Does stroke performance in amateur tennis players depend on functional power generating capacity?

Delgado-García, Gabriel; Vanrenterghem, Jos; Muñoz-García, Alejandro; Molina‐Molina, Alejandro; Soto-Hermoso, Víctor M.

doi:10.23736/s0022-4707.18.08518-3

Cited by 14 publications

(17 citation statements)

References 32 publications

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“…The MBTs require the ability to transfer power from the lower to upper limbs [ 1 ]. The MBT (simulating forehand and backhand strokes) has been used to measure strength in tennis players [ 34 ] showing a high correlation with trunk rotation and flexion strength [ 35 ] and the speed of forehand shot and backhand accuracy shot [ 36 ]. Conversely, MBT overhead (frequently used to predict the speed of the serve) which includes a large activation of rectus abdominis (used in tennis serve), has not found a very high correlation in junior ( r = 0.12–0.60) [ 1 , 9 , 10 ] or adult tennis players [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Fitness testing in tennis: Influence of anthropometric characteristics, physical performance, and functional test on serve velocity in professional players

et al. 2021

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The aims of this study were to examine the relationship between anthropometric variables, physical performance, and functional test with serve velocity regarding tennis players’ level and to design regression models that effectively predict serve velocity. A sample of sixteen male tennis players participated in this study (national level = 8, professional level = 7). Anthropometric measurements (body mass, height, body mass index and body segments) and physical test (hand strength, countermovement jump, jump on serve, and serve velocity) and functional test (medicine ball throw overhead and shot put) were performed. No differences in anthropometrics and physical test were found between national and professional levels. A significant positive correlation (p < 0.05, ranging for 0.603 to 0.932) was found between some anthropometrics measurements (body mass, height, arm, forearm, and leg segments), physical parameters (hand strength, countermovement jump) and functional test (medicine ball throw shot put and overhead) with serve velocity for all tennis players. Multiple regression analysis indicated that medicine ball throw shot put was the most important test to explain serve velocity (r2 = 0.869). The results showed how the combination of physical and anthropometric factors have an impact on serve velocity. In addition, a new functional fitness test (medicine ball throw shot put) is proposed as an alternative to traditional medicine ball throw overhead due to its high reproducibility (inter-trial reliability) and predictive validity values, as well as by multi-segmental coordination movement similar to tennis serve.

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Section: Discussionmentioning

confidence: 99%

Fitness testing in tennis: Influence of anthropometric characteristics, physical performance, and functional test on serve velocity in professional players

et al. 2021

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“…Usually, the alienation angles, linear velocities, and angular velocities are factors to be evaluated in the training of a professional tennis player [ 46 , 62 ]. Nevertheless, the analysis of the action execution is costly and involves kinetic analysis with additional instrumentation [ 63 ]. Our method shows a valuable tool based only on kinematic information provided by optical sensors.…”

Section: Resultsmentioning

confidence: 99%

An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

Valencia-Marin

Pulgarín-Giraldo

Velásquez-Martínez

et al. 2021

Sensors

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Motion capture (Mocap) data are widely used as time series to study human movement. Indeed, animation movies, video games, and biomechanical systems for rehabilitation are significant applications related to Mocap data. However, classifying multi-channel time series from Mocap requires coding the intrinsic dependencies (even nonlinear relationships) between human body joints. Furthermore, the same human action may have variations because the individual alters their movement and therefore the inter/intraclass variability. Here, we introduce an enhanced Hilbert embedding-based approach from a cross-covariance operator, termed EHECCO, to map the input Mocap time series to a tensor space built from both 3D skeletal joints and a principal component analysis-based projection. Obtained results demonstrate how EHECCO represents and discriminates joint probability distributions as kernel-based evaluation of input time series within a tensor reproducing kernel Hilbert space (RKHS). Our approach achieves competitive classification results for style/subject and action recognition tasks on well-known publicly available databases. Moreover, EHECCO favors the interpretation of relevant anthropometric variables correlated with players’ expertise and acted movement on a Tennis-Mocap database (also publicly available with this work). Thereby, our EHECCO-based framework provides a unified representation (through the tensor RKHS) of the Mocap time series to compute linear correlations between a coded metric from joint distributions and player properties, i.e., age, body measurements, and sport movement (action class).

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“…A great number of studies have found positive correlations between MBT distance or speed and SV or even other tennis strokes. 7,23,45,46 Fernandez-Fernandez et al 23 point out that MBT distance is an important predictor of SV in male tennis players. Fett et al 7 show that power values established from MBT is one of the best predictors for SV, in both male and female competitors, especially as age advances.…”

Section: Strength Power and Rommentioning

confidence: 99%

Determinant Physical Factors of Tennis Serve Velocity: A Brief Review

Colomar

Corbi

Brich

et al. 2022

International Journal of Sports Physiology and Performance

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Purpose: To review the main physical aspects that could positively or negatively influence serve velocity (SV). Methods: An examination of existing literature including studies analyzing positive (biomechanical aspects, anthropometrics, range of motion, strength, and power) and negative (competition-induced fatigue) associations to SV are summarized in this review. Results: Aspects such as lower-leg drive, hip and trunk rotations, upper-arm extension, and internal rotation seem to be the major contributors to racquet and ball speed. Favorable anthropometric characteristics, such as body height, arm length, and a greater lean body mass, seem to positively influence SV. Also, strength indicators such as maximal isometric strength and rate of force development in specific joint positions involved in the kinetic chain alongside upper-body power seem to be related to faster serves. On the other hand, the effects of prolonged or repetitive match play may impair the aforementioned factors and negatively influence SV. Conclusions: Following specific serving models that seem to enhance velocity production and efficient motion is highly recommended. Moreover, achieving a higher impact point, alongside shifting body composition toward a greater lean body mass, will most likely aid toward faster serves. Programs aiming at improving maximal isometric strength and rate of force development in specific positions involved in the kinetic chain including stretch-shortening cycle predominance and the mimicking of the serve motion seem of great interest to potentially increase SV. Effective recovery and monitoring of these variables appear to be essential to avoid impairments produced by continued or repetitive competition loads.

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Does stroke performance in amateur tennis players depend on functional power generating capacity?

Cited by 14 publications

References 32 publications

Fitness testing in tennis: Influence of anthropometric characteristics, physical performance, and functional test on serve velocity in professional players

Fitness testing in tennis: Influence of anthropometric characteristics, physical performance, and functional test on serve velocity in professional players

An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

Determinant Physical Factors of Tennis Serve Velocity: A Brief Review

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