Functional Movement Analysis (FMS) in Female Volleyball Players: Positional Differences, Injury Risk, and Asymmetries

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A total of 107 professional female athletes from the provinces of Çanakkale and Istanbul participated in the study. Height and body weight measurements were taken to calculate body mass index (BMI), and the Functional Movement Screen (FMS) was administered. Data were analyzed using the SPSS statistical software package. The Wilcoxon Signed Ranks Test was employed for asymmetry analyses, while the Kruskal-Wallis test was used to evaluate differences across playing positions. The findings revealed no statistically significant differences in FMS test scores among players in different positions for deep squat (p = 0.228), hurdle step (p = 0.834), inline lunge (p = 0.064), shoulder mobility (p = 0.858), active straight-leg raise (p = 0.298), trunk stability push-up (p = 0.110), and rotary stability (p = 0.330). Regarding injury risk, no significant associations were observed between FMS scores and injury risk for deep squat (p = 0.209), inline lunge (p = 0.134), active straight-leg raise (p = 0.448), and rotary stability (p = 0.327); however, statistically significant associations were found for hurdle step (p = 0.003), shoulder mobility (p = 0.002), and trunk stability push-up (p = 0.014). In terms of right-left asymmetries, no significant differences were identified in hurdle step (p = 0.249), inline lunge (p = 0.083), active straight-leg raise (p = 0.634), and rotary stability (p = 0.191), while shoulder mobility showed a statistically significant asymmetry between the right and left sides. In conclusion, FMS performance was found to be similar across playing positions among female volleyball players. Additionally, FMS testing appears to be a valuable tool for assessing injury risk and detecting side-to-side asymmetries in this population. Health sciences/Anatomy Health sciences/Health care Health sciences/Health occupations Health sciences/Medical research Health sciences/Risk factors volleyball female athletes functional movement screen asymmetry injury risk INTRODUCTION In today’s world, advancements in technology have led to a noticeable decline in physical activity and motor skills. This trend has also affected the consistency of training among both professional and amateur athletes. As a result, athletes frequently face the risk of injury during training or competitive performance. When such injuries occur, they must undergo treatment and participate in return-to-sport (RTS) rehabilitation to regain their performance levels. The literature contains a considerable number of studies addressing the RTS process following sports injuries ( 1 , 2 , 3 , 4 ). These studies indicate that the primary goal of an injured athlete is to return to sport as quickly as possible ( 1 ). Researchers emphasize the sense of urgency experienced by athletes and define the RTS decision as primarily based on the type and severity of the injury, recovery of symptoms and function, and the athlete’s ability to tolerate the demands of the sport ( 5 , 6 ). While injury and RTS studies hold ongoing significance, recent years have seen increasing interest in predicting injury risk before injuries occur. Studies across various sports disciplines aim to identify such risk factors. This issue has also been addressed in football-specific research ( 7 ). However, a review of the literature shows that there are relatively few studies focused on Functional Movement Screening (FMS) ( 8 , 9 ). For this reason, the current study aims to investigate positional differences, injury risk, and asymmetries in functional movement patterns among female volleyball players. The positive effects of regular physical activity on health are well established ( 10 ). However, in performance sports such as volleyball, athletes are required to maintain high levels of physical conditioning ( 11 ). Assessment tools such as FMS have the potential to identify asymmetries and weaknesses in fundamental movement patterns, offering insight into injury risk and ways to mitigate it. The use of such screening methods is crucial for athletes to achieve optimal performance across different playing positions. METHODS Study Design This descriptive cross-sectional study aimed to investigate positional differences, injury risk, and asymmetries using the Functional Movement Screen (FMS) in professional female volleyball players. As the study did not involve any clinical intervention, registration in an international clinical trials database was not required. The research was conducted in accordance with the principles of the Declaration of Helsinki, and written informed consent was obtained from each participant. Participants The target population comprised professional female volleyball players, with the sample selected from athletes actively competing in Çanakkale and Istanbul, Türkiye. A purposive sampling method was employed for participant selection. Participants were selected based on specific inclusion and exclusion criteria to ensure consistency and reliability in the assessment. The inclusion criteria were as follows: actively competing female volleyball players during the 2023–2024 season in the Turkish Women's 1st and 2nd Leagues; a minimum of three years of licensed volleyball experience; regular participation in training sessions (at least three times per week); no acute injuries at the time of data collection; and voluntary participation with signed informed consent. Exclusion criteria included having sustained a serious musculoskeletal injury (e.g., ligament rupture or meniscus surgery) within the past six months; presence of chronic orthopedic conditions (e.g., scoliosis, herniated disc); experiencing pain or discomfort preventing completion of the assessment; pregnancy; or refusal to provide informed consent or participate in the test procedures. Data Collection Procedures A total of 107 professional female volleyball players from clubs in Çanakkale (Çanakkale Belediyespor, Çan Gençlik Kale Spor, and Yeşil Bayramiç Spor) and Istanbul (Vakıfbank Sports Club) participated in the study. Anthropometric measurements—including height, body weight, and body mass index (BMI)—were recorded, and the FMS test was administered. Height Measurement Height was measured with participants standing barefoot using a SECA stadiometer (Germany) with a precision of 0.1 cm. Body Weight Measurement Body weight was measured using a SECA electronic scale (Germany) with an accuracy of 0.05 kg. BMI Calculation Body mass index was calculated using the following formula: BMI = Body Weight (kg) / Height² (m²) [12]. Functional Movement Screening (FMS) FMS assessments included seven movement tasks: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight-leg raise, trunk stability push-up, and rotary stability. Each movement was scored as follows: 3 points for flawless, pain-free performance; 2 points for compensated but pain-free performance; 1 point for flawed performance; and 0 points for inability to perform or presence of pain. The highest possible composite score was 21, indicating perfect performance across all movements [13, 14]. Data Analysis Statistical analyses were performed using the SPSS software package. As the sample size exceeded 30, the Kolmogorov–Smirnov test was used to evaluate the normality of the data. Since the data were not normally distributed, non-parametric tests were applied. The Wilcoxon Signed Ranks Test was used to assess asymmetries within participants, and the Kruskal–Wallis test was conducted to compare groups based on playing position. In cases where the Kruskal–Wallis test revealed significant differences, pairwise comparisons were made using the Mann–Whitney U test. The internal consistency of the FMS was evaluated using Cronbach’s alpha coefficient, indicating a reliable measurement tool. Table 1 Cronbach’s Alpha Reliability Coefficient of the Study Cronbach's Alpha N .728 20 According to Table 1 , the Cronbach’s Alpha reliability coefficient for the study was calculated as .728 based on a sample of 20 participants. This result indicates an acceptable level of internal consistency for the instrument used in the study, suggesting that the scale items are sufficiently correlated and measure a coherent construct. RESULTS Table 2 Demographic Characteristics of the Participating Athletes Variables Group Mean Standard Deviation Min. Max. Age (years) Setter 19.80 5.18 15.00 31.00 Opposite Hitter 19.10 3.12 15.00 25.00 Middle Blocker 20.14 5.09 15.00 30.00 Outside Hitter 18.84 3.26 14.00 31.00 Libero 20.15 3.19 16.00 25.00 Height (cm) Setter 171.10 5.79 157.00 183.00 Opposite Hitter 173.50 8.53 162.00 188.00 Middle Blocker 182.71 7.96 171.00 196.00 Outside Hitter 175.44 7.14 160.00 188.00 Libero 166.00 6.62 157.00 176.00 Body Weight (kg) Setter 57.60 7.10 51.00 71.70 Opposite Hitter 62.82 8.53 52.00 77.70 Middle Blocker 63.16 10.83 52.00 85.00 Outside Hitter 61.38 9.26 45.00 78.00 Libero 56.66 3.93 48.00 66.00 BMI Setter 19.64 1.80 17.86 24.22 Opposite Hitter 20.79 1.44 19.03 24.16 Middle Blocker 18.75 1.79 16.41 22.60 Outside Hitter 19.90 2.49 16.14 25.56 Libero 20.57 1.17 19.36 23.53 According to Table 2 , when examining the average ages of the players by position, setters (19.80 years) and opposite hitters (19.10 years) exhibited similar age means, while outside hitters (18.84 years) were relatively younger, and middle blockers (20.14 years) and liberos (20.15 years) were slightly older. In terms of height, middle blockers had the highest average (182.71 cm), followed by outside hitters (175.44 cm), opposite hitters (173.50 cm), setters (171.10 cm), and liberos (166.00 cm). Regarding body weight, middle blockers were the heaviest group (63.16 kg), whereas liberos were the lightest (56.66 kg). Setters (57.60 kg), opposite hitters (62.83 kg), and outside hitters (61.38 kg) fell in between. According to Body Mass Index (BMI) values, middle blockers had the lowest average (18.75), while opposite hitters had the highest (20.79). The averages for setters, outside hitters, and liberos were 19.65, 19.90, and 20.58, respectively. These findings indicate that the physical profiles of volleyball players are closely aligned with the specific physical demands of their playing positions. Table 3 Comparison of FMS Scores by Playing Positions in Volleyball Players Variables Group N Kruskal Wallis Df P Deep Squat Setter 20 5.635 4 .228 Opposite Hitter 20 Middle Blocker 21 Outside Hitter 26 Libero 20 Hurdle Step Setter 20 1.457 4 .834 Opposite Hitter 20 Middle Blocker 21 Outside Hitter 26 Libero 20 Inline Lunge Setter 20 8.885 4 .064 Opposite Hitter 20 Middle Blocker 21 Outside Hitter 26 Libero 20 Shoulder Mobility Setter 20 1.322 4 .858 Opposite Hitter 20 Middle Blocker 21 Outside Hitter 26 Libero 20 Active Straight-Leg Raise Setter 20 4.897 4 .298 Opposite Hitter 20 Middle Blocker 21 Outside Hitter 26 Libero 20 Trunk Stability Push-Up Setter 20 7.550 4 .110 Opposite Hitter 20 Middle Blocker 21 Outside Hitter 26 Libero 20 Rotary Stability Setter 20 4.606 4 .330 Opposite Hitter 20 Middle Blocker 21 Outside Hitter 26 Libero 20 According to Table 3 , the study investigated whether Functional Movement Screening (FMS) test scores differed by playing position among volleyball players. The Kruskal-Wallis test was used for the analysis. In the Deep Squat test, the result was χ² = 5.635 (df = 4, p = 0.228), indicating no statistically significant difference in performance among positions. Similarly, the Hurdle Step test yielded a result of χ² = 1.457 (df = 4, p = 0.834), suggesting no significant difference between positions. For the In-Line Lunge test, the Kruskal-Wallis result was χ² = 8.885 (df = 4, p = 0.064), showing no statistically significant difference. The Shoulder Mobility test produced a result of χ² = 1.322 (df = 4, p = 0.858), also indicating no significant variation across positions. In the Active Straight-Leg Raise test, the result was χ² = 4.897 (df = 4, p = 0.298), again showing no meaningful difference by position. The Trunk Stability Push-Up test had a result of χ² = 7.550 (df = 4, p = 0.110), while the Rotary Stability test yielded χ² = 4.606 (df = 4, p = 0.330), both of which revealed no significant positional differences. Overall, none of the FMS subtests demonstrated statistically significant differences across playing positions. Table 4 Distribution of FMS Scores According to Injury Risks in Volleyball Players Variables Group N Rank average Kruskal Wallis Df P Difference Deep Squat .00 4 49.50 4.538 3 .209 - 1.00 20 52.18 2.00 37 58.18 3.00 46 51.83 Hurdle Step .00 1 103.00 13.617 3 .003 0–1 < 2–3 1.00 50 52.71 2.00 47 52.91 3.00 9 61.39 Inline Lunge 1.00 4 62.88 4.026 2 .134 - 2.00 86 52.61 3.00 17 58.94 Shoulder Mobility .00 2 49.50 14.546 3 .002 0–1 < 2–3 1.00 2 76.25 2.00 95 52.32 3.00 8 69.56 Active Straight Leg Raise .00 8 56.19 2.652 3 .448 - 1.00 48 53.96 2.00 32 51.17 3.00 19 57.95 Trunk Stability Push-Up .00 8 72.13 10.643 3 .014 0–1 < 2–3 1.00 48 46.02 2.00 32 64.61 3.00 19 48.66 Rotary Stability .00 14 49.50 3.449 3 .327 - 1.00 61 53.89 2.00 26 57.73 3.00 6 49.50 Table 4 presents the evaluation of injury risks among volleyball players based on their Functional Movement Screen (FMS) scores. The FMS rates each movement on a scale from 0 to 3, where a score of 3 represents a flawless and pain-free performance, 2 indicates a compensated but pain-free movement, 1 is given when the movement is performed with noticeable faults, and 0 is assigned when the movement elicits pain. In the Deep Squat test, the Kruskal-Wallis result was 4.538 (df = 3, p = 0.209), with four athletes scoring 0 and an average rank of 49.50. These findings indicate that Deep Squat performance was not significantly associated with injury risk (p = 0.209). For the Hurdle Step test, one athlete scored 0 with an average rank of 103.00, and the Kruskal-Wallis value was 13.617 (df = 3, p = 0.003). Further analysis using the Mann-Whitney U test showed a statistically significant difference between athletes with low scores (0 and 1) and those with high scores (2 and 3) (p < 0.001), suggesting that lower scores are associated with reduced performance. In the Inline Lunge test, the Kruskal-Wallis statistic was 4.026 (df = 2, p = 0.134), indicating no significant relationship between FMS scores and injury risk (p = 0.134). For the Shoulder Mobility test, two athletes who scored 0 had an average rank of 49.50, and the Kruskal-Wallis value was 14.546 (df = 3, p = 0.002). The Mann-Whitney U test also showed a significant difference between low and high score groups (p = 0.006), demonstrating that athletes with lower scores had significantly reduced shoulder mobility. The Kruskal-Wallis statistic for the Active Straight-Leg Raise test was 2.652 (df = 3, p = 0.448), revealing no significant association between test scores and injury risk (p = 0.448). In the Trunk Stability Push-Up test, a Kruskal-Wallis value of 10.640 (df = 3, p = 0.014) indicated a statistically significant relationship between lower scores and increased injury risk. Pairwise comparisons using the Mann-Whitney U test confirmed a significant difference between low and high score groups (p < 0.001), suggesting that reduced trunk stability is associated with poorer performance and potentially higher injury risk. Regarding the Rotary Stability test, the Kruskal-Wallis result was 3.449 (df = 3, p = 0.327), indicating no statistically significant association between FMS scores and injury risk in this movement pattern (p = 0.327). Table 5 Asymmetry Scores in FMS Test among Volleyball Players Variables N Rank average Rank Total z p Hurdle Step Negative Rankings 22 26.18 576.00 -1.153 .249 Positive Rankings 30 26.73 802.00 Binds 55 Inline Lunge Negative Rankings 18 14.00 252.00 -1.732 .083 Positive Rankings 9 14.00 126.00 Binds 80 Shoulder Mobility Negative Rankings 27 15.61 421.50 -4.328 .000 Positive Rankings 3 14.50 43.50 Binds 77 Active Straight Leg Raise Negative Rankings 18 18.94 341.00 − .476 .634 Positive Rankings 17 17.00 289.00 Binds 72 Rotary Stability Negative Rankings 14 12.64 177.00 -1.308 .191 Positive Rankings 9 11.00 99.00 Binds 84 Table 5 presents the analysis of right–left asymmetries in volleyball players using the Wilcoxon Signed Ranks Test. The hypothesis was evaluated by examining performance differences between the right and left sides of the body across various functional movement tasks. In the Hurdle Step test, the mean rank of negative ranks was 26.18 (total = 576.00), and the mean rank of positive ranks was 26.73 (total = 802.00). The test result was not statistically significant (z = -1.153, p = 0.249), indicating no meaningful asymmetry between sides. For the Inline Lunge test, both negative and positive ranks had a mean rank of 14.00, with totals of 252.00 and 126.00, respectively. The test result (z = -1.732, p = 0.083) suggested a borderline, but still non-significant, asymmetry. In the Shoulder Mobility test, the mean rank for negative ranks was 15.61 (total = 421.50), and for positive ranks it was 14.50 (total = 43.50). The test revealed a statistically significant asymmetry (z = -4.328, p < 0.001), favoring the negative ranks, suggesting that scores on the left side were lower than those on the right. In the Active Straight-Leg Raise test, the mean rank of negative ranks was 18.94 (total = 341.00), while the positive ranks had a mean of 17.00 (total = 289.00). The result (z = -0.476, p = 0.634) indicated no significant difference in asymmetry. Finally, in the Rotary Stability test, the mean rank of negative ranks was 12.64 (total = 177.00), and for positive ranks it was 11.00 (total = 99.00). The analysis showed no significant asymmetry (z = -1.308, p = 0.191). DISCUSSION This study aimed to assess FMS scores, injury risk, and asymmetries among female volleyball players according to playing positions. The results showed no statistically significant differences in FMS test scores across different positions for deep squat (p = 0.228), hurdle step (p = 0.834), inline lunge (p = 0.064), shoulder mobility (p = 0.858), active straight-leg raise (p = 0.298), trunk stability push-up (p = 0.110), and rotary stability (p = 0.330). Regarding injury risk, no significant associations were found for deep squat (p = 0.209), inline lunge (p = 0.134), active straight-leg raise (p = 0.448), or rotary stability (p = 0.327). However, significant associations were observed between FMS scores and injury risk in the hurdle step (p = 0.003), shoulder mobility (p = 0.002), and trunk stability push-up (p = 0.014) tests. As for right–left asymmetries, no statistically significant differences were detected in the hurdle step (p = 0.249), inline lunge (p = 0.083), active straight-leg raise (p = 0.634), or rotary stability (p = 0.191) tests. A significant asymmetry was found only in the shoulder mobility test. The literature reveals a limited number of studies investigating the relationship between technical skills and FMS performance. However, some studies have addressed the association between technical ability and athletic performance ( 15 , 16 ) or the predictive value of FMS for injury risk ( 17 , 18 ). A systematic review examined whether variables such as athlete age, sex, sport type, injury definition, and injury mechanism contribute to inconsistent findings. It was suggested that FMS composite scores and asymmetries may be more predictive of injury risk in older athletes compared to younger ones. Additionally, in athletes from sports such as rugby, ice hockey, and American football, the effect sizes of FMS scores tended to be small ( 19 ). In one study, FMS scores were used to assess functional capacity and injury susceptibility. The findings indicated that athletes who scored below 17 on the composite FMS had approximately 4.7 times higher odds of experiencing lower extremity injuries during a regular season ( 20 ). Another systematic review evaluated the methodological quality and heterogeneity of studies examining the association between FMS composite scores and subsequent injury risk. It concluded that the predictive value of FMS was insufficient to support its use as a stand-alone injury prediction tool ( 9 ). Similarly, a separate review explored whether FMS scores were associated with future injuries in healthy athletes and found inconclusive results. About half of the included studies reported that lower FMS scores were statistically associated with an increased risk of sports injuries. The heterogeneity in study populations (e.g., athlete type, age, sport exposure) and inconsistency in injury definitions were cited as major barriers to synthesizing the evidence and drawing definitive conclusions ( 21 ). Researchers have investigated the predictive power of the FMS, with some findings contradicting previous studies by suggesting that the FMS may not be a useful tool for estimating the risk of musculoskeletal injuries ( 22 ). Another study aimed to determine whether the FMS is a valid predictor of injury among high school athletes and whether a new scoring system could be developed for this population. The results indicated that while the FMS may be helpful in identifying deficiencies in specific movements, it should not be used to predict general injury risk over the course of a season in high school athletes ( 23 ). Regarding studies on FMS and asymmetries, most have focused on football. In volleyball, limited research exists. One study examined changes in functional movement patterns over a season among university-level football and volleyball players. The study found no significant changes in overall FMS scores over time or between sports. However, some subtests showed notable differences: performance improved in the Deep Squat and Inline Lunge tests, while scores declined in the Active Straight-Leg Raise and Rotary Stability tests. Additionally, there was a decrease in asymmetry levels and in the number of low-scoring test components by the end of the season ( 24 ). In another study involving right-hand dominant female volleyball players in the Croatian Women’s Premier League, researchers investigated whether right–left asymmetries existed in range of motion and movement quality. The results revealed significant differences in the Shoulder Mobility and Active Straight-Leg Raise tests. It was suggested that the superior shoulder mobility in the dominant arm could be attributed to fewer spike and serve repetitions compared to elite-level players ( 25 ). Further research has shown that shoulder muscle strength asymmetries are commonly observed in volleyball players, largely due to the dominant arm’s frequent use. These asymmetries in the non-dominant arm may contribute to imbalances in strength development and are considered potential risk factors for shoulder injuries ( 26 ). In conclusion, FMS appears to be a valuable tool for assessing performance and estimating injury risk among volleyball players. FMS testing can be effectively used to enhance athletic performance and reduce injury risk. Developing individualized training programs based on FMS results may help minimize injuries and optimize athletic outcomes. Regular implementation of FMS assessments—particularly for female volleyball players—and their integration into training regimens is of critical importance. Consequently, systematic use of FMS and the development of personalized training strategies should be considered essential steps toward maximizing athletic performance and minimizing injury risk. Future studies with larger sample sizes, diverse age groups, sports disciplines, and levels of expertise—across both individual and team sports—are recommended. Declarations Ethics approval and consent to participate This study was approved by the Çanakkale Onsekiz Mart University Graduate Education Institute Scientific Research Ethics Committee (Approval Date: 01/12/2022; Approval Number: 21/33). All procedures were conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all individual participants prior to data collection. As this study does not involve any clinical intervention or healthcare-related treatment, registration in a clinical trial registry was not required. Consent for publication Not applicable. This study does not contain any individual person’s data in any form (including images, videos, or identifiable personal information). Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no financial support from any organization for the research, authorship, or publication of this article. Authors’ contributions GE contributed to the study design, data collection, data analysis, and writing of the manuscript. BB provided supervision throughout the research process, contributed to data interpretation, and critically revised the manuscript. All authors read and approved the final version of the manuscript. Acknowledgements This study is part of a master's thesis titled “Functional Movement Analysis (FMS) in Volleyball Players: Positional Differences, Injury Risk, and Asymmetries” completed in 2024 at Çanakkale Onsekiz Mart University under the supervision of Dr. Barış Baydemir. We would also like to express our sincere gratitude to Üzeyir Özdurak, Youth Development Coordinator of Vakıfbank, and his team; Vedat Mekik, Head Coach of Çanakkale Belediyespor, and his team; İbrahim Kırmış, Head Coach of Çan Gençlik Kale Sports Club, and his team; and Melda Bora, Head Coach of Yeşil Bayramiç Sports Club, and her team, for their valuable support throughout the study. Authors’ information Barış Baydemir – ORCID: https://orcid.org/0000-0002-8653-0664 Gözde Emir Uysal – ORCID: References Kvist, J., & Silbernagel, K. G. (2022). Fear of movement and reinjury in sports medicine: relevance for rehabilitation and return to sport. Physical therapy , 102 (2), pzab272. Slagers, A. J., Reininga, I. H., Geertzen, J. H., Zwerver, J., Van den Akker-Scheek, I. (2019). “Translation, cross-cultural adaptation, validity, reliability and stability of the Dutch Injury-Psychological Readiness to Return to Sport (I-PRRS-NL) scale. Journal of sports sciences , 37 (9), 1038-1045. 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Athletic Training & Sports Health Care , 6 (4), 161-169. Bardenett, S. M., Micca, J. J., DeNoyelles, J. T., Miller, S. D., Jenk, D. T., Brooks, G. S. (2015). “Functional movement screen normative values and validity in high school athletes: can the FMS™ be used as a predictor of injury?”. International journal of sports physical therapy , 10 (3), 303. Sprague, P. A., Mokha, G. M., & Gatens, D. R. (2014). Changes in functional movement screen scores over a season in collegiate soccer and volleyball athletes. The Journal of Strength & Conditioning Research , 28 (11), 3155-3163. Đurković, T., Ban, M., & Marelić, N. (2017, May). Asymmetry in functional movements in Croatian women’s Premier league volleyball players. In 8th International scientific conference on kinesiology, Proceedings Book, Opatija, Croatia (pp. 25-28). Zuzgina, O., & Wdowski, M. M. (2019). Asymmetry of dominant and non-dominant shoulders in university level men and women volleyball players. Human Movement , 20 (4), 19-27. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Sep, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers invited by journal 05 Aug, 2025 Editor assigned by journal 29 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 25 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7217401","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496241466,"identity":"722fa545-566d-4254-a1b9-3c852f8e2ac0","order_by":0,"name":"Gözde Emir Uysal","email":"","orcid":"","institution":"Çanakkale Onsekiz Mart University","correspondingAuthor":false,"prefix":"","firstName":"Gözde","middleName":"Emir","lastName":"Uysal","suffix":""},{"id":496241467,"identity":"93a77730-da25-4b53-8601-2e7fd5c9668c","order_by":1,"name":"Barış Baydemir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACAyA+wNgAIkGo4gBY9MAD4rWcOcDAA2InENDCANPCwNgG0cKAT4s5+9mHB3/usEvsO96deLhy3h05e7HDD4G22MnpNmDXYtmTbnCY90xy4swzZzccPLvtmTGPdJoBUEuysdkBHA47kMZwmLGNOXHDjdwNBxu3HU7skU4AaTmQuA2XlvPPGA7+bKtP3HD/LVDLHJCW9A/4tdxIYzjA23YYaAsvUEsDSEsOflssZzxjAPrluPHMM0CHNRw7bMxzO6fgQIIBbr+Y86cxf/y5o1q27/jZzR8bag7Lsc9O3/zhQ4WdHC4tMODYgOZg/MpBwJ6wklEwCkbBKBixAAAOTXVhYHdtbgAAAABJRU5ErkJggg==","orcid":"","institution":"Çanakkale Onsekiz Mart University","correspondingAuthor":true,"prefix":"","firstName":"Barış","middleName":"","lastName":"Baydemir","suffix":""}],"badges":[],"createdAt":"2025-07-25 22:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7217401/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7217401/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-35725-w","type":"published","date":"2026-01-10T15:58:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100069300,"identity":"fd5e32a7-54be-4fc2-aced-e785485ca88b","added_by":"auto","created_at":"2026-01-12 16:12:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":847503,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7217401/v1/cdc2b636-8ac6-4101-9cd9-e9c457cc1537.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Functional Movement Analysis (FMS) in Female Volleyball Players: Positional Differences, Injury Risk, and Asymmetries","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn today’s world, advancements in technology have led to a noticeable decline in physical activity and motor skills. This trend has also affected the consistency of training among both professional and amateur athletes. As a result, athletes frequently face the risk of injury during training or competitive performance. When such injuries occur, they must undergo treatment and participate in return-to-sport (RTS) rehabilitation to regain their performance levels. The literature contains a considerable number of studies addressing the RTS process following sports injuries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese studies indicate that the primary goal of an injured athlete is to return to sport as quickly as possible (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Researchers emphasize the sense of urgency experienced by athletes and define the RTS decision as primarily based on the type and severity of the injury, recovery of symptoms and function, and the athlete’s ability to tolerate the demands of the sport (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile injury and RTS studies hold ongoing significance, recent years have seen increasing interest in predicting injury risk before injuries occur. Studies across various sports disciplines aim to identify such risk factors. This issue has also been addressed in football-specific research (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, a review of the literature shows that there are relatively few studies focused on Functional Movement Screening (FMS) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). For this reason, the current study aims to investigate positional differences, injury risk, and asymmetries in functional movement patterns among female volleyball players.\u003c/p\u003e\u003cp\u003eThe positive effects of regular physical activity on health are well established (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, in performance sports such as volleyball, athletes are required to maintain high levels of physical conditioning (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Assessment tools such as FMS have the potential to identify asymmetries and weaknesses in fundamental movement patterns, offering insight into injury risk and ways to mitigate it. The use of such screening methods is crucial for athletes to achieve optimal performance across different playing positions.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eStudy Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis descriptive cross-sectional study aimed to investigate positional differences, injury risk, and asymmetries using the Functional Movement Screen (FMS) in professional female volleyball players. As the study did not involve any clinical intervention, registration in an international clinical trials database was not required. The research was conducted in accordance with the principles of the Declaration of Helsinki, and written informed consent was obtained from each participant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe target population comprised professional female volleyball players, with the sample selected from athletes actively competing in Çanakkale and Istanbul, Türkiye. A purposive sampling method was employed for participant selection. Participants were selected based on specific inclusion and exclusion criteria to ensure consistency and reliability in the assessment. The inclusion criteria were as follows: actively competing female volleyball players during the 2023–2024 season in the Turkish Women's 1st and 2nd Leagues; a minimum of three years of licensed volleyball experience; regular participation in training sessions (at least three times per week); no acute injuries at the time of data collection; and voluntary participation with signed informed consent. Exclusion criteria included having sustained a serious musculoskeletal injury (e.g., ligament rupture or meniscus surgery) within the past six months; presence of chronic orthopedic conditions (e.g., scoliosis, herniated disc); experiencing pain or discomfort preventing completion of the assessment; pregnancy; or refusal to provide informed consent or participate in the test procedures.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection Procedures\u003c/b\u003e\u003c/p\u003e\u003cp\u003e A total of 107 professional female volleyball players from clubs in Çanakkale (Çanakkale Belediyespor, Çan Gençlik Kale Spor, and Yeşil Bayramiç Spor) and Istanbul (Vakıfbank Sports Club) participated in the study. Anthropometric measurements—including height, body weight, and body mass index (BMI)—were recorded, and the FMS test was administered.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHeight Measurement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHeight was measured with participants standing barefoot using a SECA stadiometer (Germany) with a precision of 0.1 cm.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBody Weight Measurement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBody weight was measured using a SECA electronic scale (Germany) with an accuracy of 0.05 kg.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBMI Calculation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBody mass index was calculated using the following formula:\u003c/p\u003e\u003cp\u003eBMI = Body Weight (kg) / Height² (m²) [12].\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional Movement Screening (FMS)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFMS assessments included seven movement tasks: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight-leg raise, trunk stability push-up, and rotary stability. Each movement was scored as follows: 3 points for flawless, pain-free performance; 2 points for compensated but pain-free performance; 1 point for flawed performance; and 0 points for inability to perform or presence of pain. The highest possible composite score was 21, indicating perfect performance across all movements [13, 14].\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using the SPSS software package. As the sample size exceeded 30, the Kolmogorov–Smirnov test was used to evaluate the normality of the data. Since the data were not normally distributed, non-parametric tests were applied. The Wilcoxon Signed Ranks Test was used to assess asymmetries within participants, and the Kruskal–Wallis test was conducted to compare groups based on playing position. In cases where the Kruskal–Wallis test revealed significant differences, pairwise comparisons were made using the Mann–Whitney U test. The internal consistency of the FMS was evaluated using Cronbach’s alpha coefficient, indicating a reliable measurement tool.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCronbach’s Alpha Reliability Coefficient of the Study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCronbach's Alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the Cronbach’s Alpha reliability coefficient for the study was calculated as .728 based on a sample of 20 participants. This result indicates an acceptable level of internal consistency for the instrument used in the study, suggesting that the scale items are sufficiently correlated and measure a coherent construct.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic Characteristics of the Participating Athletes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e171.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e157.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e183.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e173.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e162.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e188.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e182.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e171.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e196.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e160.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e188.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e166.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e157.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e176.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eBody Weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e71.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e77.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e85.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e78.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e66.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, when examining the average ages of the players by position, setters (19.80 years) and opposite hitters (19.10 years) exhibited similar age means, while outside hitters (18.84 years) were relatively younger, and middle blockers (20.14 years) and liberos (20.15 years) were slightly older. In terms of height, middle blockers had the highest average (182.71 cm), followed by outside hitters (175.44 cm), opposite hitters (173.50 cm), setters (171.10 cm), and liberos (166.00 cm). Regarding body weight, middle blockers were the heaviest group (63.16 kg), whereas liberos were the lightest (56.66 kg). Setters (57.60 kg), opposite hitters (62.83 kg), and outside hitters (61.38 kg) fell in between. According to Body Mass Index (BMI) values, middle blockers had the lowest average (18.75), while opposite hitters had the highest (20.79). The averages for setters, outside hitters, and liberos were 19.65, 19.90, and 20.58, respectively. These findings indicate that the physical profiles of volleyball players are closely aligned with the specific physical demands of their playing positions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of FMS Scores by Playing Positions in Volleyball Players\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKruskal Wallis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDeep Squat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e5.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e.228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHurdle Step\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e1.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e.834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eInline Lunge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e8.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eShoulder Mobility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e1.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e.858\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eActive Straight-Leg Raise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e.298\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eTrunk Stability Push-Up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e7.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e.110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eRotary Stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSetter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e.330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpposite Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle Blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutside Hitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLibero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the study investigated whether Functional Movement Screening (FMS) test scores differed by playing position among volleyball players. The Kruskal-Wallis test was used for the analysis. In the Deep Squat test, the result was χ\u0026sup2; = 5.635 (df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.228), indicating no statistically significant difference in performance among positions. Similarly, the Hurdle Step test yielded a result of χ\u0026sup2; = 1.457 (df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.834), suggesting no significant difference between positions.\u003c/p\u003e\u003cp\u003eFor the In-Line Lunge test, the Kruskal-Wallis result was χ\u0026sup2; = 8.885 (df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.064), showing no statistically significant difference. The Shoulder Mobility test produced a result of χ\u0026sup2; = 1.322 (df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.858), also indicating no significant variation across positions. In the Active Straight-Leg Raise test, the result was χ\u0026sup2; = 4.897 (df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.298), again showing no meaningful difference by position.\u003c/p\u003e\u003cp\u003eThe Trunk Stability Push-Up test had a result of χ\u0026sup2; = 7.550 (df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.110), while the Rotary Stability test yielded χ\u0026sup2; = 4.606 (df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.330), both of which revealed no significant positional differences. Overall, none of the FMS subtests demonstrated statistically significant differences across playing positions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of FMS Scores According to Injury Risks in Volleyball Players\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRank average\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKruskal Wallis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDeep Squat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e4.538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHurdle Step\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e103.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e13.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e0\u0026ndash;1\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026ndash;3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eInline Lunge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e4.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eShoulder Mobility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e14.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e0\u0026ndash;1\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026ndash;3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eActive Straight Leg Raise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e2.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eTrunk Stability Push-Up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e10.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e0\u0026ndash;1\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026ndash;3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eRotary Stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e3.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the evaluation of injury risks among volleyball players based on their Functional Movement Screen (FMS) scores. The FMS rates each movement on a scale from 0 to 3, where a score of 3 represents a flawless and pain-free performance, 2 indicates a compensated but pain-free movement, 1 is given when the movement is performed with noticeable faults, and 0 is assigned when the movement elicits pain.\u003c/p\u003e\u003cp\u003eIn the Deep Squat test, the Kruskal-Wallis result was 4.538 (df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.209), with four athletes scoring 0 and an average rank of 49.50. These findings indicate that Deep Squat performance was not significantly associated with injury risk (p\u0026thinsp;=\u0026thinsp;0.209).\u003c/p\u003e\u003cp\u003eFor the Hurdle Step test, one athlete scored 0 with an average rank of 103.00, and the Kruskal-Wallis value was 13.617 (df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.003). Further analysis using the Mann-Whitney U test showed a statistically significant difference between athletes with low scores (0 and 1) and those with high scores (2 and 3) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that lower scores are associated with reduced performance.\u003c/p\u003e\u003cp\u003eIn the Inline Lunge test, the Kruskal-Wallis statistic was 4.026 (df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.134), indicating no significant relationship between FMS scores and injury risk (p\u0026thinsp;=\u0026thinsp;0.134). For the Shoulder Mobility test, two athletes who scored 0 had an average rank of 49.50, and the Kruskal-Wallis value was 14.546 (df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.002). The Mann-Whitney U test also showed a significant difference between low and high score groups (p\u0026thinsp;=\u0026thinsp;0.006), demonstrating that athletes with lower scores had significantly reduced shoulder mobility.\u003c/p\u003e\u003cp\u003eThe Kruskal-Wallis statistic for the Active Straight-Leg Raise test was 2.652 (df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.448), revealing no significant association between test scores and injury risk (p\u0026thinsp;=\u0026thinsp;0.448).\u003c/p\u003e\u003cp\u003eIn the Trunk Stability Push-Up test, a Kruskal-Wallis value of 10.640 (df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.014) indicated a statistically significant relationship between lower scores and increased injury risk. Pairwise comparisons using the Mann-Whitney U test confirmed a significant difference between low and high score groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that reduced trunk stability is associated with poorer performance and potentially higher injury risk.\u003c/p\u003e\u003cp\u003eRegarding the Rotary Stability test, the Kruskal-Wallis result was 3.449 (df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.327), indicating no statistically significant association between FMS scores and injury risk in this movement pattern (p\u0026thinsp;=\u0026thinsp;0.327).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAsymmetry Scores in FMS Test among Volleyball Players\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRank average\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRank Total\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHurdle Step\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e576.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e-1.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e802.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eInline Lunge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e252.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e-1.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e126.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eShoulder Mobility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e421.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e-4.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eActive Straight Leg Raise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e341.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e.634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e289.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRotary Stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e177.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e-1.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive Rankings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e99.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the analysis of right\u0026ndash;left asymmetries in volleyball players using the Wilcoxon Signed Ranks Test. The hypothesis was evaluated by examining performance differences between the right and left sides of the body across various functional movement tasks.\u003c/p\u003e\u003cp\u003eIn the Hurdle Step test, the mean rank of negative ranks was 26.18 (total\u0026thinsp;=\u0026thinsp;576.00), and the mean rank of positive ranks was 26.73 (total\u0026thinsp;=\u0026thinsp;802.00). The test result was not statistically significant (z = -1.153, p\u0026thinsp;=\u0026thinsp;0.249), indicating no meaningful asymmetry between sides.\u003c/p\u003e\u003cp\u003eFor the Inline Lunge test, both negative and positive ranks had a mean rank of 14.00, with totals of 252.00 and 126.00, respectively. The test result (z = -1.732, p\u0026thinsp;=\u0026thinsp;0.083) suggested a borderline, but still non-significant, asymmetry.\u003c/p\u003e\u003cp\u003eIn the Shoulder Mobility test, the mean rank for negative ranks was 15.61 (total\u0026thinsp;=\u0026thinsp;421.50), and for positive ranks it was 14.50 (total\u0026thinsp;=\u0026thinsp;43.50). The test revealed a statistically significant asymmetry (z = -4.328, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), favoring the negative ranks, suggesting that scores on the left side were lower than those on the right.\u003c/p\u003e\u003cp\u003eIn the Active Straight-Leg Raise test, the mean rank of negative ranks was 18.94 (total\u0026thinsp;=\u0026thinsp;341.00), while the positive ranks had a mean of 17.00 (total\u0026thinsp;=\u0026thinsp;289.00). The result (z = -0.476, p\u0026thinsp;=\u0026thinsp;0.634) indicated no significant difference in asymmetry.\u003c/p\u003e\u003cp\u003eFinally, in the Rotary Stability test, the mean rank of negative ranks was 12.64 (total\u0026thinsp;=\u0026thinsp;177.00), and for positive ranks it was 11.00 (total\u0026thinsp;=\u0026thinsp;99.00). The analysis showed no significant asymmetry (z = -1.308, p\u0026thinsp;=\u0026thinsp;0.191).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study aimed to assess FMS scores, injury risk, and asymmetries among female volleyball players according to playing positions. The results showed no statistically significant differences in FMS test scores across different positions for deep squat (p\u0026thinsp;=\u0026thinsp;0.228), hurdle step (p\u0026thinsp;=\u0026thinsp;0.834), inline lunge (p\u0026thinsp;=\u0026thinsp;0.064), shoulder mobility (p\u0026thinsp;=\u0026thinsp;0.858), active straight-leg raise (p\u0026thinsp;=\u0026thinsp;0.298), trunk stability push-up (p\u0026thinsp;=\u0026thinsp;0.110), and rotary stability (p\u0026thinsp;=\u0026thinsp;0.330). Regarding injury risk, no significant associations were found for deep squat (p\u0026thinsp;=\u0026thinsp;0.209), inline lunge (p\u0026thinsp;=\u0026thinsp;0.134), active straight-leg raise (p\u0026thinsp;=\u0026thinsp;0.448), or rotary stability (p\u0026thinsp;=\u0026thinsp;0.327). However, significant associations were observed between FMS scores and injury risk in the hurdle step (p\u0026thinsp;=\u0026thinsp;0.003), shoulder mobility (p\u0026thinsp;=\u0026thinsp;0.002), and trunk stability push-up (p\u0026thinsp;=\u0026thinsp;0.014) tests.\u003c/p\u003e\u003cp\u003eAs for right\u0026ndash;left asymmetries, no statistically significant differences were detected in the hurdle step (p\u0026thinsp;=\u0026thinsp;0.249), inline lunge (p\u0026thinsp;=\u0026thinsp;0.083), active straight-leg raise (p\u0026thinsp;=\u0026thinsp;0.634), or rotary stability (p\u0026thinsp;=\u0026thinsp;0.191) tests. A significant asymmetry was found only in the shoulder mobility test.\u003c/p\u003e\u003cp\u003eThe literature reveals a limited number of studies investigating the relationship between technical skills and FMS performance. However, some studies have addressed the association between technical ability and athletic performance (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) or the predictive value of FMS for injury risk (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). A systematic review examined whether variables such as athlete age, sex, sport type, injury definition, and injury mechanism contribute to inconsistent findings. It was suggested that FMS composite scores and asymmetries may be more predictive of injury risk in older athletes compared to younger ones. Additionally, in athletes from sports such as rugby, ice hockey, and American football, the effect sizes of FMS scores tended to be small (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn one study, FMS scores were used to assess functional capacity and injury susceptibility. The findings indicated that athletes who scored below 17 on the composite FMS had approximately 4.7 times higher odds of experiencing lower extremity injuries during a regular season (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Another systematic review evaluated the methodological quality and heterogeneity of studies examining the association between FMS composite scores and subsequent injury risk. It concluded that the predictive value of FMS was insufficient to support its use as a stand-alone injury prediction tool (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Similarly, a separate review explored whether FMS scores were associated with future injuries in healthy athletes and found inconclusive results. About half of the included studies reported that lower FMS scores were statistically associated with an increased risk of sports injuries. The heterogeneity in study populations (e.g., athlete type, age, sport exposure) and inconsistency in injury definitions were cited as major barriers to synthesizing the evidence and drawing definitive conclusions (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResearchers have investigated the predictive power of the FMS, with some findings contradicting previous studies by suggesting that the FMS may not be a useful tool for estimating the risk of musculoskeletal injuries (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Another study aimed to determine whether the FMS is a valid predictor of injury among high school athletes and whether a new scoring system could be developed for this population. The results indicated that while the FMS may be helpful in identifying deficiencies in specific movements, it should not be used to predict general injury risk over the course of a season in high school athletes (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegarding studies on FMS and asymmetries, most have focused on football. In volleyball, limited research exists. One study examined changes in functional movement patterns over a season among university-level football and volleyball players. The study found no significant changes in overall FMS scores over time or between sports. However, some subtests showed notable differences: performance improved in the Deep Squat and Inline Lunge tests, while scores declined in the Active Straight-Leg Raise and Rotary Stability tests. Additionally, there was a decrease in asymmetry levels and in the number of low-scoring test components by the end of the season (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn another study involving right-hand dominant female volleyball players in the Croatian Women\u0026rsquo;s Premier League, researchers investigated whether right\u0026ndash;left asymmetries existed in range of motion and movement quality. The results revealed significant differences in the Shoulder Mobility and Active Straight-Leg Raise tests. It was suggested that the superior shoulder mobility in the dominant arm could be attributed to fewer spike and serve repetitions compared to elite-level players (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurther research has shown that shoulder muscle strength asymmetries are commonly observed in volleyball players, largely due to the dominant arm\u0026rsquo;s frequent use. These asymmetries in the non-dominant arm may contribute to imbalances in strength development and are considered potential risk factors for shoulder injuries (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn conclusion, FMS appears to be a valuable tool for assessing performance and estimating injury risk among volleyball players. FMS testing can be effectively used to enhance athletic performance and reduce injury risk. Developing individualized training programs based on FMS results may help minimize injuries and optimize athletic outcomes. Regular implementation of FMS assessments\u0026mdash;particularly for female volleyball players\u0026mdash;and their integration into training regimens is of critical importance. Consequently, systematic use of FMS and the development of personalized training strategies should be considered essential steps toward maximizing athletic performance and minimizing injury risk. Future studies with larger sample sizes, diverse age groups, sports disciplines, and levels of expertise\u0026mdash;across both individual and team sports\u0026mdash;are recommended.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Çanakkale Onsekiz Mart University Graduate Education Institute Scientific Research Ethics Committee (Approval Date: 01/12/2022; Approval Number: 21/33). All procedures were conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all individual participants prior to data collection.\u003c/p\u003e\n\u003cp\u003eAs this study does not involve any clinical intervention or healthcare-related treatment, registration in a clinical trial registry was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not contain any individual person’s data in any form (including images, videos, or identifiable personal information).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support from any organization for the research, authorship, or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGE contributed to the study design, data collection, data analysis, and writing of the manuscript. BB provided supervision throughout the research process, contributed to data interpretation, and critically revised the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is part of a master's thesis titled “Functional Movement Analysis (FMS) in Volleyball Players: Positional Differences, Injury Risk, and Asymmetries” completed in 2024 at Çanakkale Onsekiz Mart University under the supervision of Dr. Barış Baydemir.\u003c/p\u003e\n\u003cp\u003eWe would also like to express our sincere gratitude to Üzeyir Özdurak, Youth Development Coordinator of Vakıfbank, and his team; Vedat Mekik, Head Coach of Çanakkale Belediyespor, and his team; İbrahim Kırmış, Head Coach of Çan Gençlik Kale Sports Club, and his team; and Melda Bora, Head Coach of Yeşil Bayramiç Sports Club, and her team, for their valuable support throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBarış Baydemir – ORCID: https://orcid.org/0000-0002-8653-0664\u003c/p\u003e\n\u003cp\u003eGözde Emir Uysal – ORCID: \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKvist, J., \u0026amp; Silbernagel, K. 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(2014). \u0026ldquo;Relationship between functional movement screening score and history of injury and identifying the predictive value of the FMS for injury\u0026rdquo;. \u003cem\u003eInternational journal of injury control and safety promotion\u003c/em\u003e, 21(4), 355-360. \u003c/li\u003e\n\u003cli\u003eTrinand-Fernandez, M., Gonzalez-Sanchez, M. Cuesta-Vargas, A. I. (2019). \u0026ldquo;Is a low Functional Movement Screen score (\u0026le; 14/21) associated with injuries in sport? A systematic review and meta-analysis\u0026rdquo;. \u003cem\u003eBMJ open sport \u0026amp; exercise medicine\u003c/em\u003e, 5(1), e000501.\u003c/li\u003e\n\u003cli\u003eWiese, B. W., Boone, J. K., Mattacola, C. G., McKeon, P. O., Uhl, T. L. (2014). \u0026ldquo;Determination of the functional movement screen to predict musculoskeletal injury in intercollegiate athletics\u0026rdquo;. \u003cem\u003eAthletic Training \u0026amp; Sports Health Care\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4), 161-169.\u003c/li\u003e\n\u003cli\u003eBardenett, S. M., Micca, J. J., DeNoyelles, J. T., Miller, S. D., Jenk, D. T., Brooks, G. S. (2015). \u0026ldquo;Functional movement screen normative values and validity in high school athletes: can the FMS\u0026trade; be used as a predictor of injury?\u0026rdquo;. \u003cem\u003eInternational journal of sports physical therapy\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), 303.\u003c/li\u003e\n\u003cli\u003eSprague, P. A., Mokha, G. M., \u0026amp; Gatens, D. R. (2014). Changes in functional movement screen scores over a season in collegiate soccer and volleyball athletes. \u003cem\u003eThe Journal of Strength \u0026amp; Conditioning Research\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(11), 3155-3163.\u003c/li\u003e\n\u003cli\u003eĐurković, T., Ban, M., \u0026amp; Marelić, N. (2017, May). Asymmetry in functional movements in Croatian women\u0026rsquo;s Premier league volleyball players. In \u003cem\u003e8th International scientific conference on kinesiology, Proceedings Book, Opatija, Croatia\u003c/em\u003e (pp. 25-28).\u003c/li\u003e\n\u003cli\u003eZuzgina, O., \u0026amp; Wdowski, M. M. (2019). Asymmetry of dominant and non-dominant shoulders in university level men and women volleyball players. \u003cem\u003eHuman Movement\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(4), 19-27.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"volleyball, female athletes, functional movement screen, asymmetry, injury risk","lastPublishedDoi":"10.21203/rs.3.rs-7217401/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7217401/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to identify positional differences, injury risk, and asymmetries in functional movement patterns among female volleyball players. A total of 107 professional female athletes from the provinces of \u0026Ccedil;anakkale and Istanbul participated in the study. Height and body weight measurements were taken to calculate body mass index (BMI), and the Functional Movement Screen (FMS) was administered. Data were analyzed using the SPSS statistical software package. The Wilcoxon Signed Ranks Test was employed for asymmetry analyses, while the Kruskal-Wallis test was used to evaluate differences across playing positions.\u003c/p\u003e\u003cp\u003eThe findings revealed no statistically significant differences in FMS test scores among players in different positions for deep squat (p\u0026thinsp;=\u0026thinsp;0.228), hurdle step (p\u0026thinsp;=\u0026thinsp;0.834), inline lunge (p\u0026thinsp;=\u0026thinsp;0.064), shoulder mobility (p\u0026thinsp;=\u0026thinsp;0.858), active straight-leg raise (p\u0026thinsp;=\u0026thinsp;0.298), trunk stability push-up (p\u0026thinsp;=\u0026thinsp;0.110), and rotary stability (p\u0026thinsp;=\u0026thinsp;0.330). Regarding injury risk, no significant associations were observed between FMS scores and injury risk for deep squat (p\u0026thinsp;=\u0026thinsp;0.209), inline lunge (p\u0026thinsp;=\u0026thinsp;0.134), active straight-leg raise (p\u0026thinsp;=\u0026thinsp;0.448), and rotary stability (p\u0026thinsp;=\u0026thinsp;0.327); however, statistically significant associations were found for hurdle step (p\u0026thinsp;=\u0026thinsp;0.003), shoulder mobility (p\u0026thinsp;=\u0026thinsp;0.002), and trunk stability push-up (p\u0026thinsp;=\u0026thinsp;0.014). In terms of right-left asymmetries, no significant differences were identified in hurdle step (p\u0026thinsp;=\u0026thinsp;0.249), inline lunge (p\u0026thinsp;=\u0026thinsp;0.083), active straight-leg raise (p\u0026thinsp;=\u0026thinsp;0.634), and rotary stability (p\u0026thinsp;=\u0026thinsp;0.191), while shoulder mobility showed a statistically significant asymmetry between the right and left sides.\u003c/p\u003e\u003cp\u003eIn conclusion, FMS performance was found to be similar across playing positions among female volleyball players. Additionally, FMS testing appears to be a valuable tool for assessing injury risk and detecting side-to-side asymmetries in this population.\u003c/p\u003e","manuscriptTitle":"Functional Movement Analysis (FMS) in Female Volleyball Players: Positional Differences, Injury Risk, and Asymmetries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-09 05:31:57","doi":"10.21203/rs.3.rs-7217401/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T09:47:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T03:06:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148149306108444503468683843201079442879","date":"2025-09-17T01:55:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T17:06:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269184690296235508352158369900319273423","date":"2025-08-18T03:14:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-05T22:31:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T08:31:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T09:46:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-25T21:53:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1321c748-ef02-4be7-b03a-04f04bddd5ea","owner":[],"postedDate":"August 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52706017,"name":"Health sciences/Anatomy"},{"id":52706018,"name":"Health sciences/Health care"},{"id":52706019,"name":"Health sciences/Health occupations"},{"id":52706020,"name":"Health sciences/Medical research"},{"id":52706021,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-12T16:04:06+00:00","versionOfRecord":{"articleIdentity":"rs-7217401","link":"https://doi.org/10.1038/s41598-026-35725-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-10 15:58:20","publishedOnDateReadable":"January 10th, 2026"},"versionCreatedAt":"2025-08-09 05:31:57","video":"","vorDoi":"10.1038/s41598-026-35725-w","vorDoiUrl":"https://doi.org/10.1038/s41598-026-35725-w","workflowStages":[]},"version":"v1","identity":"rs-7217401","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7217401","identity":"rs-7217401","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}