0.503) and second serve (r = 0.486), while BMI did not show significance (r = 0.26 and r = 0.125). Similar to the results of this study, they reinforce the importance of height and agree that the correlation of BMI is not significant (²⁰).

Among the limitations in interpreting the results, the sample size, although calculated to ensure statistical power, is limited to 79 tennis players from a single sports center, restricting the generalizability of the findings. Furthermore, the cross-sectional observational design prevents establishing causal relationships, allowing only the identification of associations. Anthropometric and serve speed measurements can vary between evaluators, despite using certified anthropometrists and calibrated equipment. The study did not consider factors such as individual technique, specific training level, and environmental conditions. The lack of diversity in the sample, composed exclusively of male tennis players from Lima, Peru, limits the applicability of the results. Variables of weekly playing hours and years of experience were self-reported, affecting their accuracy. Evaluation in a controlled environment might not fully reflect real playing conditions or capture the consistency of the serve during a match. Recognizing these limitations is crucial for the appropriate interpretation of the results and planning of future studies.

CONCLUSIONS

In conclusion, this study identified height and weight as significant predictors of serve speed in Peruvian tennis players. Additionally, limb measurements and years of playing experience positively influence, while BMI did not show a significant correlation.