Table of ContentsIntroductionReviewConclusionPractical ApplicationsIntroductionIn recent years, youth tennis has become increasingly competitive at all levels and ages. Recognition of the importance of physical preparation and development in youth tennis has also increased. Therefore, effective modalities that improve physical attributes relevant to performance are crucial. The serve is considered the most important strategic move. As a result, improving serve performance is a major goal of youth tennis programs. A key aspect of service performance is speed of service. The serve involves multiple segments of the body simultaneously producing force via complex coordinated muscle activations; called the “kinetic chain”. Fett, Ulbricht, and Ferrauti (2018) found that among elite youth tennis players, strength and power predictors explained 41–66% of the variance in serve speed in boys and 19–45% in the girls. Therefore, to increase serve speed and improve tennis performance, youth programs should aim to attenuate strength and power throughout the kinetic chain. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay Plyometric training is a well-established, appropriate, and safe modality for improving power, speed of movement, and execution of explosive actions in young athletes. Plyometric exercise causes muscles to rapidly stretch and shorten – a phenomenon called the “stretch-shorten cycle” (SCC). Plyometric training induces neural adaptations resulting in an improved ability to use the SCC and generate greater tension and subsequent force. Plyometric training can provide specific stimuli through manipulation of movement patterns, speeds, loads, and metabolic demands. For example, Chu (2003) proposed guidelines for tennis-specific plyometric training. Despite the theoretical potential for transfer to service, it has not been fully elucidated in the literature. Therefore, the purpose of this critical evaluation is to review studies of plyometric training interventions on serve speed in youth tennis players. Articles are sourced from SPORT Discus, PubMed, Google Scholar and relevant references from acquired studies. Search terms included “plyometric training,” “serve speed,” “youth,” and “tennis.” The Boolean modifiers “AND” and “OR” were used to refine the search to include alternative phrases such as “plyometrics,” “serve speed,” and “young.” Studies had to include a plyometric training intervention of at least six weeks duration, a control group, a sample of youth tennis players, and outcome measures including serve speed.ReviewAll studies except one , measured the maximum service speed. Fernandez-Fernandez and Ellenbecker (2013), Fernandez-Fernandez, De Villarreal, Sanz-Rivas and Moya (2016) and Pardos-Mainer, Ustero-Perez and Gonzalo-Skok (2017) all asked their players to perform eight serves maximums and used the highest recorded speed for later analyzes – to be recorded, services had to land within the service area. On the other hand, Behringer et al. (2013) measured the average speed of services, recorded on twenty services without any precision requirements.Behringer and colleagues say this strengthens the study's practical relevance and ecological validity. On the one hand, fatigue during matches reduces the speed of service. Thus, one could suggest that it is more important to improve the average speed on many services than to improve the maximum speed on a small number of services. However, this method primarily measures the effects of plyometric training on fatigue resistance during repeated submaximal serve performances, rather than on maximum serve speed. Additionally, the lack of precision requirements negates the potential ecological benefits of this method. If a player can serve with a greater average speed over 20 serves but cannot land in the service zone, performance is simply not improved. Additionally, Behringer and colleagues' findings are undermined by other limitations. This study reported a significant increase in service speed compared to post-intervention control. However, average speed decreased over the study period in the control group (–5.3%), while simultaneously increasing in the plyometric group (2.9%). This suggests that the significant difference reported by the authors was due to uncontrolled external variables and not just the intervention. Additionally, the authors did not report significant within-group improvement before and after the intervention in the plyometric group. Thus, the results of Behringer et al. (2013) should be interpreted with great caution. Behringer et al. (2013) and all other studies in this review did not blind the interventions. Blinding is a crucial methodological tool to reduce bias. Failure to use it may result in the Avis and/or John Henry effect. This is where participants assigned to the control condition either become disappointed and perform worse (John Henry effect) or are motivated and outperform the intervention group (Avis effect). Although it is difficult to effectively “blind” a plyometric intervention, it is possible to reduce these effects by manipulating the study design. For example, using certain repeated measures designs, waiting list control, or a stepped baseline. However, the studies in this review did not use any of these tools. This reduces the ability to interpret the results of these studies as strong evidence for the effects of plyometric training on serving speed. Additionally, the John Henry effect may partly explain the drop in serving speed observed in the control group in the study by Behringer et al. (2013). The intervention programs in the majority of studies were similar and considered suitable and appropriate for adaptation. Interventions lasted six to eight weeks, with training two to three times per week with sessions including upper and lower body exercises with familiarization and coaching throughout. Fernandez-Fernandez et al. (2016) and Pardos-Mainer et al. (2017) used similar programs, adhering to traditional plyometric programming principles. These two studies reported mixed but relatively positive results. Behringer et al. (2013) based on exercises based on NSCA recommendations, a needs analysis by Reid et al. (2003) and tennis-specific plyometric training recommendations from Chu (2003). However, these exercises were then grouped into one upper and two lower body "circles", consisting of three to four exercises. These were then carried out in an interval training style – unlike traditional plyometric training. Fernandez-Fernandez and Ellenbecker (2013) used arange of exercises including plyometrics, core and elastic tubing and medicine ball exercises. This intervention could be considered a non-strictly plyometric program, making the intervention unsuitable. It could be suggested that adaptations of these two somewhat inappropriate interventions would not be consistent with plyometric training. Both of these studies reported significant improvements in the plyometric group compared to the control group and after the intervention. However, it is difficult to interpret these studies as strong evidence for the effects of specifically plyometric training on serve speed in young tennis players, due to these methodological shortcomings. Neither Fernandez-Fernandez and Ellenbecker (2013), Pardos-Mainer et al. (2017) or Behringer et al. (2013) controlled training volume between the intervention and control groups. Participants in these plyometric groups performed the program in addition to the standard in-season diet. Therefore, these participants were exposed to a greater training volume than the control group. The improvements in serving speed reported by these studies could be due to greater training volume, rather than the intervention alone, making it difficult to interpret the results of these studies. On the other hand, Fernandez-Fernandez et al. (2016) controlled training volume between groups. This was achieved by asking participants to complete plyometric sessions as a replacement for tennis training periods. This provides a simple and effective way to equalize training volume between groups. However, many coaches would prefer significant training time focused on the technical aspects of the game – particularly with younger players, for whom skill development is of paramount importance (Reid et al. , 2007). Therefore, implementing plyometric training in this manner may not be suitable for youth tennis programs; reducing the applicability of the results of this study. Predicted age at peak velocity (PHV) is crucial in attempting to recognize whether power developments are caused by training interventions or by natural improvements that occur during maturation and growth. The start of this natural surge usually occurs about a year to a year and a half before PHV. Biological age of maturity can be calculated to identify the number of years before or after PHV participants at the time of measurement. Differences in biological age of maturity between groups may be a confounding variable, explaining some of the differences in serve speed between the control and plyometric groups. Therefore, it is crucial that studies assess the age of biological maturity of the sample to control for these confounding pediatric factors. Behringer et al. (2013) used a self-report pubertal stage test to measure maturational status. This produced nominal data, in which participants were assigned to one of five pubertal stages. This method provides an idea of ​​differences in pubertal stage between groups but is limited in its scope for statistical analyses. Pardos-Mainer et al. (2017) only measured age, height, weight, and BMI. In contrast, Fernandez-Fernandez and Ellenbecker (2013) and Fernandez-Fernandez et al. (2016) measured age of maturity and reported no significant differences between the control and plyometric groups. This suggests that the service speed improvements in this study are not caused by differences in maturity. However, the underlying mechanisms and adaptations remain.