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Machine Learning in Elite Volleyball pp 1–11 Cite as

Nature of Volleyball Sport, Performance Analysis in Volleyball, and the Recent Advances of Machine Learning Application in Sports

• Rabiu Muazu Musa 7 ,
• Anwar P. P. Abdul Majeed 8 ,
• Muhammad Zuhaili Suhaimi 7 ,
• Mohd Azraai Mohd Razman 8 ,
• Mohamad Razali Abdullah 9 &
• Noor Azuan Abu Osman 10  
• First Online: 19 June 2021

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3 Citations

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

This chapter presents the overview, nature, and history of the volleyball sport. The chapter also highlights the performance-related parameters that contribute to the successful delivery of performance in this sport. The recent advances in the application of various machine learning models towards solving the classification and regression problems associated with the data often acquired in the sporting domain are also provided. Moreover, the detailed procedures of the participants’ recruitment, data collection techniques via performance analysis as well as various univariate statistical analyses employed to achieve the purpose of the present study have also been presented.

• Machine learning models
• Clustering algorithms
• Univariate analysis
• Performance analysis
• Performance indicators

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Rabiu Muazu Musa & Muhammad Zuhaili Suhaimi

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Anwar P. P. Abdul Majeed & Mohd Azraai Mohd Razman

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Muazu Musa, R., Abdul Majeed, A.P.P., Suhaimi, M.Z., Mohd Razman, M.A., Abdullah, M.R., Abu Osman, N.A. (2021). Nature of Volleyball Sport, Performance Analysis in Volleyball, and the Recent Advances of Machine Learning Application in Sports. In: Machine Learning in Elite Volleyball. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-3192-4_1

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ORIGINAL RESEARCH article

Relative age effect on youth female volleyball players: a pilot study on its prevalence and relationship with anthropometric and physiological characteristics.

• 1 Department of Physical Education and Sport Science, Laboratory of Evaluation of Human Biological Performance, Aristotle University of Thessaloniki, Thessaloniki, Greece
• 2 Department of Nutritional Sciences and Dietetics, International Hellenic University, Thessaloniki, Greece
• 3 Institute of Primary Care, University of Zurich, Zurich, Switzerland
• 4 Exercise Physiology Laboratory, Nikaia, Greece

The relative age effect (RAE) on human performance has been well studied in many sports, especially in soccer; however, little information has been available about the prevalence of RAE in volleyball, and its role on anthropometric and physiological characteristics. The aim of the present study was to examine (a) the prevalence of RAE in selected (i.e., to be considered for the national team) and non-selected youth female volleyball players, and (b) the relationship of birth quarter (BQ) with anthropometric and physiological characteristics. Selected ( n = 72, age 13.3 ± 0.7 years, weight 62.0 ± 7.2 kg, height 1.72 ± 0.06 m) and non-selected female volleyball players ( n = 53, age 13.9 ± 1.1 years, weight 56.4 ± 7.3 kg, height 1.66 ± 0.06 m) performed a series of anthropometric and physiological tests. Twenty-six selected participants were born in the first quarter of the year, 19 in the second, 14 in the third, and 13 in the forth. The corresponding frequency by BQ in non-selected participants was 12, 12, 17, and 12. No association was observed between the number of participants and their frequency by BQ neither in the selected ( χ 2 = 2.79, p = 0.425) nor in the non-selected group ( χ 2 = 0.64, p = 0.886). Anthropometric and physiological characteristics did not vary by BQ ( p > 0.05). The absence of RAE in female volleyball players and the similarities of anthropometric and physiological characteristics among BQ might be due to technical-tactical character of this sport. These findings would be of great practical value for coaches and fitness trainers working with young volleyball players.

Introduction

The relative age effect (RAE) on human performance – i.e., the larger prevalence of athletes born in the first months (e.g., first quarter) of the year (“early born”) compared to their counterparts born in the last months (e.g., last quarter) of the year (“late born”) – has attracted an increased scientific interest during the last three decades considering its relevance for sport performance ( Barnsley et al., 1992 ) and other domains of human performance ( Alsaker and Olweus, 1993 ). This phenomenon indicated a potential advantage of “early born” compared to “late born” athletes ( Duarte et al., 2019 ). So far, most of the research of RAE in sports has been conducted in soccer ( Peña-González et al., 2018 ; Schroepf and Lames, 2018 ; Yagüe et al., 2018 ) and focused on the prevalence of RAE analyzing the distribution of births among months of year. On the other hand, less information exists in female volleyball ( Okazaki et al., 2011 ), which has been one of the most popular team sports in women worldwide ( Deaner et al., 2012 ), and – to the best of our knowledge – no study has ever examined the relationship of RAE with anthropometric and physiological characteristics in this sport.

The findings of existing literature on RAE in volleyball have been controversial so far. An absence of RAE has been observed in Dutch volleyball ( Van Rossum, 2006 ), elite Brazilian adult female volleyball players ( Parma and Penna, 2018 ) and Israeli Division 1 ( Lidor et al., 2014 ). On the other hand, RAE has been shown in the top Japanese volleyball league ( Nakata and Sakamoto, 2012 ), elite Brazilian youth female volleyball players ( Okazaki et al., 2011 ) and female United Kingdom school-children 11–18 years volleyball players ( Reed et al., 2017 ). With an exception, where an over-representation of the last quarters of the year for the whole population in recreational volleyball players was found ( Larouche et al., 2010 ), RAE indicated a higher prevalence of “early born” volleyball players especially in the younger age groups suggesting that RAE was attenuating with age in volleyball. This observation was in agreement with findings in soccer, where RAE was less remarkable in the older soccer players compared to their younger counterparts ( Brustio et al., 2018 ).

Considering the above-mentioned literature on volleyball with some studies observing RAE ( Okazaki et al., 2011 ; Nakata and Sakamoto, 2012 ; Reed et al., 2017 ) and others not ( Van Rossum, 2006 ; Lidor et al., 2014 ; Parma and Penna, 2018 ), it was suggested that further research on the prevalence of RAE in this sport was needed. Such information would be of great practical interest for volleyball practitioners and policy makers, since an observation of disproportionally high number of “early born” volleyball players would indicate a bias against their “late born” counterparts increasing the risk of drop-outs. This topic was particularly important in adolescence, which was a crucial period for the adherence in sports ( Soares et al., 2019 ). Furthermore, it would be of great practical importance to examine the relationship of RAE with anthropometric and physiological characteristics related to performance in female volleyball players. It has been shown that female volleyball players of high performance level were taller, jumped higher and had larger handgrip muscle strength than their counterparts of lower performance level ( Nikolaidis et al., 2015 ). Also, more successful female volleyball players were taller, lighter and scored higher in standing broad jump and medicine ball throw than their less successful counterparts ( Milić et al., 2017 ). Thus, it would be interesting to examine whether “early born” volleyball players would exhibit superior anthropometric and physiological characteristics compared to “late born.” Maturation has been considered previously as a confounding factor of RAE ( Peña-González et al., 2018 ), since early maturers exhibited superior performance than late maturers ( Cripps et al., 2016 ). Therefore, the aim of the present study was to examine (a) the prevalence of RAE in selected and non-selected female volleyball players, and (b) the relationship of RAE with anthropometric and physiological characteristics. Based on relevant research in soccer ( Buchheit et al., 2014 ; De Oliveira Matta et al., 2015 ), it was hypothesized that RAE would be observed in volleyball players, “early born” would have superior anthropometric and physiological characteristics than “late born” volleyball players, and RAE would have larger magnitude in selected than non-selected volleyball players. For the purpose of this study, “selected” referred to volleyball players who were selected by national team coaches to be considered for the national team of their age group.

Materials and Methods

A cross-sectional study design was used in the present research. Birth quarter (BQ), i.e., the quarter of birth, was defined as the independent variable, whereas anthropometric and physiological characteristics were designated as dependent variables. Selected ( n = 72, age 13.3 ± 0.7 years, weight 62.0 ± 7.2 kg, height 1.72 ± 0.06 m) and non-selected female volleyball players ( n = 53, age 13.9 ± 1.1 years, weight 56.4 ± 7.3 kg, height 1.66 ± 0.06 m) participated in the present study. Selected volleyball players competed in volleyball clubs in Athens. Non-selected volleyball players were members of two youth academies of competitive volleyball clubs from Athens (Greece). All procedures were in accordance with the Declaration of Helsinki as revised in 2008 and approved by the local Institutional Review Board. Participants’ parents or guardians provided informed consent prior to exercise testing session. All participants played volleyball at least three years before the study, had three to four training sessions and one official match weekly.

The testing session was carried out during competitive period in indoor volleyball court. It lasted 90 min, and included a supervised warm-up (10 min submaximal running and 5 min stretching exercises) and the following tests in the specific order: weight, height, skinfolds’ thickness, sit-and-reach test (SAR), Abalakov jump (AJ), four tests of isometric muscle strength (right and left handgrip, lifting with extended and flexed knees) and 20 m endurance shuttle run test (SRT). Two trials were performed for SAR, AJ, and right and left handgrip test, and the best score was recorded for each of these tests. 1 min break was provided between trials and 5 min break among tests. Although this physical fitness test battery was not sport-specific, e.g., it did not include tests corresponding to movements usually performed in volleyball, the selected tests have been used widely due to their ability to discriminate volleyball players by performance level and playing position ( Nikolaidis et al., 2015 ; Sattler et al., 2015 ; Brazo-Sayavera et al., 2017 ; Milić et al., 2017 ; Paz et al., 2017 ).

An electronic scale (HD-351 Tanita, Arlington Heights, IL, United States) and a stadiometer (SECA, Leicester, United Kingdom) were used to measure weight and height, respectively. Body mass index (BMI) was calculated using the formula “weight (kg)/height (m) 2 .” Body fat percentage (BF%) was predicted using the sum of ten skinfolds’ thickness (cheek, wattle, chest I, triceps, subscapular, abdominal, chest II, suprailiac, thigh, and calf; skinfold caliper Harpenden, West Sussex, United Kingdom) ( Parizkova, 1978 ). The difference from the age at peak height velocity (Δaphv) was evaluated only in the selected group – because sitting height was measured only in this group – and was used as a measure of maturation ( Mirwald et al., 2002 ). A parameter that was evaluated only in the selected Opto-jump system (Microgate Engineering, Bolzano, Italy) was used to measure AJ, i.e., jumping ability of single vertical jump with countermovement and arm-swing ( Bosco et al., 1983 ). Flexibility was tested by SAR on a box providing 15 cm advantage, i.e., the participant got a 15 cm score when reaching the toes of her feet ( Adam et al., 1988 ). Aerobic capacity was assessed by SRT, a widely used graded exercise test ( Adam et al., 1988 ). Isometric muscle strength was evaluated as the sum of four measures (right and left handgrip test, lifting with extended and flexed knees tests; use of digital handgrip and back-and-leg digital dynamometer; Takei, Tokyo, Japan) and expressed either in absolute (kg) or relative (kg/kg of body weight) values ( Heyward and Gibson, 2014 ).

All variables were expressed using mean and standard deviations. Statistical analyses were carried out on IBM SPSS v.20.0 (SPSS, Chicago, IL, United States) and GraphPad Prism v. 7.0 (GraphPad Software, San Diego, CA, United States). A t test examined differences in all measures between the selected and non-selected group. A chi-square test (χ 2 ) examined the association of the number of participants by BQ with expected values. Differences in – adjusted for age – anthropometric and physiological characteristics among BQ groups were examined by one-way multivariate analysis of covariance (MANCOVA). In the case of the selected group, the differences were adjusted for both age and Δaphv. The magnitude of the differences was tested by partial eta square, evaluated as small (0.010 < η p 2 ≤ 0.059), medium (0.059 < η p 2 ≤ 0.138), and large (η p 2 > 0.138) ( Cohen, 1988 ). The relationship among variables was examined by Pearson’s product moment correlation coefficient ( r ), whose magnitude was interpreted as trivial ( r < 0.10), small (0.10 ≤ r < 0.30), moderate (0.30 ≤ r < 0.50), large (0.50 ≤ r < 0.70), very large (0.70 ≤ r < 0.90), nearly perfect ( r ≥ 0.90), and perfect ( r = 1.00) ( Batterham and Hopkins, 2006 ). Significance was set at alpha = 0.05.

The descriptive characteristics of participants are presented in Table 1 . Twenty-six selected participants were born in the first quarter of the year, 19 in the second, 14 in the third, and 13 in the forth. The corresponding numbers in non-selected participants were 12, 12, 17, and 12. No association was observed between the number of participants and their frequency by BQ neither in the selected ( χ 2 = 2.79, p = 0.425) nor in the non-selected group ( χ 2 = 0.64, p = 0.886). In the non-selected group, there was no statistically significant difference among BQ groups on the combined dependent variables after controlling for age in the non-selected group [ F ( 36, 104 ) = 1.198, p = 0.239, Wilks’ Λ = 0.359, η p 2 = 0.239]. In the selected group, no statistically significant difference among BQ groups on the combined dependent variables after controlling for age and Δaphv [ F ( 36, 157 ) = 0.881, p = 0.663, Wilks’ Λ = 0.581, η p 2 = 0.165] ( Figures 1 – 3 ). The relationship of anthropometric and physiological characteristics with age in the non-selected group was shown in Figures 4 – 6 .

Table 1. Descriptive statistics (mean ± standard deviation) of anthropometric and physiological characteristics of participants.

Figure 1. Age and anthropometric characteristics by birth quarter. G1 = born in January, February, and March; G2 = born in April, May, and June; G3 = born in July, August, and September; G4 = born in October, November, and December; BMI = body mass index; BF = body fat percentage. ∗ G1 older than G3 and G4, ∗∗ G2 older than G4 in selected participants at p < 0.05.

Figure 2. Jumping, flexibility, and aerobic capacity by birth quarter. G1 = born in January, February, and March; G2 = born in April, May, and June; G3 = born in July, August, and September; G4 = born in October, November, and December; SAR = sit-and-reach test; SRT = 20 m endurance shuttle run test. Error bars represent standard deviations.

Figure 3. Isometric muscle strength by birth quarter. G1 = born in January, February, and March; G2 = born in April, May, and June; G3 = born in July, August, and September; G4 = born in October, November, and December; “Isometric strength” refers to the sum of the four measures (right and left handgrip strength, lifting with extended and flexed knees. Error bars represent standard deviations.

Figure 4. Relationship of anthropometric characteristics with age in the non-selected group ( n = 53). BMI = body mass index; BF = body fat percentage. The shadow line represents 95% confidence intervals.

Figure 5. Relationship of jumping, flexibility and aerobic capacity with age in the non-selected group ( n = 53). SBJ = standing broad jump; SAR = sit-and-reach test; SRT = 20 m endurance shuttle run test. The shadow line represents 95% confidence intervals.

Figure 6. Relationship of isometric muscle strength with age in the non-selected group ( n = 53). “Isometric strength” refers to the sum of the four measures (right and left handgrip strength, lifting with extended and flexed knees. The shadow line represents 95% confidence intervals.

The relationship of anthropometric and physiological characteristics with age and Δaphv in the selected group can be seen in Table 2 . Age did not correlate with of the other measures. Δaphv correlated very largely with height, moderately with weight, and with small magnitude with left handgrip muscle strength.

Table 2. The relationship (Pearson correlation coefficient r ) of anthropometric and physiological characteristics with age and difference from the age at peak height velocity in the selected group ( n = 72).

The main findings of the present study were that, (a) RAE was not observed in selected and non-selected female volleyball players, (b) anthropometric and physiological characteristics did not differ among BQ groups, and (c) the relationship of anthropometric and physiological characteristics with age varied by performance group with stronger correlations observed in the non-selected than in the selected group.

The absence of RAE in the examined sample of female young volleyball players was in agreement with previous studies in volleyball that did not show any difference on the frequency of BQ groups ( Van Rossum, 2006 ; Lidor et al., 2014 ; Parma and Penna, 2018 ). An explanation of the absence of RAE in our sample might be that volleyball has been considered a team sport that did not require exceptional demands in physiological characteristics ( Lidor and Ziv, 2010a ) and was characterized by large variability in these characteristics ( Nikolaidis et al., 2012 ). However, it was acknowledged that other studies conducted on this sport ( Okazaki et al., 2011 ; Nakata and Sakamoto, 2012 ; Reed et al., 2017 ) observed RAE highlighting the overall conflicting findings in research on volleyball and addressing the need of further research on this topic. Based on the findings of the present study, it might be assumed that sport and human performances without high demands in physiological characteristics – e.g., aerobic capacity, muscle strength and speed – would attenuate the occurrence of RAE.

The absence of RAE in the present study was in disagreement with the existed literature on team sports with high demands in physiological characteristics. For instance, most studies ( Korgaokar et al., 2018 ; Peña-González et al., 2018 ; Raᵭa et al., 2018 ; Schroepf and Lames, 2018 ; Yagüe et al., 2018 ; Marques et al., 2019 ) in soccer have observed an occurrence of RAE, where most soccer players were born in the first quarter or half of the year. Moreover, it has been shown that the number of soccer players born in January would be twice the number of those born in December in the top five European leagues ( Raᵭa et al., 2018 ). An occurrence of RAE would have implications for talent identification and soccer players’ selection and would require action to balance the chances of success for players born in the end of a year ( Yagüe et al., 2018 ). On the contrary, such a bias in talent identification and players’ selection should not be a concern in volleyball.

The similar anthropometric and physiological characteristics among BQ were in agreement with the absence of RAE in the present study. This relationship has been examined previously in soccer, where some studies supported an association between BQ and these characteristics, i.e., “early born” showed superior characteristics than “late born” ( Pedretti and Seabra, 2015 ; Altimari et al., 2018 ), whereas other studies did not observe differences ( De Oliveira Matta et al., 2015 ; Junior et al., 2015 ; Lovell et al., 2015 ; Skorski et al., 2016 ; Peña-González et al., 2018 ). An explanation of the similar anthropometric and physiological characteristics among BQ might be the role of maturation as a covariate ( Lovell et al., 2015 ; Peña-González et al., 2018 ).

With regards to the role of chronological age, the findings in the non-selected group showed that weight, height and isometric muscle strength increased with age, whereas BMI, BF and the other physiological characteristics did not. On the contrary, no relationship was observed between age and these characteristics in the selected group. Considering the adolescence as a period with large changes in the characteristics of volleyball players ( Lidor and Ziv, 2010b ), the variation in the abovementioned relationship by performance level might be partially attributed to the smaller age range of the selected compared to the non-selected group indicating that the former group was more homogeneous than the latter one. In addition, the variation of this relationship when Δaphv – measure of maturation – was considered instead of chronological age, confirmed the important role of maturation during volleyball players’ selection ( Melchiorri et al., 2017 ; Nunes et al., 2019 ), since height (a major determinant of success in volleyball) correlated very largely with Δaphv.

A limitation of the present study was that it was conducted in young volleyball players and it would be needed caution to generalize the findings in adult volleyball players, as it has been observed in other team sports (e.g., soccer) that the prevalence of RAE might vary by age group ( Lovell et al., 2015 ; Korgaokar et al., 2018 ). Moreover, the administered fitness batter included tests corresponding to important parameters for volleyball performance (e.g., height and jump ability) ( Nikolaidis et al., 2015 ; Milić et al., 2017 ); however, future studies should include sport-specific tests to mimic volleyball movements. In addition, it was acknowledged that the adopted methodological approach to evaluate maturation based on a combination of anthropometric characteristics and chronological age ( Mirwald et al., 2002 ) provided only a proxy measure. Although this approach has been used widely in research on maturation and team sports performance ( Pion et al., 2015 ; Rubajczyk et al., 2017 ; Lovell et al., 2019 ; Rommers et al., 2019 ), it would be recommended that future studies use laboratory methods (e.g., Tanner scale, hand-wrist skeleton), too. On the other hand, strength of the study was its novelty as it was the first one to examine differences in anthropometric and physiological characteristics among BQ of volleyball players. These findings would be of both practical and theoretical importance for practitioners and scientists, respectively. From a practical perspective, it would be suggested that RAE should not be a concern of volleyball coaches and fitness trainers, in contrast with soccer where practitioners should manage the selection bias of their athletes due to the prevalence of RAE. Nonetheless, coaches and fitness trainers should monitor BQ of their volleyball players, especially in the context of players’ selection; in case they observed RAE, they should act (e.g., setting individualized fitness goals) to prevent drop-out of potential talents. From a theoretical point of view, the absence of RAE observed in the young volleyball players under examination might imply that human performance not relying on high levels of physical abilities would not be influenced by BQ at young age. Moreover, scientists interested in this topic should examine further the prevalence of RAE and its role on anthropometric and physiological characteristics especially in sports with more technical than physical demands. With regards to the role of performance level, recently it was observed in soccer that RAE was more prevalent in elite than in non-elite academies ( Bezuglov et al., 2019 ). Thus, future studies should be conducted on the variation of RAE by performance level in volleyball using large sample size to verify this trend.

The absence of RAE in female volleyball players and the similarities of anthropometric and physiological characteristics among BQ might be due to technical-tactical character of this sport. These findings would be of great practical value for coaches and fitness trainers working with young volleyball players.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the Institutional Review Board, Exercise Physiology Laboratory, Nikaia, Greece. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

SDP and PN: conceptualization, formal analysis, data curation, and visualization. SDP, SKP, and PN: methodology, validation, investigation, and resources. PN: software. SDP, SKP, TR, BK, and PN: writing – original draft preparation and writing – review and editing. SDP, BK, and PN: supervision and project administration.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The voluntarily participation of all athletes in the present study is gratefully acknowledged.

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Keywords : birth quarter, body composition, human performance, jumping ability, isometric muscle strength

Citation: Papadopoulou SD, Papadopoulou SK, Rosemann T, Knechtle B and Nikolaidis PT (2019) Relative Age Effect on Youth Female Volleyball Players: A Pilot Study on Its Prevalence and Relationship With Anthropometric and Physiological Characteristics. Front. Psychol. 10:2737. doi: 10.3389/fpsyg.2019.02737

Received: 17 October 2019; Accepted: 19 November 2019; Published: 03 December 2019.

Reviewed by:

Copyright © 2019 Papadopoulou, Papadopoulou, Rosemann, Knechtle and Nikolaidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Beat Knechtle, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Influence of mental energy on volleyball competition performance: a field test

Affiliations.

• 1 Graduate School of Leisure and Exercise, National Yunlin University of Science and Technology, Yunlin, Taiwan.
• 2 Graduate Institute of Sport Coaching Science, Chinese Culture University, Taipei, Taiwan.
• 3 Kinesiology, University of North Carolina at Greensboro, Greensboro, NC, United States of America.
• 4 Tainan University of Technology, Tainan University of Technology, Tainan, Taiwan.
• 5 Department of Sport Sciences, Zand Institute of Higher Education, Shiraz, Iran.
• PMID: 36992946
• PMCID: PMC10042163
• DOI: 10.7717/peerj.15109

Athletic mental energy is a newly emerging research topic in sport science. However, whether it can predict objective performance in competition remains unexplored. Thus, the purpose of this study was to examine the predictability of mental energy on volleyball competition performance. We recruited 81 male volleyball players ( M age = 21.11 years ± SD = 1.81) who participated in the last 16 remaining teams in a college volleyball tournament. We assessed participants' mental energy the night before the competition and collected their competition performance over the next 3 days. We used six indices of the Volleyball Information System (VIS) developed by the International Volleyball Federation (FIVB) to examine its associations with mental energy. All six factors of mental energy -motivation, tirelessness, calm, vigor, confidence, and concentration correlated with volleyball competition performance. Further, a hierarchical regression found mental energy predicted volleyball receivers' performance (R 2 = .23). The findings advance our knowledge of mental energy and objective performance in competition. We suggest that future studies may examine the effects of mental energy on different sports with different performance indices.

Keywords: Peak performance; Concentration; Emotional state; Optimal state of mind; Psychology of sport excellence.

©2023 Shieh et al.

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• Research Support, Non-U.S. Gov't
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• Volleyball*

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62 Interesting Facts About Volleyball

Last updated on June 21st, 2023

Volleyball like any other sport is meant to help players spend their time doing some physical activity which is also good for the mind. Volleyball is played across the world by people of varying ages and genders. It inculcates in us the value of team spirit and coordination. Let us learn 62 interesting facts about Volleyball , some interesting facts about its popular players and also read some common injuries and their solutions.

1. Volleyball was invented by William George Morgan, an American educator. Morgan was an acquaintance of basketball inventor James Naismith and the former was inspired by the new sport to invent his own. However, he wanted one that could be played indoors. Taking a badminton net and a soccer ball bladder, he created volleyball.

2. Volleyball had a dainty name: Mintonette. This is on account of the fact that it was similar to badminton (“minton” later turned to “mintonette). It was renamed “volleyball” by another educator, Alfred S. Halstead because the ball was volleyed back and forth. Halstead is credited for popularizing the sport.

3. Originally, volleyball was meant to be played indoors. It was also invented because Morgan wanted a game where players did not have to run. He wanted a game that was physical and required players to perform plenty of actions but does not require direct contact with opposing players.

4. The very first volleyball game was an exhibition played in 1896 at Massachusetts’ Springfield College, then known as the International YMCA Training.

5. Volleyball teams are made up of 6 players – 3 at the net and 3 on the backline. School teams often have 10 to 12 players.

6. Volleyball became a popular sport in Asian countries when it was added to the Asian Games in 1913 . Held in Manila, the Games welcomed volleyball to its list of sporting events.

7. The game of Volleyball that we know today was made official in 1947 after the Federation International de Volleyball was formed.

8. Since the formation of the FIVB, clothing restrictions were introduced, one of which stated that players were to wear knee length pants 47 cm in length, and no less than 3 cm above the knee.

9. Basketballs were once used to play the game but their weight prevented players from keeping the ball in flight. The first volleyballs, which were made from a basketball bladder, proved to be too light and slow.

10. The first “official” volleyball was commissioned by A.G. Spalding & Sons for William Morgan. The company designed a ball that had three layers – the innermost layer was made of latex bladder, the next layer was made of cheesecloth, and the outermost was made of leather.

11. There are size and weight requirements for volleyballs. The circumference should be between 25-27 inches while the weight must be from 9-12 ounces. The ball has 18 slightly rectangular panels divided into six sections, with each section consisting of three panels. It may be made of genuine or synthetic leather.

12. Standard indoor volleyballs are slightly smaller than beach volleyballs. They also tend to have a smoother surface and higher internal pressure.

13. There are six positions in the volleyball court. These are: the Setter, the Outside Hitter, the Opposite Hitter, the Middle Hitter, the Libero, and the Defensive Specialist. To truly excel in the game, each player must master their skills at their positions.

14. In a six-man team, there is one player who has a different-looking jersey. This is the libero – the player who can move freely on the court and off. He/She can replace a hitter who may not be effective on defense in the back row. The libero is not allowed to hit the ball in the front area of the 10-foot line. They may hit the ball behind this line provided they do not clear the top line of the net.

15. The offensive styles we now know as “set” and “spike” were first introduced in the Philippines . The ball was hit high by one player and another player hits the ball sharply to land on the opponent’s court. The hit is usually strong, which makes it difficult to defend. The spike or kill was called “bomba” and the spiker was referred to as a “bomberino”.

16. The 3-hit rule which states that players in one team can only hit the ball three times was introduced in 1920. This helped increase the momentum of the game and improve its action. Since the hits are limited, players must handle the ball in the best ways possible so they can score.

17. Beach volleyball is believed to have begun, unsurprisingly enough, on a beach in Waikiki, Hawaii . Players from the Outrigger Canoe Club put up a net on the beach and began a game of volleyball. However, the two-man team started in 1930 at the Athletic Club in Santa Monica. When other players failed to show up for a game, Paul Johnson, a member of the club, decided to play with just four players – two for each team. This setup became so popular that it is still being used today.

18. Beach volleyball became an official Olympic sport for men and women in 1996 at the Atlanta Olympics. It was a demonstration sport at the 1992 Olympics in Barcelona.

19. 1956 saw sitting volleyball introduced as injured soldiers looked for new ways to keep themselves fit and busy during rehabilitation.

20. The Heatwave for Sick Kids Beach Volleyball Tournament, held on July 12, 2014 in Canada, holds the current record for the world’s largest beach volleyball tournament. A total of 968 volleyball players took part in the tournament.

21. If you’ve ever played a game of volleyball, you will know that the game requires you to jump quite often. Did you know that a volleyball player will jump roughly 300 times during a match?

22. The first time volleyball was played in the Olympics was in 1964. The spectators in Tokyo witnessed a three-way competition fiercely fought by the men’s teams from Japan, Czechoslovakia, and the former USSR. Japan won bronze, Czechoslovakia placed second, and the USSR went home with the gold. In the women’s division, Poland garnered bronze, the USSR won silver, and the home team of Japan won their first-ever Olympic gold in the sport.

23. The first time colored volleyballs were allowed in tournaments was in 1998. This was to make it easier for spectators to see and follow the ball while watching the game on TV.

24. The longest volleyball set ever played took place in 2006 when Brazil and Italy battled it out at the FIVB World Championships for 2 hours, 37 minutes. Brazil took the title with an impressively close score of 73 – 71.

25. The longest marathon volleyball match that made it the Guinness World Record occurred in January 2017. It was played by SVU Volleyball in Amstelveen, Netherlands, and lasted for 101 hours. It had 63 matches, 338 sets, and ended with a 14,635 score.

26. The record for most passes in volleyball was set on February 5, 2010, in Raleigh, North Carolina . The record formed a part of a Guiness world record challenge by the Triangle Volleball Club as they raised funds for Haiti earthquake victims. The current record for most passes is set at 110.

27. The very first beach volleyball professional tournament took place at the Will Rogers State Beach in 1976. It was named Olympia World Championship of Beach Volleyball.

28. A time limit for service is set at 8 seconds. This means that a player who will serve must do so within 8 seconds after the referee blows the whistle. If the server fails or is late, a point is given to the other team and the service turn is forfeited. If the server serves the ball prior to the whistle, he/she is allowed to serve again without penalties.

29. The average volleyball game lasts for 60 to 90 minutes, depending on how many sets are played.

30. Volleyball games used to be played with a time limit of 8 minutes. Within this time, a team must have reached 15 points first or have a 20-point advantage.

31. To win in a competitive adult match, a team must emerge victorious in best-of-five sets. To win a set, a team must reach 25 points. If a fifth set is played, the target score is only 15 points. However, to win a set, the team should have a two-point advantage over the opposing team.

32. For a volleyball game to be played effectively indoors, the gym or facility must have a minimum vertical ceiling height of 23 feet.

33. In the past, only the serving team could earn a point if the opposing team fails to return the ball. This changed in 1999 when the Rally Scoring method was imposed. Under this new method, either team can earn a point regardless of which team served.

34. A one-handed block is called a “kong”. This is in reference to King Kong who swatted at planes with one hand as he balanced himself on the Empire State Building.

35. On October 2, 2008, the people of Taichung County in Taiwan gathered in Yung Shin Sports Park to set the record of the most number of people controlling volleyballs. There were 299 people who successfully attempted the feat. The participants had to control the ball for a minimum of 10 seconds.

36. The youngest volleyball player to win an international title in beach volleyball is China’s Xue Chen. Xue was 17 years old when she won at the China Shanghai Jinshan Open with partner Zhang Xi. The pair beat fellow Chinese players with a 23-21 and 21-14 score in just 38 minutes. The talented Xue stands at a towering 6 feet 3 inches.

37. On August 13, 2005, American Charles Frederick Kiraly, known as Karch, set the record as the oldest volleyball player to secure an AVP (Association of Volleyball Professionals) tour title. Kiraly, along with his partner Mike Lambert, won at Huntington Beach in California. He was 44 years old at the time. Kiraly holds the distinction of being the only volleyball player to win Olympic gold medals for playing both beach and indoor volleyball games.

38. Karch Kiraly also set another record as one-half of the oldest volleyball team to snatch a win at the Huntington Beach Open in 2003. His combined age with his teammate (Brent Doble) was 76 years and 117 days. The pair defeated Sean Scott and Todd Rogers.

39. There is only one women’s team to win three consecutive gold medals at the Olympics and it is Cuba ‘s national team. They won the medals in 1992, in 1996, and in 2000.

40. In beach volleyball, there is only one team to win gold medals for three consecutive times. Misty May-Treanor and Kerri Walsh-Jennings won in 2004, 2008, and 2012.

41. The country that holds the most number of national championships is Puerto Rico . The record is held by the same team – the Changos de Naranjito, which is a member of the country’s Men’s National League. The record runs between 1958 and 2004.

42. When the first beach volleyball game was broadcast live at the 1996 Olympics, more than 1 billion people watched. Twenty-four teams took part in the Olympic tournament. The games were won by Brazil (Sandra Pires and Jackie Silva) for the women’s division and by the U.S.A. (Karch Kiraly and Kent Steffes) for the men’s division. The game was first introduced at the 1992 Summer Games but only as a demonstration event.

43. The tallest volleyball player in the world is Wuttichai Suksara of Thailand . He towers over even other very tall players at 7’3.5″ At 13, Suksara was already 6’6″ tall. He has gigantism and has received medical treatments for this condition. He had difficulty finding shoes his own size, prompting a Japanese company to make shoes especially for him. After graduating from college, Suksara later enlisted in the army.

44. The shortest male volleyball player is Farhad Zarif of Iran . He stands at 5’5″ tall. In 2006, Zarif became a member of the national team. He currently plays for Paykan Tehran. He was awarded Best Libero seven times from 2006 to 2013.

45. The minimum height for volleyball players is 5 feet. The game requires high jumps at certain points of the game for certain positions, so height is considered an advantage. Different volleyball associations may have varying height standards, however. In many cases, the shortest player takes on the role of the libero or defensive specialist.

46. The position with the tallest height requirement is the Middle Hitter/Blocker. It is the blocker’s job to stop all offensive attacks from the opposing team and they can perform this best at a position above the net. Tall players have this leverage.

47. The next position that will benefit best from having a tall player is the Setter. Tall setters can fake a move and then sneak a strong hit over the net to the opposing team’s side. Although the setter usually “sets” the ball for the hitter, having a height advantage definitely helps when it’s time to dig or block.

48. The fastest volleyball serve made clocked at 83.3 mph courtesy of Ivan Zaytsev of Italy in a Volleyball National League game against Serbia . This record was set in May 2018. Zaytsev also holds the Olympic speed record for serving at 78.9 mph, sharing the distinction with Gyorgy Grozer and Christian Savani.

49. Among female players, the fastest serve made by far was performed by Serbia ‘s Tijana Boskovic. Her serve clocked in at 62.75 mph (101 kph). This occurred during the women’s tournament at the Rio 2016 Games. Boskovic’s performance bested Brankica Mihajlovic’s 61.5 mph (99 kph) serve. Boskovic was 19 when she became the very first volleyball player to break the 100 kph ceiling in Olympic history. Later in the day, the record was matched by Italy’s Paola Egonu.

50. The highest-paid professional volleyball player in the world is China’s Zhu Ting. She played for VakifBank Istanbul in Turkey for which she earned $1.68 million annually. She returned to China to rejoin the national team and prepare for the 2020 Olympics.

51. The first player to hit a salary of $1 million in professional volleyball is Randy Stoklos. He also earned MVP honors from AVP four times.

52. The FIVB (Federation Internationale de Volleyball) is the organization that governs all volleyball forms. They have chosen the top volleyball players of the century. For the male players, the honor goes to Karch Kiraly. For the female players, the title belongs to Regla Torres of Cuba. Torres has won three golds with Cuba’s national women’s team.

53. The richest volleyball (female) player in the world is American Gabrielle Reece. Reece began playing in high school and attended university using her volleyball sports scholarship. Her good looks also turned her into a cover girl and model, gracing magazine covers such as Shape, Women’s Sports and Fitness, and Playboy. She has also turned to hosting and acting. Her net worth is estimated at $10 million.

54. Among the male volleyball players, Wilfredo Leon is considered as the top earner . Leon started in the sport at a very young age, playing for his native Cuba’s team Capitalinos. At just 14, he began playing for the national team. He later defected and played for Zenit Kazan, a Russian Super League team. He led the team from one major win to another from 2015 until 2018. He now holds Polish citizenship. His estimated annual salary is $1.4 million.

55. The most successful coach in volleyball is Bernardo Rocha de Rezende of Brazil . Affectionately called Bernardinho, he has earned over 30 major volleyball titles in a colorful career spanning 20 years. He has coached both men’s and women’s Brazilian teams and has himself earned an Olympic silver as a player.

56. The lowest score ever recorded in volleyball is 25-1. It was in a match won by Chinese Taipei against the Maldives team in 2004 when they faced each other at the Asian Jr. Women’s Volleyball Championship.

57. The most number of aces served is seven, made by Australia’s Samuel Walker. This record was made when he played in Poland against Brazil.

58. The player who scored the most number of points in a match is Netherland’s Nimir Abdel-Aziz. In a match against France in 2021, he scored 43 points, shattering the previous record of 37 points set by China’s Jiang Chuan, also against France, in 2018.

59. The first Men’s Volleyball Championships were held in 1949 in Prague, Czechoslovakia.

60. Russia holds the title of being the country with the most World Volleyball Championships. They currently have 6 golds.

61. The country with the most number of inductees in the Volleyball Hall of Fame is the United States, with 62, followed by Brazil (15), and Russia (14).

62. Volleyball is ranked as the sixth most popular competitive sport in the world. However, according to the Olympic Program Commission, it is the most widely played. The sport has an estimated 900 million fans all over the world but it also has the most number of professional leagues.

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Home — Essay Samples — Life — Sports — Volleyball

Essays on Volleyball

Volleyball essay topics and outline examples, essay title 1: "the evolution of volleyball: from beach to olympics".

Thesis Statement: Volleyball has come a long way from its humble beginnings on the beaches of California to becoming a globally recognized Olympic sport.

• Introduction
• History of Volleyball
• The Early Beach Volleyball Scene
• Volleyball's Transition to Indoor Courts
• Volleyball's Inclusion in the Olympics

Essay Title 2: "The Physical and Mental Demands of Competitive Volleyball"

Thesis Statement: Competitive volleyball requires a unique combination of physical prowess and mental agility, making it a challenging but rewarding sport.

• The Physical Demands of Volleyball
• The Importance of Teamwork and Communication
• Strategies for Mental Toughness in Volleyball
• Training and Preparation for Competitive Volleyball

Essay Title 3: "The Impact of Volleyball on Personal Growth and Development"

Thesis Statement: Playing volleyball not only enhances physical fitness but also fosters personal growth, teaching valuable life skills such as teamwork, leadership, and perseverance.

• Physical Fitness Benefits of Playing Volleyball
• Building Character Through Teamwork
• Leadership Skills Developed in Volleyball
• Overcoming Challenges and Perseverance

Comparison and Contrast of Softball and Volleyball

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Misty May Treanor's Biography

A story about new volleyball coach, the impact of volleyball: academic, social, and personal growth.

Volleyball, known as a dynamic team sport, is played on a rectangular court that can be found indoors or outdoors, specifically on sand courts. The essence of the game lies in the interaction between two teams, each comprised of six players, all aiming to accumulate points by skillfully striking the ball over a net and successfully landing it on the opponent's side of the court.

In 1895, William G. Morgan, a physical education director at the YMCA in Holyoke, Massachusetts, USA, was credited with the invention of volleyball. Morgan aimed to invent a new game that combined elements of basketball, tennis, handball, and baseball, creating a less physically demanding alternative. Originally called "Mintonette," the game's name was later changed to volleyball due to the nature of the sport. The first official game of volleyball was played on July 7, 1896, at the YMCA in Springfield, Massachusetts. The sport quickly gained popularity and spread internationally. It was included in the program of the Summer Olympics for the first time in 1964. The Fédération Internationale de Volleyball (FIVB) was established in 1947 as the governing body for international volleyball competitions. Over the years, volleyball has undergone various rule changes and modifications, evolving into a fast-paced and dynamic sport. It is now played on both indoor and beach courts, with different variations such as six-player indoor volleyball and two-player beach volleyball.

In the United States, volleyball gained popularity in the early 20th century and has since grown into one of the most popular team sports. The sport is governed by USA Volleyball, the national governing body responsible for organizing national teams, tournaments, and development programs. Collegiate volleyball is particularly popular in the US, with both men's and women's teams competing at the National Collegiate Athletic Association (NCAA) level. The NCAA volleyball championships are highly anticipated events, attracting a large audience and showcasing the talent and skill of collegiate players. Beach volleyball has also gained significant traction in the US, with professional leagues and tournaments drawing large crowds and television viewership. The Association of Volleyball Professionals (AVP) hosts professional beach volleyball events across the country, featuring top players from around the world. The US has produced many successful volleyball players who have made significant contributions to the sport, both domestically and internationally. The country's national teams have achieved notable success in international competitions, including Olympic medals and World Championship titles.

Team Composition: A standard volleyball team comprises six individuals on either side of the net, with an equal distribution of three players in the front row and three players in the back row. Scoring: Points are awarded when a team successfully grounds the ball on the opponent's court or if the opposing team commits a fault. A team must win a rally to earn a point, and matches are usually played in sets. The team that reaches the specified point limit first wins the set. Serving: The game begins with a serve. The server must stand behind the end line and hit the ball over the net to start the rally. If the serve lands in the opponent's court or is not successfully returned, the serving team earns a point. Rally: Following the service, the teams participate in a rally where their objective is to sustain ball movement by executing no more than three touches to send it back over the net. It is essential for each team to utilize a maximum of three hits to successfully return the ball. Rotation: Players must rotate positions clockwise after winning a rally and gaining the right to serve. This ensures that each player has an opportunity to play in different positions on the court. Faults: Various faults can occur during a game, such as stepping on or over the boundary lines, touching the net, double contact, or committing a foot fault during a serve.

Karch Kiraly, Gilberto Amauri de Godoy Filho (Giba), Misty May-Treanor, Sheilla Castro

1. Volleyball has been an official Olympic sport since 1964 for both men and women. It is widely popular and highly anticipated during the Summer Olympics. 2. Volleyball is known for its fast-paced nature. On average, a volleyball can travel at speeds of up to 60 miles per hour (97 kilometers per hour) during a professional match. 3. Height plays a significant role in volleyball, particularly in blocking and spiking. The tallest recorded professional male volleyball player was Igor Omrčen from Croatia, standing at an impressive 7 feet 5 inches (226 cm). 4. Volleyball is one of the most popular sports globally, with an estimated 900 million fans worldwide. It is played in over 220 countries, making it one of the most widely participated team sports. 5. The longest recorded volleyball match lasted for a staggering 75 hours and 30 minutes, taking place in Kingston, North Carolina, in 1984. The marathon match was played by two teams of high school students. 6. The fastest recorded serve in volleyball history was achieved by Bartosz Kurek from Poland, who recorded a serve speed of 132 kilometers per hour (82 miles per hour) during a match in 2012.

Volleyball is an important topic to explore in an essay due to its widespread popularity and impact on both individuals and society. This sport brings people together, promotes physical fitness, and fosters teamwork and communication skills. Writing an essay about volleyball allows for an exploration of its rich history, from its origins in the late 19th century to its development as a global sport played at various levels. It offers an opportunity to delve into the rules, techniques, and strategies employed in the game, as well as the physical and mental benefits associated with playing volleyball. Furthermore, studying volleyball opens doors to understanding the cultural significance of the sport in different regions and its influence on communities. Essays on volleyball can also highlight the social and economic aspects, such as the growth of professional leagues, sponsorship deals, and the impact on tourism.

1. Engström, L. M., & Carlsson, T. (2014). Injury incidence and injury patterns in professional volleyball players of a national league. International Journal of Sports Physical Therapy, 9(3), 358-363. 2. Fernandes, R. J., & Almeida, P. L. (2017). Tactics in volleyball: A systematic review. Journal of Human Kinetics, 58(1), 225-241. https://doi.org/10.1515/hukin-2017-0059 3. Gonçalves, C. E., Figueira, B. E., Maçãs, V., Sampaio, J., & Leite, N. (2012). Effect of player position on movement behaviour, physical and physiological performances during an elite male volleyball game. Journal of Sports Sciences, 30(13), 1429-1437. https://doi.org/10.1080/02640414.2012.710757 4. Knapik, J. J., Steelman, R. A., Hoedebecke, E. L., Austin, K. G., Farina, E. K., Hammond, K. G., & Lieberman, H. R. (2018). A systematic review and meta-analysis on the effects of physical training on volleyball performance. Journal of Strength and Conditioning Research, 32(3), 892-907. https://doi.org/10.1519/JSC.0000000000002336 5. Lima, R., Oliveira, J., & Gonçalves, B. (2018). Effects of mental imagery on volleyball serve performance: A systematic review. Journal of Sports Sciences, 36(7), 776-787. https://doi.org/10.1080/02640414.2017.1340637 6. McHugh, M. P., & Cosgrave, C. H. (2010). To stretch or not to stretch: The role of stretching in injury prevention and performance. Scandinavian Journal of Medicine & Science in Sports, 20(2), 169-181. https://doi.org/10.1111/j.1600-0838.2009.01058.x 7. Mroczek, D., Lech, G., & Mroczek, G. (2015). The role of coordination abilities in the prevention of injuries in youth volleyball players. Biology of Sport, 32(1), 49-53. https://doi.org/10.5604/20831862.1127270 8. Nalepa, G., & Wołoszyn, N. (2018). Physical fitness and motor performance of elite and sub-elite female volleyball players. Polish Journal of Sport and Tourism, 25(4), 186-192. https://doi.org/10.2478/pjst-2018-0019 9. Sattler, T., Hadzic, V., Dervisevic, E., & Markovic, G. (2012). Vertical jump performance of professional male and female volleyball players: Effects of playing position and competition level. Journal of Strength and Conditioning Research, 26(6), 1532-1538. https://doi.org/10.1519/JSC.0b013e318234e66b 10. Sheppard, J. M., Gabbett, T. J., & Stanganelli, L. C. R. (2009).

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This article has been retracted.

Biomechanics of volleyball players' run-up and take-off link under deep learning.

1 Zhejiang Sci-Tech University, Hangzhou 310000, Zhejiang, China

2 Department of Physical Education, Central South University, Changsha 410083, China

3 China Volleyball College, Beijing Sport University, Beijing 100089, China

Associated Data

The data underlying the results presented in the study are available within the article.

In volleyball, the correct approach and start (including the number of steps and stride speed) are a prerequisite for all technical movements to attack. It can not only improve the horizontal speed of the athlete, but also properly convert the total speed into vertical speed, so that the hitting point is improved and the ball speed is accelerated. To explore the biomechanical characteristics of lower limb movements in the run-up and take-off stage of volleyball spiking, this paper takes four male volleyball players from the Physical Education College of X University as the research objects to analyze the kinematics and dynamics of the run-up process and the take-off process. This paper uses the precise recognition method under the background of deep learning to accurately capture the movements of the research object. This paper discusses the effects of time, speed, distance, knee, and hip parameters (angle, joint muscle torque, and power) on the effect of spiking techniques. It is expected to provide reference for the diagnosis, guidance, and muscle strength training of this special technical movement. The  research results show that the horizontal speed of No. 2 athlete is 3.62 m/s and the vertical speed is 2.71 m/s when he takes off. The  landing time is 0.375 s and the lift-off time is 0.16 s. The torque and power of the knee joint changed greatly during the take-off process, and the change of the hip joint was small.

1. Introduction

Spike is an important scoring method in volleyball. With the height of volleyball players, the level of jumping is getting higher and higher. To improve the effect of spiking, the players' offensive strategy has gradually changed from a flat attack to a three-dimensional attack. The techniques of front row spiking, front row coping, and backcourt spiking have been integrated, increasing the difficulty of the opponent's interception. In volleyball, the technical action of spiking is divided into four processes: run-up, take-off, air, and landing. Among these four processes, the run-up process and the take-off process are the most important. The effect of running and jumping on both feet directly determines the success rate and quality of technical and tactical actions such as blocking and spiking. There are also a large number of experts and scholars who have carried out kinematics and dynamics analysis and research on the various phases of the double-foot approach and take-off action. Some foreign studies are also trying to establish the action model of the human vertical jump, but the gap between scientific research and training practice still needs to be filled little by little. This article is based on the start-up stage of a complete spiking action, which lasts only 0.6 seconds. It is the basic stage of the entire spiking action. In this stage, if there are some unreasonable and unstable movements, it will greatly affect the subsequent technical movements such as flying, blocking, and spiking.

Deep learning is an important branch of machine learning. Unlike algorithms that involve specific tasks, it is based on the learning of representations from the data. Deep learning simulates the neural network model of the human brain, which uses multilevel nonlinear operation units in series to extract and transform data information. Each sequence takes the output of the previous sequence as input, and it takes the initial data as the input of the first sequence to obtain the final feature information. Deep learning technologies such as deep neural networks, convolutional neural networks, and recurrent neural networks have been widely used in computer vision, speech recognition, natural language processing, audio recognition, social networks, and other fields. In this paper, through the method of high-speed camera video acquisition, the accurate recognition algorithm in artificial neural network is used. It collects data on the running width, the running distance, and the dynamic change process of the knee and hip joints in the run-up and take-off stage. This paper analyzes joint muscle torque and power. Through the kinematic analysis of the run-up process and the dynamic analysis of the take-off process, this paper determines the functions and characteristics of the knee joint and the hip joint in the run-up and take-off process. It can diagnose the movement situation and have a better grasp of the dynamic changes of each joint. The purpose of this paper is to investigate the biomechanical properties of the volleyball spike during the approach and take-off process to better understand the movement characteristics and muscle activity of the technique.

2. Related Work

In recent years, researchers from various countries have discussed the application and development prospects of deep learning methods in various fields. It has done relevant research in all aspects and applications of deep learning methods. Chen et al. combined the idea of deep learning with the classification of hyperspectral data. They also proposed a new deep learning framework to fuse two classification methods (spectral information-based classification and spatial-dominant information-based classification) for maximum recognition precision. They proposed a method based on principal component analysis, deep learning, and logistic multivariate regression model. It is demonstrated that the classifier constructed on this basis has better performance [ 1 ]. Kermany et al. have developed a deep learning-based medical diagnostic to screen patients with retinopathy. The framework uses artificial neural network technology based on transfer learning, which has been widely used in optical coherence tomography images. Experiments have confirmed that this method can accelerate the diagnosis of retinopathy, and it can promote its early treatment and improve its clinical efficacy [ 2 ]. Lee discussed the development and implementation of blockchain technology in cyber-physical production systems. He proposed a unified three-tier blockchain architecture as a guideline for the industry. It clearly identifies the potential of blockchain. It aimed to integrate and synchronize the world of machines and manufacturing facilities into the networked computing space to move towards Industry 4.0 [ 3 ]. Oshea and Hoydis presented and discussed several new implementations of deep learning at the physical level. In the process of autocoding communication systems, they developed a new approach that views the design of a communication system as an end-to-end reconfiguration, with a single process that optimizes both transmitting and receiving elements. Based on this, they applied it to networks with multiple senders and receivers and introduced it into machine learning models [ 4 ]. Ravi et al. presented a comprehensive overview of relevant research on the use of deep learning in health informatics. They provided an insight into the relevant advantages, potential pitfalls, and prospects. They focused on the application of deep learning in bioinformatics, medical imaging, universal sensing, medical informatics, and public health [ 5 ]. Schirrmeister et al. have developed convolutional neural network-based electroencephalopathological decoding technology and visualized deep learning techniques. They performed an analysis of pathological and normal EEGs from the abnormal EEG corpus at Temple University School of Medicine. On the basis of CNN, they used two CNN architectures to decode tasks in electroencephalopathology. Results showed that convolutional neural networks were 6% more accurate than the published results for this dataset when decoding EEG pathology [ 6 ].

3. Artificial  Neural  Networks  and Convolutional Neural Networks

3.1. artificial neural network.

Artificial neural network is formed based on the neural network in the human brain. After a lot of anatomical research, scientists gradually understand the composition principle of neural networks. It has two advantages: first, each branch is parallel and does not interfere with each other. Second, the neural network has good learning and generalization ability. During training, reasonable outputs are obtained even without learning. These two advantages of the neural network enable the neural network to simulate the relationship between complex functions, and it can use the learning method to find the approximate solution of the function [ 7 ]. In the brain, nerve cells transmit information through chemical information, and computers can use binaries to simulate chemical information in cells. Usually, to facilitate simulation, neurons in the brain are usually divided into two states: excitation and inhibition, 1 for excitation and 0 for inhibition [ 8 ]. The excitatory and inhibitory signals of a neuron are influenced by the dendrites of that neuron, which can receive stimulation signals from multiple neurons simultaneously. And through a series of complex operations, it finally forms a specific excited or inhibited state.

Machine learning is a sure-fire way to artificial intelligence, and it encompasses knowledge from many disciplines. Fundamentally speaking, machine learning is to extract the statistical rules of the learned objects from the massive training samples. It enables human beings to “cognize” and correctly predict new things [ 9 ]. Before the advent of deep learning, many traditional machine learning methods have been well applied. However, for some complex problems or problems with nonlinear properties, it is often difficult to classify them with traditional machine learning methods. The extracted features are summed up by a large amount of research experience, and the processing of the same problem is also very complex and unstable, and these are determined by the large amount of experience of scientific researchers. However, deep learning does not require human intervention; just throw a large amount of data to it, and it can learn autonomously. The principle of artificial neural network is to simulate the activity of neurons in the human brain, and it is expected to establish a model that can learn autonomously. Since its emergence, neural networks have good potential for nonlinear regression and classification decisions. However, it requires a large number of samples, and the amount of computation involved is also very large, and it is prone to local optima during the training process, which makes the generalization ability of the neural network poor [ 10 ]. However, with the rapid development of information technology and the rapid development of computer hardware, the computing power of computers has been greatly developed. And the training algorithm has also been continuously updated, and people's understanding of deep neural networks is constantly developing and improving.

Figure 1 shows the perception mechanism of a single neuron. It, like neurons in the human brain, has two different output modes. The perceptron is the most basic model in the feedforward network. After receiving the input signal, it is weighted with the corresponding weights, and then the final output is obtained by the activation function.

Schematic diagram of the neuron perceptron model.

In the perceptron model diagram, x 1 , x 2 ,…, x n are the input data, and w 1 , w 2 ,…, w n are the corresponding weights. The specific calculation formula is as follows:

In this model, α ( ) is the activation function, b is the bias, and the learning process of the neuron is to adjust the weight parameter W , so that the output of the training sample is the closest to the actual value. However, the perceptron model is only suitable for decomposable linear problems, while the artificial neural network is a multilayer perceptual system composed of one or more neurons, and the output of the neurons in the previous layer is provided to the neurons in the next layer. An artificial neural network is a multilayered perceptron, each of which contains one or more neurons, and the output of the neurons in the previous layer is the input of the neurons in the next layer. When the number of neurons is sufficient, it can approximate any complex continuous functions and logical expressions, so that the artificial neural network has better representation ability [ 11 ].

The artificial intelligence network model is consistent with a three-stream system consisting of an input layer, a hidden layer, and a producer layer. The input layer is the gateway to the entire network object. Sample data enters the network template through this layer and it is exported to the hidden layer. The hidden layer contains one or more layers of neurons. The more the layers, the stronger the representation ability of the neural network, but the corresponding training difficulty will also increase. Figure 2 is an artificial neural network model with only one hidden layer. The output layer is the final output of the neural network, which is generally the classification result.

Schematic diagram of a single hidden layer neural network.

A neural network is a way to model the relationship between input data and target data. Through shallow neural network to explore the correlation between input data and actual output, the neural network can only obtain a simple functional relationship with the target data. However, more complex relationships cannot be obtained [ 12 ]. Moreover, the more the hidden layers in the neural network are, the more abstract the feature information will be. It enables the neural network to better simulate more complex functional relationships from the initial linear relationship to the subsequent nonlinear functions.

3.2. Backpropagation Algorithm

Backpropagation algorithm is a learning algorithm in the field of artificial intelligence, which is a typical neural network learning algorithm. The basic idea of this method is as follows: in the process of neural network training, the output error of each level will be backpropagated to the neural network. The neural network will correct the weight and bias of the network according to the feedback error to reduce the deviation from the target data and make it closer to the target data. In the training process of the neural network, it adopts the gradient descent algorithm. Its function is to correct weights and bias by learning the training set [ 13 ].

The formula principle of the backpropagation algorithm is as follows.

w jk l represents the connection weight between the j th neuron in the l th layer and the k th neuron in the previous layer. b j l represents the bias of the j th neuron in the lth layer; z j l represents the input of the j th neuron in the l th layer. The output value can be expressed as

where δ represents the activation function.

The cost function is used to measure the gap between the predicted value and the actual value. The purpose of the training is to reduce the output of the cost function step by step, so that the output of the network is closer and closer to the actual value. Usually, a quadratic cost function or its variant is used as the cost function:

Among them, x represents the input sample, y represents the actual classification, a L is the network output, and L is the number of network layers.

The formula for calculating the error generated by a neuron is

The output of the cost function is

The output error of the network is

Among them, ∙ is the multiplication between matrices.

The derivation process of formula ( 6 ) is as follows:

Calculating the errors in each layer of the network at once:

The derivation process of formula ( 8 ) is as follows:

Calculating the weight gradient:

Calculating the bias gradient:

Therefore, the steps of the backpropagation algorithm are as follows:

• (a) Input training set.
• (b) For each sample X in the training set, set the activation value corresponding to the input layer to a l .
• Forward propagation: z l = w l a l − 1 + b l , a l = δ z l . (12)
• (c) Calculating error generated by the output layer: δ L = ∇ a C • δ ′ z L . (13)
• (d) Calculating the backpropagation error: δ l = w l + 1 T δ l + 1 • δ ′ z l . (14)
• (e) Gradient descent training parameters: w l ⟶ w l − η m ∑ x δ x , l a x , l − 1 T , b l ⟶ b l − η m ∑ x δ x , l . (15)

3.3. Convolutional Neural Networks

The cumulative neural network is a new neural network based on the synthetic neural network. By doing integration work on it, it has much better compatibility than the virtual network. Compared with artificial intelligence networks, the advantages of networked networks are as follows.

3.3.1. Local Connection

Modern biology believes that the picture seen by the human eye is from the part of the whole. In an image, the closer the pixels are spaced, the tighter they are connected and vice versa. Therefore, each node only needs to be connected to a local area in the image and does not need to be fully connected to the features of the previous level. In convolutional neural networks, local connections reduce the number of parameters needed in the learning process. In a convolutional neural network, the local connection is to combine the neurons of this layer with the neurons of the upper layer, thereby reducing the parameters required in the learning process. Due to the large number of parameters in the network, the training of the network becomes more difficult. Therefore, the method of local connection is an important factor in the evolution of artificial neural networks from shallow to deep neural networks [ 14 ].

3.3.2. Weight Sharing

Compared with the full connection, the partial connection can greatly reduce the number of parameters, but, in the case of a large number of features, even if the partial connection is used, it will cause a large amount of parameters [ 15 ]. Convolution is a method to extract features from an image; however, feature extraction should be position independent. That is, using the convolution check image to perform convolution (feature extraction), the same feature should be extracted, which is weight sharing.

3.3.3. Pooling

By extracting the features of the convolutional layer, the feature map of part of the original image can be obtained, but, due to its large size, it cannot be directly classified. Since there is a large correlation between adjacent pixels in the image, the bottom-shaped parts and adjacent pixels are replaced with the same resolution without much loss of detail. It created a feature map using the method; by this way, a new feature map was obtained. This process is called “aggregation” [ 16 ]. Pooling can also improve the build performance and strength of the network, and it can prevent the network from running efficiently.

3.3.4. Multiple Convolutional Layers

As the number of network layers increases, the obtained image features will become more reasonable, so using multiple convolutional layers for image feature extraction can achieve better fitting effects [ 17 ].

The lexical network can effectively reduce the amount of space required for network training and reduce the amount of data required for network training, thus improving the network training efficiency. In the convolutional neural network, the essence of weight sharing is to use a specific area in the same convolutional check image to perform convolution operations, so that the same feature information can be extracted from different positions. Therefore, even after the image has been translated, the convolutional neural network can still recognize the image. It does not affect the feature extraction of convolutional neural networks due to image movement [ 18 , 19 ].

As shown in Figure 3 , the hard core is a multilayer network, which is consisted by a layer input, a convolutional layer, a downsampled layer (i.e., layer aggregation), a fully connected layer, and a layer output. In a complex network, signals are transmitted sequentially by many neuronal nodes. It then uses continuous convolution pooling techniques to decode, deduce, and pool to map the signal to the feature space of the hidden layer and classify the output [ 20 ].

Convolutional neural network structure diagram.

(1) Convolutional Layer . Convolutional layers essentially use filtering operations to complete local feature responses. The convolution kernel is a middle filter, which uses the same convolution kernel to scan the entire image and extract all features, to achieve the purpose of weight sharing. Usually, each convolution layer has several convolution kernels, the image features extracted from these convolution kernels are called feature maps, and the calculation formula is as follows:

Among them, y j l is the bias value, N j −1 l is the feature quantity of each feature map, M is the feature quantity of each convolutional layer, and α ( ) is the activation function. Each feature map can only represent one type of feature, and the later convolution layer actually obtains more expressive features by continuously extracting the underlying feature map. Therefore, when increasingly abstract features are obtained, the number of feature maps in the network layer will increase accordingly.

(2) Pooling Layer . The feature information after the convolution layer convolution is still very large, which will not only bring about the decline of the computing performance, but also cause the phenomenon of overfitting. Therefore, while reducing the feature dimension, it can extract representative feature information and make the processed feature map have a larger receptive field. This operation of replacing the whole feature with partial features is called pooling operation. The pooled image still has translation invariance, and the pooling operation will blur the specific position of the feature. After the image is translated, the same feature can still be generated. The pooling operation can further abstract the features of a local area, an element in the pooling corresponds to a region in the input data, and the pooling effect can reduce the number of parameters and reduce the image dimension [ 21 ].

Common loading methods include maximum load and average load. The maximum concentration is to choose the maximum value in each subregion as the final result. And choose the mean of all the values as the final result. Figure 4 shows a schematic diagram of two pooling effects with a pooling step size of 2.

Schematic diagram of two pooling effects.

(3) Fully Connected Layer . In a traditional multilayer perceptron, a fully connected layer is a hidden layer similar to a multilayer perceptron. The convolutional neural network is a neural network in which each layer of neurons is connected to the neurons of the next layer. In the convolutional neural network, the specific calculation formula of the fully connected layer is as follows:

where h W, b ( x ) represents the output of the fully connected layer; x i represents the output of the previous layer of neurons, that is, the input of the fully connected layer; W i represents the weight of the connection between neurons; b represents the bias.

Allowing complex neural networks to compare more complex business relationships, implementations are often integrated behind a complex layer or a fully connected layer. It allows difficult networks to learn stronger working relationships. In addition, adding deployment functionality to a complex network can add unnecessary components to the network. It allows complex neural networks to perform any unusual function, which increases the ability of neural networks of solving behavioral problems. Popular deployment services include ReLU deployment service, Sigmoid deployment service, and Tanh deployment service [ 22 ]. The formulas are as follows:

The image of the sigmoid deployment function is a sigmoid regression, so the sigmoid deployment function is also known as the sigmoid function. Because the sigmoid activation function is continuous and monotonic between (0, 1) and the output range of the function is limited, the sigmoid activation function is often used in binary classification problems. The image of the sigmoid deployment function is shown in Figure 5 .

Sigmoid function image.

The image of the Tanh activation function is a hyperbola, so the Tanh activation function is also called the bitangent function. The advantage of the Tanh activation function is that it works well in scenes with significantly different feature information, and it expands the feature effect during training.

The ReLU activation function is a nonsaturating activation function, also known as a rectified linear unit. Compared with the most commonly used deployment services, the ReLU deployment function has strong offline ReLU deployment capabilities in the network. For faulty performance in the network, the ReLU implementation function can keep the model's integration rate at a relatively constant level. Therefore, the ReLU deployment function is also a frequently used deployment service in virtual networks. Figure 6 is a function image of ReLU.

ReLu function image.

4. Experiment of Volleyball Approach and Jump

The research objects of this paper are 4 male volleyball players from the Physical Education College of X University, all of whom have reached the national second level and above. The subjects did not perform high-intensity training or competition one week before the experiment, and the subjects were in good physical condition on the test day. The basic information of the experimental subjects is shown in Table 1 .

Basic body shape indexes of subjects.

This paper takes the spiking action of volleyball as the research content and compares and analyzes two typical actions represented by front row No. 4 and back row No. 6. This paper focuses on the analysis of the kinematics and dynamics parameters of the two stages of the spiking run-up process and the take-off process.

4.1. Kinematics of Approach Process

The run-up process refers to the process from the start of the first step to the moment when both feet touch the ground. To approach the ball and choose the jumping point, the human body should be given a suitable and faster movement speed according to the goals of different projects before jumping. It also prepares the body for the best take-off position. One of the purposes of the run-up is to prepare for take-off and hitting the ball, and the other is to obtain a greater horizontal speed. According to the theory of sports anatomy, when the athlete's take-off leg support strength reaches a certain level, the faster the run-up speed, the greater the impact on the take-off leg strength. The muscle load of the take-off leg will also increase accordingly, and the extensor muscles of the take-off leg will also adjust as many muscle fibers as possible, so that the muscle contraction force is greatly improved. Therefore, the maximum approach speed is very critical to the height of the jump. Table 2 shows the stride length, approach distance, and maximum speed of the test subjects in the run-up stage.

Running width and speed data table.

The experimental subjects all adopted a three-step approach. During the approach, the approach width gradually increased, and the last step reached the maximum. The average approach distance was 3.30 m, and the maximum approach speed during the approach was 4.35 m/s. And the research subjects' run-up showed that the first step was small, the second step was large, the stride gradually increased, and the second step had the largest stride, accounting for 52% of the approach distance. Athlete No. 1 has the largest stride in the second step, 1.89 m, the first step is 0.93 m, the second step is 0.83 m, and his approach speed is 4.54 m/s. The second and first steps of No. 2 athlete were both 1.56 m and 1.03 m smaller than those of No. 1 athlete. However, its parallel stride is greater than that of No. 1 athlete, which is 0.91 m, and its approach speed is 4.67 m/s. It can be concluded that the stride length also affects the approach speed. In addition, the parallel distances of the study subjects varied widely. And there is a significant positive correlation between parallel stride and approach speed. Technically speaking, the athlete must first observe the angle and arc of the ball to determine the landing point and then determine the direction and time of the take-off. Therefore, in the run-up phase, the first step should not be too large. The second step has a large stride. One is to lower the center of gravity and convert the inertial force after braking into an upward speed. The second is to ensure the position of the jump and the accurate landing point after the start. As the approach speed increases, so does the parallel distance. If the step distance is too short, the lateral speed buffer cannot be fully completed, so that the forward thrust after the jump is large and it is easy to fall.

4.2. Dynamic of Take-Off Process

Taking off is a prerequisite for offensive serving and spiking. Its purpose is to gain height and to choose the right timing. The correct take-off method includes both height and distance. Height: jumping higher at a given distance not only increases the angle of the ball over the net, but also gives enough time to ensure a rotating swing. Distance: the longer the jump is, the more power the body gets. It can not only prolong the hitting distance at the moment of hitting the ball, but also increase the impact on the ball, making the hitting speed faster and the attacking power stronger. At the same time, the increase of the jumping distance reduces the interval between the hitting point and the landing point. At the same speed, it reduces the time in which the ball is in the air, making it more difficult for the opponent to catch the ball. The increased jumping distance allows players to quickly return to their position on the field, either defensively or offensively.

Table 3 shows the lift-off angle of the center of gravity and the loss rate of horizontal velocity during the take-off stage.

Lifting angle of center of gravity and loss rate of horizontal speed in take-off stage.

Through the experimental research on the center of gravity speed and the take-off time of each athlete during the take-off process, this paper analyzes the dynamics of the athletes during the take-off process. It includes the following aspects: horizontal speed, vertical speed, landing time, and time off the ground. The research results are shown in Figures ​ Figures7 7 and ​ and8, 8 , respectively.

Speed comparison chart of take-off stage. Figure 7 (a) shows the horizontal speed of each athlete during the take-off process. Figure 7 (b) shows the vertical speed of each athlete during the take-off process.

Time comparison chart of take-off stage. Figure 8 (a) shows the landing time of each athlete during the take-off process. Figure 8 (b) shows the time off the ground for each athlete during the take-off process.

As can be seen from Figure 7 , the horizontal speed of No. 4 athlete is small (2.44 m/s), but his vertical speed is high (2.93 m/s), which is an upward vertical jump. Athletes No. 1 and No. 2 had higher horizontal speeds when they took off 3.3 m/s and 3.62 m/s, respectively, while their vertical speeds were relatively small (2.65 m/s, 2.71 m/s), showing an obvious forward rush. Among them, athlete No. 2 obtained a larger vertical speed when he took off, which indicated that athlete No. 2 had a reasonable take-off action. It is not only conducive to maintain a horizontal forward thrust, but also conducive to the vertical rise of the center of gravity. After taking off, the center of gravity can obtain the appropriate height and distance. In the case of a certain take-off speed, the higher the horizontal speed is, the more favorable it is to rush forward and obtain a suitable distance. The higher the vertical speed, the better the outcome for jumping and getting a suitable height.

Landing time refers to the difference in the time between the landing of the right foot and the landing of the left foot during the run-up process. It is an important indicator to measure the time of the single support of the right foot. The time off the ground refers to the time difference between the left foot and the moment when the left foot leaves the ground. It is used to measure the time of the left foot single support. It can be seen from Figure 8 that athlete No. 4 has a short time off the ground (0.12 s) and the single support time of the left foot is short, showing the movement state of the left and right feet leaving the ground almost at the same time. Athletes 1 and 2 were off the ground longer than athletes 3 and 4. That is, athletes Nos. 1 and 2 support the left and right feet longer than athletes Nos. 3 and 4. This shows that the landing and lifting of the left and right legs are in order during the spiking take-off. Athlete No. 2 has the largest difference in landing time, with both feet landing on the ground in turn and the right foot ahead of the left, which meets the mechanical requirements of jumping. Judging from the take-off and landing time, athlete No. 4 took 0.2 s and took the shortest time, and athlete No. 2 took 0.345 s as the longest jump. The length of time is closely related to the size of the study object and the size of the step. The shorter the distance between the feet is, the shorter the take-off time is, and the faster the take-off action is completed. To a certain extent, the take-off time can reflect the range of buffering and kicking during the take-off stage.

Through the data collection of 4 athletes, the angle changes of the knee and hip joints were studied, and the average test data of these four athletes were summarized and tabulated, as shown in Table 4 .

Knee and hip joint angles at different times.

Since the joint muscle moments of the knee and hip joints of the 4 players are similar to the power curves when the volleyball spike takes off, only player 2 is used for analysis, as shown in Figures ​ Figures9 9 and ​ and10 10 .

Muscle moments of the knee and hip joints. Figure 9 (a) shows the knee joint muscle torque during the take-off of No. 2 athlete. Figure 9 (b) shows the hip joint muscle moment during the take-off of No. 2 athlete.

Muscle power of the knee and hip joints. Figure 10 (a) shows the knee joint muscle power during the take-off of No. 2 athlete. Figure 10 (b) shows the power of the hip joint muscles of No. 2 athlete during the take-off process.

It can be seen from Figure 9 that athlete No. 2 reaches the maximum knee joint muscle torque (0.38 N∙m) at 0.45 s and the hip joint muscle torque reaches the maximum value (0.22 N∙m) at 0.25 s. From 0 to 0.25 s, the knee joint muscle torque increased significantly, while the hip joint muscle torque increased gently. At 0.25∼0.35 s, the knee joint muscle torque continued to increase, while the hip joint muscle torque began to decrease. At 0.35∼0.45 s, the knee joint muscle torque basically remained between 0.36 and 0.38, and the hip joint muscle torque slowly decreased. From 0.45 to 0.60 s, the knee joint muscle torque decreased from 0.38 N∙m to 0.03 N∙m, and the hip joint muscle torque decreased from 0.14 N∙m to −0.03 N∙m.

It can be seen from Figure 10 that the knee joint muscle power of No. 2 athlete reached the maximum value (2.28 W) at 0.45 s. From 0 to 0.25 s, the knee muscle power changed from a positive value to a negative value, from 0.05 W centripetal to 2.12 W eccentric. At 0.25∼0.35 s, the eccentric power of knee joint muscles gradually decreased, and, at 0.35 s, the eccentric power decreased to 0. From 0.35 to 0.45 s, the concentric power of the knee muscles gradually increased, and the concentric power reached the maximum value at 0.45 s. From 0.45 to 0.60 s, the centripetal power of the knee joint muscle decreased continuously to 0.01 W. Athlete No. 2 showed less significant changes in hip muscle power compared to knee muscle power. From 0 to 0.25 s, the eccentric power of the hip muscles basically was maintained between 0.1 and 0.2 W, and, from 0.25 to 0.60 s, the centripetal power of the hip muscles first slowly increased to 0.5 W and then decreased to 0.

5. Discussion

• The whole approach run process showed that the first step was small, the second step was large, and the stride gradually increased. The second step has the largest amplitude, accounting for 52% of the total run-up distance, and the maximum speed of the run-up is also generated in this step. In this way, the runner can gradually increase the speed of the center of gravity to facilitate the transition between the two steps.
• The moment and power of the knee joint have great changes during the take-off process. Small changes in the hip joint are not conducive to the full use of the hip extensor group, which may lead to poor take-off height. It is recommended to attach importance to the training of hip extension in the approach and take-off training and appropriately increase the range of motion of the hip joint and the contribution rate during the take-off process.

6. Conclusion

The experimental part of this paper mainly studies the change trend of the running width, distance and speed, knee joint and hip joint muscle torque, and power of male volleyball players in the run-up and take-off link, but there are also some limitations. First, the sample size is too small, which cannot reflect the general laws and development trends of volleyball players in the run-up and take-off link. Therefore, this article is only a small case study. It is recommended to do further generalization and test in future research to get the final conclusion. Second, although this paper focuses on the change trend of the knee and hip joints of volleyball players during the run-up and take-off, the experiment is slightly one-sided in the absence of the change trend of the ankle joint angle. It is recommended that subsequent researchers try to add ankle joint indicators to qualitatively and comprehensively discover relevant laws and characteristics.

Data Availability

The authors confirm that the content of the manuscript has not been published or submitted for publication elsewhere.

Conflicts of Interest

There are no potential conflicts of interest in this paper.

Authors' Contributions

All authors have seen the manuscript and approved it for publication.