Functional Movement Quality, Dynamic Balance and Injury Prevalence in Adolescent Soccer Players: Comparison with Sedentary Adolescents

Functional Movement Quality, Dynamic Balance and Injury Prevalence in Adolescent Soccer Players: Comparison with Sedentary Adolescents | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Functional Movement Quality, Dynamic Balance and Injury Prevalence in Adolescent Soccer Players: Comparison with Sedentary Adolescents Abdullah GÜLLÜ, Merve BOZ CİNCİ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9348731/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Functional movement quality and dynamic balance are key components that play an important role in relation to injury prevalence in adolescent athletes. Rapid growth and neuromuscular changes during mid-adolescence can increase susceptibility to movement deficiencies and sports-related injuries. Comparing physically active and sedentary adolescents within the same methodological framework may contribute to a better understanding of modifiable injury prevalence factors. Methods: A total of 184 adolescents aged 14–16 years were included in the study (48 male soccer players, 45 female soccer players, 48 sedentary males, and 43 sedentary females). Functional movement quality was assessed using Functional Movement Analysis (FMS), and dynamic balance was assessed using the Y-Balance Test. Agility, flexibility, and coordination tests were applied to assess biomotor capacities. The differences between the functional movement quality, dynamic balance, and biomotor performance variables of the research group were examined using one-way ANOVA followed by multiple comparisons. The effects of activity level (soccer player/sedentary), gender (male/female), and biological maturity offset (MO) factors on motor performance variables were analysed using 2x2 factorial ANCOVA. The relationships between the groups’ FMS, Y-Balance scores, and biomotor performance capacities were examined using Pearson correlation analysis. Results: Soccer player groups exhibited significantly higher FMS scores (p<0.05), Y-Balance composite scores (p<0.001), and agility performance (p<0.001) compared to sedentary participants. Activity level emerged as the primary determinant for all motor performance variables (p<0.001), while the effect of gender was more limited. Significant correlations were found between FMS and Y-Balance test scores and agility and coordination performances (ranging from r=−0.375 to r=0.385, p0.05). Conclusion: Regular soccer training was found to positively affect the functional movement quality, dynamic balance, and agility performance of adolescents. The results suggested that FMS and Y-Balance Tests were more suitable tools for identifying individual movement deficiencies and planning targeted training programs in this age group, rather than solely predicting future injury. Sports Medicine and Kinesiology Functional movement analysis Y-Balance test dynamic balance motor performance injury prevalence adolescent athletes INTRODUCTION Sports training programs, such as soccer training implemented in educational institutions, make significant contributions to the physical development and social adaptation processes of children during adolescence. The prevalence of sports-related injuries is more pronounced, particularly in the 14–16 age group, a period when growth accelerates and motor control is being reshaped [1,2]. Self-reported injury prevalence shows significant gender differences during adolescence; female athletes exhibit different injury patterns compared to male athletes due to factors such as anatomical features, differences in neuromuscular control, and hormonal influences [3,4]. For example, anterior cruciate ligament (ACL) injuries are 2–8 times more common in female athletes than in male athletes. Therefore, considering gender in methods for preventing and evaluating sport-related injuries is important [5]. However, it is also noted that gender-based movement differences and decreased performance may occur in athletes with similar regular training histories. In this context, school sports programs can offer an important opportunity for observation and diagnosis in terms of early identification of sport injury history that may arise during adolescence. Injuries not only negatively affect athletic performance, but can also lead to long-term functional losses, decreased quality of life, and negative economic, sociological, and psychological consequences [6,7]. Biomotor components such as flexibility, coordination, agility, and balance are improvable traits and therefore form the basis of injury prevention programs, and deficiencies in these components are included in injury prevalence factors [8]. Young athletes are more likely to experience lower extremity injuries due to deficiencies in these motor skills [8,9]. In soccer, contact-based challenges, sudden changes of direction, repeated acceleration and deceleration cycles, and high-intensity training periods all contribute to an increase in injuries, particularly in the lower extremities [10–12]. Therefore, identifying injury proneness in advance for young players on school soccer teams, and providing personalized protective equipment and environments, is crucial to prevent them from suffering sports injuries. It has been reported that there is a strong association between the increasing sedentary lifestyles of adolescents in recent years and musculoskeletal disorders [13], and that this effect is more pronounced especially in female adolescents with neck and back pain [14]. As is known, adolescents who do not regularly participate in physical activity experience significant decreases in dynamic balance, neuromuscular coordination, and functional movement quality, leading to increased injury prevalence rates and negatively impacting their quality of life. FMS, although a widely used screening tool for assessing movement quality, has limited predictive power in terms of injury prediction [15,16]. However, recent studies show that FMS scores, supported by motor performance tests, can provide more accurate movement risk profiles [17]. For example, evaluating performance tests representing components such as flexibility, coordination, agility, and balance in conjunction with FMS can more comprehensively characterise functional movement deficiencies, especially in adolescent female athletes. Because recent studies have shown that assessments taking gender and biological differences into account are more successful in explaining functional performance of female athletes, especially [3,5]. Therefore, gender needs to be considered, especially in sport injury prevention approaches [5]. Because gender differences are pronounced during adolescence, women are more susceptible than men to the negative effects of sedentary behaviour [3,14]. Accordingly, it is believed that evaluating both sedentary and active groups separately for both genders in studies can lead to the development of more effective and goal-oriented strategies. In summary, comparing the motor risk profiles of sedentary individuals with those of soccer players is crucial for both sports’ injury prevention and public health. Unlike previous studies, this study had simultaneously examined five motor performance components (FMS, Y-Balance Test, agility, flexibility, and coordination) within a fully factorial 2x2 design encompassing activity level, gender, and biological maturity assessment in a school-based Turkish adolescent sample. The FMS and Y-Balance Test were selected for use in this study because both instruments have demonstrated validity and reliability in adolescent populations, offer standardised field-applicable protocols, and provide complementary information on functional movement quality and dynamic balance respectively [18–21]. Their combined use within a multi-component battery can enable a more comprehensive movement profile than either tool alone or existing normative data can support their appropriateness for the 14–16-year age range [17,22]. In light of the information provided above, the aim of our study was to determine the injury prevalence profiles of male and female players aged 14–16 who were actively involved in school soccer teams, as well as sedentary male and female individuals, based on functional movement quality and motor performance capacity, and to examined the interaction of activity level, gender, and biological maturity factors. Inclusion of sedentary adolescents in this study can allow for a comparative interpretation of functional and motor-based injury history profiles. This study can guide coaches and physical education teachers in structuring injury prevention training programs specific to adolescence and gender. In this respect, this study has the potential to offer generalizable implications for school sports and youth sports programs in different countries. What this study adds: To our knowledge, this is the first school-based study in Türkiye to simultaneously profile five motor performance components (FMS, Y-Balance Test, agility, flexibility, and coordination) within a fully factorial 2x2 design that accounts for activity level, sex, and biological maturity in the same sample. The findings can provide practical benchmarks for coaches, physical education teachers, and physiotherapists working in school sport settings: specifically, that activity level –not biological maturity or sex– is the dominant determinant of functional movement quality and dynamic balance in adolescents, and that FMS and Y-Balance assessments can serve as useful tools for identifying movement deficiencies and designing individualised training programmes, rather than for prospective injury prediction alone. METHODS This study was designed and reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational cross-sectional studies [23]. The completed STROBE checklist is provided as Supplementary File 1 Research Group This study included a total of 184 participants (48 male soccer players, 45 female soccer players, 48 sedentary male students, and 43 sedentary female students) aged 14-16 years old, studying at high schools affiliated with the Ministry of National Education in the city centre of Uşak. The participants were divided into four different groups based on activity level (soccer player/sedentary) and gender (male/female) variables. These were the male soccer player group (G1), the male sedentary group (G2), the female soccer player group (G3), and the female sedentary group (G4), respectively. The soccer groups (G1 and G3) consisted of actively licensed athletes who were part of the school soccer team and trained regularly at least 4 days a week for at least 2 hours per training session. The sedentary groups (G2 and G4) consisted of individuals who had not regularly participated in exercise or sports activities in the past six months and did not hold a license in any sport. All participants were selected based on the following criteria: no active musculoskeletal injury that could affect performance during measurements, no history of surgery within the last six months, no acute or chronic illness, or no regular medication use. Subjects who did not meet these criteria were excluded from the study. Ethical Approval and Voluntary Participation Prior to this study, ethical approval was obtained from the Uşak University Non-Interventional Clinical Research Ethics Committee (Date: 18/05/2023, Decision No: 02). All participants and their parents or legal guardians were informed verbally, in writing, and visually about the purpose, scope, and tests to be performed. Since the research group consisted of individuals under 16 years of age, written informed consent was obtained from their parents or legal guardians. Verbal assent was also obtained from all participants. All procedures were conducted in accordance with the principles of the Helsinki Declaration. Sample Size The sample size of study was calculated using Power analysis (G*Power 3.1) based on average effect sizes from similar studies [24] and a minimum of 42 participants was found required for each group. Since the study had a 2x2 factorial design [gender (female/male) x activity status (athlete/sedentary)], power calculations were performed based on two-way ANOVA (2x2 factorial ANOVA; effect size f=0.25; power 80%; p<0.05). To account for potential data loss (10-15% dropout rate) and measurement errors, groups were formed with a minimum of 43 and a maximum of 48 participants. Small variations in group sizes resulted from the participation criteria and the voluntary sampling process. Application Process All measurements for the research group were performed over a week and at the same time each day by trainers and physiotherapists with at least 5 years of experience in the field. This minimized measurement errors and the effects of biological rhythms and intraday performance variables. Prior to the tests, necessary explanations about the test content were provided, and a standard 10-minute warm-up program was implemented. All tests were ranked from lowest to highest according to performance intensity levels. This minimized potential performance degradation due to fatigue. Passive recovery periods were applied between tests to allow sufficient time for participants to prepare for the second load. Anthropometric Measurements The ages of the research group were determined according to their national identity card (chronological age) information. Participants’ height was measured in meters using a stadiometer (Harpenden Holtain, UK) with an accuracy of ±0.01 cm, with bare feet together and in an upright standing position. Sitting height was measured using the same stadiometer with the participant seated on a flat surface, maintaining an upright posture; leg length was derived as the difference between standing height and sitting height. Body weight was measured using a digital scale (Seca 877 GmbH, Hamburg, Germany). Body mass index (BMI) was calculated using the formula [body weight (kg) / height (m²)] [25]. Biological Maturity Assessment Biological maturity status was estimated for all participants using the maturity offset (MO) method described by Mirwald et al. (2002). MO reflects the number of years before or after Peak Height Velocity (PHV) and is calculated from five anthropometric variables: standing height, sitting height, leg length (standing height minus sitting height), body mass, and chronological age –all of which were routinely collected during the measurement protocol of the present study. Sex-specific regression equations were applied as follows: For males: MO = − 9.236 + 0.0002708 × (leg length x sitting height) − 0.001663 x (age x leg length) + 0.007216 x (age x sitting height) + 0.02292 x (body mass / standing height x 100) For females: MO = − 9.376 + 0.0001882 x (leg length x sitting height) + 0.0022 x (age x leg length) + 0.005841 x (age x sitting height) − 0.002658 x (age x body mass) + 0.07693 x (body mass / standing height x 100) Participants were classified into three maturity categories based on their MO value: pre-PHV (MO 1 year) [27,28]. Maturity offset was calculated and reported descriptively by group. Functional and Biomotor Measurements The study group's susceptibility to injury was determined using the FMS test, and their biomotor characteristics were assessed using Y-balance, flexibility, Illinois agility, and hexagonal coordination tests, respectively. Standard application protocols described in the relevant literature were applied for all measurements. Functional and Biomotor Performance Assessments Functional Movement Screen (FMS) : The study group was scored on 7 basic movements (deep squat, step over obstacle, linear forward lunge, shoulder mobility, active straight leg raises, trunk stability push-up, and rotational stability) using FMS measurements. Each movement was evaluated according to standard protocols with a score between 0 and 3, and the total score was calculated between 0 and 21 points (X=0-21), with X<14 points considered as high risk of injury [18,19]. Y-Balance Test : This test was performed using a specialized Y-Balance Test kit (a graded three-arm platform) with sliding measurement blocks. The test was administered three times to assess lower extremity dynamic balance performance. Maximum best reach distances obtained from anterior, posteromedial, and posterolateral directions were measured for both extremities (excluding invalid trials) and used in the analysis. The obtained raw values were normalized to leg length, and percentage scores were calculated. The composite score was determined using the formula: [(Anterior + Posteromedial + Posterolateral) / (3 x Leg length) x 100]. An anterior difference of >4 cm between the right and left was considered an indicator of functional asymmetry [20,21]. Flexibility Test : To determine the flexibility of the lower extremity and posterior trunk muscle groups, the sit-and-reach test was performed three times using a flexibility bench. The best measurement obtained was recorded in centimetres for use in the analysis [25]. Agility Test : Agility performance was assessed twice using the Illinois Agility Test. The research group was asked to complete a course containing different acceleration components in the shortest possible time, and the best test time was recorded in seconds [29]. Coordination Test : To assess coordination and multifaceted movement skills, the Hexagonal Coordination Test was administered twice, and the shortest completion time was recorded in seconds [30]. Injury Data Collection Injury history data for the research group were collected through structured face-to-face interviews using a standardized interview form developed by the researchers (Supplementary File 1) [31]. Participants were asked standardized questions regarding injuries experienced during the previous 12 months. In this study, an injury was defined as a musculoskeletal injury that prevented participation in training or competition for at least one day; non-sport injuries were excluded from the analysis. Recurrent injuries in the same anatomical region were counted as separate events. Injuries were classified according to injury type (acute or chronic), anatomical location (lower extremity, upper extremity, or trunk), and severity based on the number of days lost from training or competition (Supplementary File 2) [31]; however, severity categories were used for descriptive purposes only and were not included as variables in the inferential analyses. The information obtained during the interviews was based on participant self-reports and was cross-checked with school medical records. It should be noted that this study assessed 12-month retrospective injury history (self-reported injury prevalence) and did not prospectively track injury incidence; accordingly, FMS and Y-Balance Test scores cannot be interpreted as predictors of future injury in this sample. Statistical Analysis Descriptive statistics of the data obtained from this research were calculated, including arithmetic mean and standard deviation. The normality of the data distribution was checked using the Shapiro-Wilk test, and the homogeneity of variances was checked using the Levene test (p>0.05), and parametric tests were applied. Differences between groups were determined using one-way ANOVA, and effect sizes were calculated using eta-squared (η²). For variables with a significant difference, Tukey HSD post-hoc comparisons were used to determine which group the difference originated from. A two-way ANCOVA with a 2x2 factorial design was used to examine the effects of activity level (soccer player/sedentary), gender (male/female), and biological maturation factors on the motor performance capacity of the groups. This allowed for the interpretation of activity-level and sex-based comparisons after statistically adjusting for differential pubertal timing between male and female participants. The relationships between the groups' FMS, Y-Balance scores, and biomotor performance capacities were examined using the Pearson correlation coefficient (r). All statistical analyses were evaluated using the SPSS 25.0 statistical package program (IBM Corp., USA) at a significant level of p<0.05. RESULTS Table 1 presented the demographic information of the data obtained from the research group, including mean and standard deviation. Table 1: Demographic variables of the research group. Variables Male Soccer Players (n=48) Male Sedentary Students (n=48) Female Soccer Players (n=45) Female Sedentary Students (n=43) Mean SD Mean SD Mean SD Mean SD Age (yrs) 15.07 0.81 15.14 0.85 14.80 0.91 15.04 0.88 Height (m) 1.77 0.08 1.75 0.06 1.63 0.07 1.62 0.05 Weight (kg) 66.86 9.35 63.96 8.91 54.36 7.95 58.39 8.76 BMI (kg/m²) 21.22 2.17 20.80 2.23 20.37 2.22 22.13 2.87 SD: Standard deviation. Table 2: Descriptive statistics values of the research group. Variables Male Soccer Players (n=48) Male Sedentary Students (n=48) Female Soccer Players (n=45) Female Sedentary Students (n=43) Mean SD Mean SD Mean SD Mean SD Total FMS Score 15.68 1.54 12.93 2.29 15.80 1.96 13.22 2.11 Right Y-Balance Composite (%) 95.95 7.34 92.02 10.66 92.44 9.63 77.41 7.58 Left Y-Balance Composite (%) 95.17 6.96 91.94 11.80 91.52 12.57 80.50 9.16 Right Anterior (%) 77.46 6.70 69.79 10.15 62.04 8.86 53.91 9.77 Right Posteromedial (%) 99.25 8.75 95.75 12.13 90.52 8.97 76.61 10.60 Right Posterolateral (%) 101.00 6.84 97.75 12.42 86.88 11.31 71.83 12.78 Left Anterior (%) 78.00 6.91 67.79 9.97 61.12 10.20 55.30 11.14 Left Posteromedial (%) 98.14 7.48 98.46 14.04 89.84 11.15 79.39 12.50 Left Posterolateral (%) 99.79 6.76 96.32 12.91 84.24 14.43 73.13 17.26 Agility (sec) 16.46 1.46 20.85 1.73 19.25 2.50 22.73 2.41 Flexibility (cm) 26.64 3.96 23.07 5.44 26.62 3.46 23.35 5.06 Coordination (sec) 12.96 1.21 17.04 1.83 14.90 1.59 17.91 1.90 SD: Standard deviation. Table 2 presented the descriptive statistical analyses of the data obtained from the research group, including mean and standard deviation. Group classifications included male soccer players, male sedentary individuals, female soccer players, and female sedentary individuals. Groups were age-homogeneous (F=0.772, p=0.512). Height differences between male and female groups are consistent with sex-based anthropometric variation at this developmental stage (males 1.76±0.07 m vs. females 1.63±0.06 m). All participants presented BMI values within the normal range (15.8–27.7 kg/m²). Total FMS scores below the recommended threshold of 14 points were observed in 53.6% of G2 and 47.8% of G4, compared to 7.1% of G1 and 12.0% of G3, indicating a higher prevalence of functional movement deficiency in sedentary groups. Anterior Y-Balance reach asymmetry exceeding 4 cm between limbs (a recognised indicator of functional imbalance) was present in 57.1% of G2, 56.0% of G3, and 43.5% of G4, compared to only 7.1% of G1. Table 3: Individual maturity offset values by participants. Statistic Male soccer players Male sedentary students Female soccer players Female sedentary students N 48 48 45 43 Mean MO (years) -0.04 -0.15 -6.25 -6.29 SD 0.87 0.77 0.47 0.48 Min MO -1.7 -1.64 -7.12 -7.22 Max MO 1.99 1.29 -5.36 -5.37 Pre-PHV (MO 1) 8 9 4 5 MO: Maturity offset; PHV: Peak height velocity; SD: Standard deviation; Pre-PHV: MO 1 year. Table 3 presented the individual maturity offset values of the participants by group. Male soccer players had a mean maturity offset of −0.04 ± 0.87 years, with values ranging from −1.70 to 1.99 years. Of these participants, 13 were classified as pre-PHV (MO 1), indicating that the majority of male soccer players were in the peri-PHV phase at the time of assessment. Male sedentary students had a mean maturity offset of −0.15 ± 0.77 years, ranging from −1.64 to 1.29 years, with 10 participants classified as pre-PHV, 29 as peri-PHV, and 9 as post-PHV, similarly reflecting a predominantly peri-PHV distribution. Female soccer players demonstrated a mean maturity offset of −6.25 ± 0.47 years, with values ranging from −7.12 to −5.36 years. Within this group, 24 participants were classified as pre-PHV, 17 as peri-PHV, and 4 as post-PHV, indicating that the majority of female soccer players had not yet reached their peak height velocity. Female sedentary students showed a comparable mean maturity offset of −6.29 ± 0.48 years, ranging from −7.22 to −5.37 years, with 22 participants in the pre-PHV category, 16 in the peri-PHV category, and 5 in the post-PHV category. Overall, male groups were predominantly in the peri-PHV phase, whereas female groups were predominantly in the pre-PHV phase, reflecting a markedly earlier stage of biological maturation in female participants relative to their male counterparts at the time of data collection. Table 4: Injury history and injury rates of the research group. Group Injury Presents No Injuries Totally Injury Ratio (%) Male Soccer Players 21 27 48 43.8 Male Sedentary Students 17 31 48 35.4 Female Soccer Players 17 28 45 37.8 Female Sedentary Students 9 34 43 20.9 The results regarding the study group's injury history and injury rates were presented in Table 4. Among male soccer players, 21 individuals had a history of injury, resulting in an injury rate of 43.8% among the total of 48 participants. Among sedentary male students, among sedentary male students, 17 participants had a history of injury, resulting in an injury rate of 35.4%. 17 female soccer players had a history of injury, resulting in an injury rate of 37.8% in this group. Among sedentary female students, the number of individuals with a history of disability was 9, and the disability rate was the lowest compared to other groups at 20.9%. Overall, it was determined that injury rates were higher in both male and female soccer player groups compared to sedentary groups, and that male soccer players had the highest injury rate among all groups. Table 5. Intergroup differences and multiple comparison values of the research group . Variable Group N Mean ± SD F p η² Multiple comparison (Post-hoc) Total FMS (score) G1 48 15.68±1.54 15.788 G2; G1 G4; G2 < G3 G2 G4 G2 48 12.93±2.29 G3 45 15.80±1.96 G4 43 13.22±2.11 Right Y-Balance Composite (%) G1 48 95.95±7.34 20.398 G2; G1 > G3 G1 > G4; G2 G4; G3 > G4 G2 48 92.02±10.66 G3 45 92.44±9.63 G4 43 77.41±7.58 Left Y-Balance Composite (%) G1 48 95.17±6.96 9.284 G2; G1 > G3 G1 > G4; G2 > G3 G2 > G4; G3 > G4 G2 48 91.94±11.80 G3 45 91.52±12.57 G4 43 80.50±9.16 Right Anterior (%) G1 48 77.46±6.70 32.627 G2; G1 > G3 G1 > G4; G2 > G3 G2 > G4; G3 > G4 G2 48 69.79±10.15 G3 45 62.04±8.86 G4 43 53.91±9.77 Right Posteromedial (%) G1 48 99.25±8.75 23.315 G2; G1 > G3 G1 > G4; G2 > G3 G2 > G4; G3 > G4 G2 48 95.75±12.13 G3 45 90.52±8.97 G4 43 76.61±10.60 Right Posterolateral (%) G1 48 101.00±6.84 35.574 G2; G1 > G3 G1 > G4; G2 > G3 G2 > G4; G3 > G4 G2 48 97.75±12.42 G3 45 86.88±11.31 G4 43 71.83±12.78 Left Anterior (%) G1 48 78.00±6.91 26.616 G2; G1 > G3 G1 > G4; G2 > G3 G2 > G4; G3 > G4 G2 48 67.79±9.97 G3 45 61.12±10.20 G4 43 55.30±11.14 Left Posteromedial (%) G1 48 98.14±7.48 15.086 <0.001* 0.312 G1 G3 G1 > G4; G2 > G3 G2 > G4; G3 > G4 G2 48 98.46±14.04 G3 45 89.84±11.15 G4 43 79.39±12.50 Left Posterolateral (%) G1 48 99.79±6.76 21.425 G2; G1 > G3 G1 > G4; G2 > G3 G2 > G4; G3 > G4 G2 48 96.32±12.91 G3 45 84.24±14.43 G4 43 73.13±17.26 Agility (sec) G1 48 16.46±1.46 43.729 <0.001* 0.567 G1 < G2; G1 < G3 G1 G3 G2 < G4; G3 G2; G1 > G3 G1 > G4; G2 < G3 G2 G4 G2 48 23.07±5.44 G3 45 26.62±3.46 G4 43 23.35±5.06 Coordination (sec) G1 48 12.96±1.21 48.065 <0.001* 0.590 G1 < G2; G1 < G3 G1 G3 G2 < G4; G3 < G4 G2 48 17.04±1.83 G3 45 14.90±1.59 G4 43 17.91±1.90 *p 0.01, medium > 0.06, large > 0.14); Post-hoc: Tukey HSD; >: Statistically significant in favour; >: In favour but statistically insignificant. Table 5 showed that there were significant differences in all variables of functional movement quality, dynamic balance, and biomotor performance in the intergroup comparisons. Group differences were assessed using one-way ANOVA followed by Tukey HSD post-hoc test. A significant difference was found between the groups in terms of FMS scores (F = 15.788; p < 0.001; η² = 0.321). Post-hoc analyses revealed that male soccer players had higher FMS scores than both male and female sedentary groups, while female soccer players achieved higher scores than both male and female sedentary groups. Significant differences were found between the groups in terms of Y-Balance Test composite scores in both the right (F = 20.398; p < 0.001; η² = 0.380) and left extremities (F = 9.284; p < 0.001; η² = 0.218). Male soccer players achieved higher composite scores compared to all other groups, while female soccer players reached higher values compared to the female sedentary group. When Y-Balance reach distances were examined, significant differences were observed between the groups in all directions for both extremities (p < 0.05). Male soccer players outperformed sedentary groups in most reach directions, while female soccer players surpassed sedentary individuals in several parameters. Notably, in the left posteromedial reach direction, female soccer players showed higher values compared to both male sedentary students and male soccer players. Significant differences were found between the groups in terms of agility performance (F = 43.729; p < 0.001; η² = 0.567). Male soccer players exhibited the best agility performance, followed by female soccer players, male sedentary students, and female sedentary students, respectively. Coordination performance also differed significantly between the groups (F = 48.065; p < 0.001; η² = 0.590). Flexibility values similarly showed significant differences between the groups (F = 4.944; p = 0.003; η² = 0.129), with soccer players demonstrating higher flexibility values than sedentary individuals. Table 6. 2x2 factorial ANCOVA values of activity level, gender, and maturity offset factors of the groups. Soccer group Sedentary group Male Group Female group Main effect of activity Main effect of gender Main effect of maturity Activity x Gender interaction Variables Mean Mean Mean Mean F p F p F p F p Total FMS (score) 15.74 13.06 14.3 14.56 19.94 0.001* 0.38 0.540 0.29 0.593 0.029 0.866 Right YBC (%) 94.29 85.43 93.98 85.24 33.65 0.000* 2.22 0.139 0.27 0.606 10.708 0.001* Left YBC (%) 93.45 86.78 93.55 86.24 13.55 0.001* 1.31 0.255 0.24 0.626 3.851 0.053 Right Anterior (%) 70.19 62.63 73.62 58.15 9.77 0.002* 0.19 0.663 0.41 0.523 0.999 0.320 Right PM (%) 95.13 87.12 97.5 83.85 21.96 0.000* 1.06 0.305 0.01 0.925 11.064 0.001* Right PL (%) 94.34 86.06 99.38 79.67 22.75 0.000* 6.67 0.011* 1.38 0.243 11.352 0.001* Left Anterior (%) 70.04 62.16 72.89 58.33 4.35 0.040* 0.39 0.532 0.02 0.876 0.100 0.752 Left PM (%) 94.23 89.86 98.3 84.83 9.85 0.002* 2.2 0.141 0.25 0.621 8.086 0.005* Left PL(%) 92.45 85.86 98.05 78.92 8.58 0.004* 2.94 0.089 0.44 0.508 3.925 0.050 Agility (sec) 17.78 21.7 18.66 20.91 34.51 0.001* 0.34 0.561 0.01 0.975 1.313 0.255 Flexibility (cm) 26.63 23.2 24.86 25.05 6.1 0.015* 0.09 0.762 0.07 0.786 0.013 0.911 Coord. (sec) 13.87 17.43 15.0 16.34 39.99 0.000* 0.39 0.533 0.10 0.758 2.721 0.102 *p<0.05; Right YBC: Right Y-Balance Composite; Left YBC: Left Y-Balance Composite; Right PM: Right Posteromedial; Right PL: Right Posterolateral; Left PM: Left Posteromedial; Left PL: Left Posterolateral; Coord.: Coordination. Whereas Table 5 presented one-way ANOVA results with post-hoc pairwise comparisons across all four groups (G1–G4), Table 6 reported the independent main effects of activity level, gender and maturity offset factors, and their interaction term from the 2x2 factorial ANCOVA, addressing analytically distinct research questions. Table 6 presented the 2x2 factorial ANCOVA results examining the main effects of activity level, gender, and maturity offset, as well as the Activity x Gender interaction, on all measured variables, with maturity offset included as a covariate. Activity level emerged as a significant main effect for all variables (p < 0.05). Soccer players demonstrated higher FMS scores (F = 19.94; p = 0.001), better Y-Balance composite scores in both the right (F = 33.65; p < 0.001) and left extremities (F = 13.55; p = 0.001), and superior reach distances across all directions compared to sedentary individuals. Soccer players also exhibited significantly better agility (F = 34.51; p = 0.001), coordination (F = 39.99; p < 0.001), and flexibility performance (F = 6.10; p = 0.015) than their sedentary counterparts. Gender was a significant main effect only for right posterolateral reach distance (F = 6.67; p = 0.011), with male participants achieving higher values than female participants. For all other variables, gender did not emerge as a significant main effect (p > 0.05). Maturity offset, included as a covariate, showed no significant main effect on any of the measured variables (p > 0.05 for all), suggesting that the observed differences in motor performance between groups were not primarily attributable to differential biological maturation. The Activity × Gender interaction was statistically significant for right Y-Balance composite score (F = 10.708; p = 0.001), right posteromedial reach distance (F = 11.064; p = 0.001), right posterolateral reach distance (F = 11.352; p = 0.001), and left posteromedial reach distance (F = 8.086; p = 0.005), indicating that the effect of activity level on these dynamic balance parameters differed between male and female participants. Specifically, the mean scores suggest that the activity-related advantage in these reach directions was more pronounced in male participants (soccer: 97.50 vs. sedentary: 87.12 for right posteromedial; 99.38 vs. 86.06 for right posterolateral) compared to female participants (90.52 vs. 83.85 and 86.88 vs. 71.83, respectively), indicating a greater magnitude of training-related adaptation in male soccer players for these particular balance parameters. For all remaining variables (including FMS score, left Y-Balance composite, all anterior reach distances, left posterolateral reach, agility, flexibility, and coordination) the Activity x Gender interaction was not statistically significant (p > 0.05), indicating that the effect of activity level on these variables was consistent across both sexes. Table 7. Correlation values between groups' FMS, Y-Balance scores, and biomotor performance capacities. V 1 2 3 4 5 6 7 8 9 10 11 2 0.375 *** 3 0.339 *** 0.898 *** 4 0.245 * 0.696 *** 0.644 *** 5 0.300 ** 0.758 *** 0.641 *** 0.604 *** 6 0.274 ** 0.788 *** 0.726 *** 0.685 *** 0.806 *** 7 0.241 * 0.690 *** 0.720 *** 0.913 *** 0.596 *** 0.676 *** 8 0.209 * 0.648 *** 0.739 *** 0.611 *** 0.768 *** 0.751 *** 0.609 *** 9 0.281 ** 0.719 *** 0.769 *** 0.672 *** 0.720 *** 0.898 *** 0.673 *** 0.697 *** 10 -0.385 *** -0.453 *** -0.393 *** -0.457 *** -0.376 *** -0.432 *** -0.426 *** -0.300 ** -0.429 *** 11 0.116 0.153 0.159 0.087 0.003 0.106 0.173 -0.014 0.110 -0.258 ** 12 -0.380 *** -0.367 *** -0.269 ** -0.369 *** -0.415 *** -0.401 *** -0.343 *** -0.249 * -0.374 *** 0.608 *** -0.302 ** Note: Lower triangle displays r values with significance indicators. *p<0.05; **p<0.01; p < 0.001 ***; V: Variables; 1: Total FMS (score); 2: Right Y-Balance Composite (%); 3: Left Composite (%); 4: Right Anterior (%); 5: Right Posteromedial (%); 6: Right Posterolateral (%); 7: Left Anterior (%): 8: Left Posteromedial (%); 9: Left Posterolateral (%): 10: Agility (sec); 11: Flexibility (cm); 12: Coordination (sec) Table 7 presented the Pearson correlation coefficients between total FMS score, Y-Balance Test composite and reach direction scores, and biomotor performance variables across all participants. Total FMS score showed significant positive correlations with all Y-Balance composite and reach direction scores (r = 0.209–0.375; p < 0.05), indicating that higher functional movement quality was associated with better dynamic balance performance. FMS score was also significantly and negatively correlated with both agility (r = −0.385; p < 0.001) and coordination (r = −0.380; p 0.05). Y-Balance composite and reach direction scores demonstrated strong positive intercorrelations with each other (r = 0.604–0.913; p < 0.001), confirming the internal consistency of the dynamic balance measures. All Y-Balance variables were significantly and negatively correlated with agility (r = −0.300 to −0.457; p < 0.01) and coordination (r = −0.249 to −0.415; p 0.05). Agility and coordination were significantly and positively correlated with each other (r = 0.608; p < 0.001), suggesting that participants who performed better in agility also tended to perform better in coordination tasks. Agility showed a significant negative correlation with flexibility (r = −0.258; p < 0.01), while coordination was negatively correlated with flexibility as well (r = −0.302; p < 0.01). DISCUSSION AND CONCLUSION The main objective of this study was to determine the functional movement quality, dynamic balance, and self-reported injury prevalence of 14-16-year-old soccer players and to compare these parameters with their sedentary peers. Our study findings showed that significant differences emerged in agility, functional strength performance, and dynamic balance variables depending on the activity level. However, gender was found to play a statistically significant role, albeit a secondary one, in some parameters. These performance-related differences were particularly evident in the hexagonal and agility tests, with high effect sizes favouring the groups of players. Consistent with the literature, regular and structured soccer training is reported to contribute to the development of soccer-specific skills such as balance, explosive power, and agility [32]. Therefore, considering that agility is a complex construct involving numerous components such as change-of-direction mechanics, neuromuscular coordination, acceleration, and deceleration, which are not limited solely to linear speed, the results obtained can be said to be consistent with the holistic movement demands required by soccer [33, 34]. In this context, it is thought that the increasing tempo and change of direction requirements in modern soccer may make the role of multi-component motor skills such as agility on performance increasingly prominent [33]. According to the Functional Movement Screening results, G1's total FMS score was found to be significantly higher compared to G2. This result, consistent with previous studies [33, 35], demonstrates that regularly continuous soccer training positively influences the quality of functional movement. The absence of a gender-based difference in total FMS scores suggests that gender-related differences may decrease in female and male athlete populations with similar training backgrounds. Martín-Moya R, et al. (2023) found no difference in FMS scores between male and female semi-professional soccer players. Similarly, another study [36] reported no significant difference in total FMS scores between elite female and male rugby players. In contrast, Anderson BE, et al. (2015) reporting higher total FMS scores in favour of males compared to female middle school athletes suggests that factors such as sample characteristics (level of sports participation/training history) and branch heterogeneity may have influenced the results in this study. These conflicting results may point to a need for a more comprehensive evaluation of FMS scores through comparative studies encompassing different sports. The literature reports that gender-related differences in performance and movement patterns can be reduced with targeted neuromuscular training applications [38]. In this context, the results of our study show that soccer players have higher FMS scores than their sedentary peers, and that regularly continuous soccer training positively affects functional movement quality without differences between genders. The lack of significant differences in self-reported injury prevalence between groups is consistent with current understanding that injuries have a multifactorial aetiology. The higher injury prevalence observed in soccer player groups can be explained by the effects of high-intensity training and competition (Table 4). Current models emphasise that sport injury and injury prevalence is not limited solely to physical capacity but is shaped by the interaction of numerous factors such as training load, recovery balance, game conditions, player-opponent interactions, and previous injury history [39,40]. In this context, evaluating injury frequency independently of performance level may allow for a more realistic interpretation of the results. The maturity offset analysis, conducted using the Mirwald et al. (2002) equations, revealed that male participants spanned all three maturity categories (pre-, peri-, and post-PHV), while all female participants were peri- or post-PHV. Importantly, maturity status did not differ significantly between soccer and sedentary groups within each sex, suggesting that the observed activity-level differences in FMS, Y-Balance, agility, and coordination scores are more likely attributable to training adaptation than to differential maturation. Nevertheless, the substantial sex difference in MO (female MO ≈ 1.79 years vs. male MO ≈ 0.02 years) indicates that males and females were at different pubertal stages, which must be considered when interpreting sex-based performance comparisons. In the present study, when MO was included as a covariate in the ANCOVA model, it did not reach statistical significance for the majority of outcome variables (p>0.05), indicating that the observed activity-level and sex-based differences in motor performance were not primarily attributable to differential biological maturation. This supports the interpretation that training adaptation was the primary driver of performance differences [26-28]. In this study, FMS and Y-Balance tests were used not only for injury prediction but also for evaluating functional movement quality and dynamic balance profiles. Although the literature reports that these tests alone have limited validity for predicting injury, they are stated to offer valuable information in identifying individual movement disorders and creating performance profiles [17, 22, 41-43]. The findings support the idea that interpreting these tests within multifactorial performance and risk models is more appropriate than using them in isolation with fixed threshold values [17, 44]. Y-Balance test results showed that the soccer player groups (G1 and G3) exhibited statistically superior dynamic balance performance compared to the sedentary groups (G2 and G4). The high effect sizes observed, particularly in right composite scores and posterolateral extension, suggest that soccer training has a strong influence on proprioceptive control and postural stability, and the significant results obtained in athletes' agility scores also support this. These results suggest the importance of hip stabilizers and trunk control in soccer performance. Evaluating Y-Balance test scores as percentages increases methodological power by reducing limb length-related biases [21]. Literature has shown that Y-Balance test asymmetries are associated with injury history, and that athletes with anterior or posteromedial asymmetry ≥4 cm have a higher reported injury prevalence [20, 45]. The absence of a significant difference in injury rates suggests that low left-right asymmetry values across all groups may be a contributing factor [45, 46]. The relationships between agility, dynamic balance, and functional movement measurements reveal that the performance components were not independent of each other. Relationships between dynamic balance scores as well as power, speed, and deflection performance showed that FMS and Y-Balance test results were significantly associated with these performance components [47]. The decrease in agility times as dynamic balance scores increase supports the decisive role of postural control in change-of-direction performance. Similarly, the negative correlation between total FMS scores and agility tests, and a positive correlation with balance scores, suggests that functional movement quality was central to the multi-performance construct [48, 49]. It was thought that numerous factors, such as sudden changes of direction, stopping and starting, and high-speed movement inherent in nature of soccer, may influence the development of neuromuscular coordination. The fact that gender showed a significant effect on some performance variables and an insignificant effect on others reflects the interaction of biological differences with training adaptations. While the superior performance of male athletes in tests requiring upper extremity strength and trunk stability can be explained by biological factors, a history of regular training has been shown to reduce gender-related differences in multi-component parameters such as functional movement quality and dynamic balance. Similarly, the literature reports that gender differences are minimized in athlete populations with high levels of training [38, 50]. Factors limiting the interpretation of these study findings include the cross-sectional design, cause-and-effect relationships, the retrospective collection of injury data, and the inability to control for variations in the rate of biological maturation during adolescence [51]. Most critically, although maturity offset (MO) was estimated using the Mirwald et al. (2002) equations and statistically controlled as a covariate, this approach provides an indirect estimation of biological maturation and may not fully reflect inter-individual variability. At ages 14–16, maturation directly influences all outcome variables through mechanisms including transient proprioceptive disruption during Peak Height Velocity (PHV), rapid limb elongation affecting balance normalisation, and neuromuscular reorganisation during the pubertal growth spurt [27, 28]. The substantial sex difference in maturity offset observed in this sample (females ≈ 1.79 years post-PHV vs. males ≈ 0.02 years near-PHV) means that sex-based performance comparisons should be interpreted with caution. In the present study, ANCOVA with maturity offset as covariate was applied; the non-significant covariate effect corroborates the training-adaptation interpretation. It should additionally be noted that the Mirwald et al. (2002), female regression equation has documented validity limitations when applied to girls aged 14–16, a period substantially beyond the calibration range of the original sample [52]. This represents an acknowledged methodological constraint, and future research should employ population-specific equations for female adolescents in this age range [26-28]. Additionally, injury data were collected retrospectively via self-report, which carries a risk of recall bias and may underestimate true injury prevalence. The school-based sampling frame, while ecologically valid, limits generalisability to community or elite youth sport populations. In conclusion, it was observed that regular and consistent football training can improve the functional movement quality, dynamic balance, and agility performance of young athletes. However, considering that these performance improvements are not directly related to a reduction in self-reported injury prevalence, it suggests that FMS and Y-Balance tests may be more appropriately used in field applications not only as prospective injury screening tools, but also for identifying individual movement deficiencies and preparing individualized training programs. This approach can enable coaches and field practitioners to create targeted functional training programs that support performance development. Furthermore, it is thought that this study can increase awareness of the positive and protective effects of physical activity on musculoskeletal health in young individuals. Abbreviations • ACL : Anterior Cruciate Ligament • ANCOVA : Covariance Analysis • ANOVA : Analysis of Variance • BMI : Body Mass Index • FMS : Functional Movement Screen • G1 : Male Soccer Players • G2 : Male Sedentary Students • G3 : Female Soccer Players • G4 : Female Sedentary Students • IP : Injury Prevalence • MO : Maturity Offset • PHV : Peak Height Velocity • SD : Standard Deviation Declarations Ethical Approval and Consent Forms The study design and methodology were reviewed and approved by the Uşak University Non-Interventional Clinical Research Ethics Committee (Decision No: 02, Date: 18/05/2023). All participants were given detailed information about the scope of the study, and their written consent was obtained through the "Informed Consent Form". The study was conducted in accordance with ethical standards regarding human trials and the 2013 revised version of the 1975 Helsinki Declaration. Since the research group consisted of individuals under 16 years of age, written informed consent was obtained from their parents or legal guardians. Consent for Publication Not applicable Data Availability The datasets used and analysed in this study are available from the corresponding author upon reasonable request. Protocol and Statistical Analysis Plan The study protocol and statistical analysis plan are not publicly available, but all relevant methodological details are provided in the manuscript. Competing interests The authors reported no conflicts of interest. Funding All resources used for this study were provided by the authors. Author Contributions Statement Author 1 : Conceived and designed the study, coordinated data collection, performed the statistical analyses, and drafted the manuscript. Author 2 : Contributed to the study design, assisted with data interpretation, and critically revised the manuscript for important intellectual content. 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J Exerc Rehabil . 2019;15(5):657–662. https://doi.org/10.12965/jer.1938422.211 Chimera NJ, Smith CA, Warren M. Injury history and FMS/Y-Balance. J Athl Train . 2015;50(5):475–485. https://doi.org/10.4085/1062-6050-49.6.02 Dorrel BS, Long T, Shaffer S, Myer GD. FMS as injury prediction tool. Sports Health . 2015;7(6):532–537. https://doi.org/10.1177/1941738115607445 Hewett TE, Myer GD, Ford KR. ACL injuries in female athletes. Am J Sports Med . 2006;34(2):299–311. https://doi.org/10.1177/0363546505284183 Read PJ, Oliver JL, De Ste Croix MB, et al. Landing kinematics in youth soccer. J Athl Train . 2018;53(4):372–378. https://doi.org/10.4085/1062-6050-493-16 Lauren B Sherar 1, Robert L Mirwald, Adam D G Baxter-Jones, et al. Prediction of adult height using maturity-based cumulative height velocity curves. J Pediatr . 2005;147(4):508-14. https://doi: 10.1016/j.jpeds.2005.04.041 Additional Declarations The authors declare no competing interests. Supplementary Files SupplamentaryFile1STROBEchecklist.docx Supplamentary File 1 (STROBE checklist) SupplementaryFile2.docx Supplamentary File 2 SupplementaryFile3.docx Supplamentary File 3 Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9348731","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619162127,"identity":"664ccb92-3072-4671-86d7-7009c6ea9744","order_by":0,"name":"Abdullah GÜLLÜ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYDACCQYDhgcFDAlAJuOBBww2QJoNivFpSTAAa2E4kMCQhtDCQ6SWw4S18M9u3iaRYGCXx8C/xuBAYtv5xPntxxIYPpQdZrCXPoDdkjvHyoBakosZJN6AtNxO3HAm7QDjjHOHGXj4ErBbcyPHDKiFObFB4gxUC0N6AzNvG1ALDpfJQ7TUw7ScS5zf/7yB+S8eLQYQLYcTG/h7QFoOJDbcSDvAzIhHi+GdY8UWCQbHE9sk2AoOJJxLNt5w41nCwZ5z6Tw8Z7BrkbvdvPHGh4rqxH7+wxsffCizk53fn2b44EeZtRx7D3YtcMAmkYDgHGDAE5MIwH+AsJpRMApGwSgYmQAA3edkhmgqaDcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2699-3069","institution":"Uşak University, Faculty of Sports Sciences, Uşak, Türkiye","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"GÜLLÜ","suffix":""},{"id":619162128,"identity":"c42923c1-7fd0-4656-8369-f77e1f91a0f4","order_by":1,"name":"Merve BOZ CİNCİ","email":"","orcid":"https://orcid.org/0000-0001-7356-0234","institution":"Uşak University, Graduate Education Institute, Uşak, Türkiye","correspondingAuthor":false,"prefix":"","firstName":"Merve","middleName":"BOZ","lastName":"CİNCİ","suffix":""}],"badges":[],"createdAt":"2026-04-07 18:54:24","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9348731/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9348731/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106959888,"identity":"46f793ff-1580-4bb6-9166-e0a170330ce6","added_by":"auto","created_at":"2026-04-15 09:16:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1595098,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9348731/v1/b418dda2-6ba0-4ee2-b73b-c7fef055825c.pdf"},{"id":106724642,"identity":"4740c18e-52bb-47a7-ac01-06de4e044955","added_by":"auto","created_at":"2026-04-12 18:28:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21666,"visible":true,"origin":"","legend":"\u003cp\u003eSupplamentary File 1 (STROBE checklist)\u003c/p\u003e","description":"","filename":"SupplamentaryFile1STROBEchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-9348731/v1/3e0d2e36946b14a53f09bfa3.docx"},{"id":106724646,"identity":"fd8d69b5-9c69-4e6a-807f-9ad1805b405d","added_by":"auto","created_at":"2026-04-12 18:28:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15177,"visible":true,"origin":"","legend":"\u003cp\u003eSupplamentary File 2\u003c/p\u003e","description":"","filename":"SupplementaryFile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9348731/v1/b146f8b987a4bf3ed7bf4f6c.docx"},{"id":106505163,"identity":"bfff1278-6852-4f4d-b367-cf8b4771e894","added_by":"auto","created_at":"2026-04-09 09:50:13","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15345,"visible":true,"origin":"","legend":"\u003cp\u003eSupplamentary File 3\u003c/p\u003e","description":"","filename":"SupplementaryFile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9348731/v1/19d5c5b7d674bf424aa1f07e.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eFunctional Movement Quality, Dynamic Balance and Injury Prevalence in Adolescent Soccer Players: Comparison with Sedentary Adolescents\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSports training programs, such as soccer training implemented in educational institutions, make significant contributions to the physical development and social adaptation processes of children during adolescence. The prevalence of sports-related injuries is more pronounced, particularly in the 14\u0026ndash;16 age group, a period when growth accelerates and motor control is being reshaped [1,2]. Self-reported injury prevalence shows significant gender differences during adolescence; female athletes exhibit different injury patterns compared to male athletes due to factors such as anatomical features, differences in neuromuscular control, and hormonal influences [3,4]. For example, anterior cruciate ligament (ACL) injuries are 2\u0026ndash;8 times more common in female athletes than in male athletes. Therefore, considering gender in methods for preventing and evaluating sport-related injuries is important [5]. However, it is also noted that gender-based movement differences and decreased performance may occur in athletes with similar regular training histories. In this context, school sports programs can offer an important opportunity for observation and diagnosis in terms of early identification of sport injury history that may arise during adolescence.\u003c/p\u003e \u003cp\u003eInjuries not only negatively affect athletic performance, but can also lead to long-term functional losses, decreased quality of life, and negative economic, sociological, and psychological consequences [6,7]. Biomotor components such as flexibility, coordination, agility, and balance are improvable traits and therefore form the basis of injury prevention programs, and deficiencies in these components are included in injury prevalence factors [8]. Young athletes are more likely to experience lower extremity injuries due to deficiencies in these motor skills [8,9]. In soccer, contact-based challenges, sudden changes of direction, repeated acceleration and deceleration cycles, and high-intensity training periods all contribute to an increase in injuries, particularly in the lower extremities [10\u0026ndash;12]. Therefore, identifying injury proneness in advance for young players on school soccer teams, and providing personalized protective equipment and environments, is crucial to prevent them from suffering sports injuries.\u003c/p\u003e \u003cp\u003eIt has been reported that there is a strong association between the increasing sedentary lifestyles of adolescents in recent years and musculoskeletal disorders [13], and that this effect is more pronounced especially in female adolescents with neck and back pain [14]. As is known, adolescents who do not regularly participate in physical activity experience significant decreases in dynamic balance, neuromuscular coordination, and functional movement quality, leading to increased injury prevalence rates and negatively impacting their quality of life.\u003c/p\u003e \u003cp\u003eFMS, although a widely used screening tool for assessing movement quality, has limited predictive power in terms of injury prediction [15,16]. However, recent studies show that FMS scores, supported by motor performance tests, can provide more accurate movement risk profiles [17]. For example, evaluating performance tests representing components such as flexibility, coordination, agility, and balance in conjunction with FMS can more comprehensively characterise functional movement deficiencies, especially in adolescent female athletes. Because recent studies have shown that assessments taking gender and biological differences into account are more successful in explaining functional performance of female athletes, especially [3,5]. Therefore, gender needs to be considered, especially in sport injury prevention approaches [5]. Because gender differences are pronounced during adolescence, women are more susceptible than men to the negative effects of sedentary behaviour [3,14]. Accordingly, it is believed that evaluating both sedentary and active groups separately for both genders in studies can lead to the development of more effective and goal-oriented strategies. In summary, comparing the motor risk profiles of sedentary individuals with those of soccer players is crucial for both sports\u0026rsquo; injury prevention and public health.\u003c/p\u003e \u003cp\u003eUnlike previous studies, this study had simultaneously examined five motor performance components (FMS, Y-Balance Test, agility, flexibility, and coordination) within a fully factorial 2x2 design encompassing activity level, gender, and biological maturity assessment in a school-based Turkish adolescent sample. The FMS and Y-Balance Test were selected for use in this study because both instruments have demonstrated validity and reliability in adolescent populations, offer standardised field-applicable protocols, and provide complementary information on functional movement quality and dynamic balance respectively [18\u0026ndash;21]. Their combined use within a multi-component battery can enable a more comprehensive movement profile than either tool alone or existing normative data can support their appropriateness for the 14\u0026ndash;16-year age range [17,22].\u003c/p\u003e \u003cp\u003eIn light of the information provided above, the aim of our study was to determine the injury prevalence profiles of male and female players aged 14\u0026ndash;16 who were actively involved in school soccer teams, as well as sedentary male and female individuals, based on functional movement quality and motor performance capacity, and to examined the interaction of activity level, gender, and biological maturity factors. Inclusion of sedentary adolescents in this study can allow for a comparative interpretation of functional and motor-based injury history profiles. This study can guide coaches and physical education teachers in structuring injury prevention training programs specific to adolescence and gender. In this respect, this study has the potential to offer generalizable implications for school sports and youth sports programs in different countries.\u003c/p\u003e \u003cp\u003eWhat this study adds: To our knowledge, this is the first school-based study in T\u0026uuml;rkiye to simultaneously profile five motor performance components (FMS, Y-Balance Test, agility, flexibility, and coordination) within a fully factorial 2x2 design that accounts for activity level, sex, and biological maturity in the same sample. The findings can provide practical benchmarks for coaches, physical education teachers, and physiotherapists working in school sport settings: specifically, that activity level \u0026ndash;not biological maturity or sex\u0026ndash; is the dominant determinant of functional movement quality and dynamic balance in adolescents, and that FMS and Y-Balance assessments can serve as useful tools for identifying movement deficiencies and designing individualised training programmes, rather than for prospective injury prediction alone.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis study was designed and reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational cross-sectional studies [23]. The completed STROBE checklist is provided as Supplementary File 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included a total of 184 participants (48 male soccer players, 45 female soccer players, 48 sedentary male students, and 43 sedentary female students) aged 14-16 years old, studying at high schools affiliated with the Ministry of National Education in the city centre of Uşak. The participants were divided into four different groups based on activity level (soccer player/sedentary) and gender (male/female) variables. These were the male soccer player group (G1), the male sedentary group (G2), the female soccer player group (G3), and the female sedentary group (G4), respectively. The soccer groups (G1 and G3) consisted of actively licensed athletes who were part of the school soccer team and trained regularly at least 4 days a week for at least 2 hours per training session. The sedentary groups (G2 and G4) consisted of individuals who had not regularly participated in exercise or sports activities in the past six months and did not hold a license in any sport. All participants were selected based on the following criteria: no active musculoskeletal injury that could affect performance during measurements, no history of surgery within the last six months, no acute or chronic illness, or no regular medication use. Subjects who did not meet these criteria were excluded from the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Voluntary Participation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to this study, ethical approval was obtained from the Uşak University Non-Interventional Clinical Research Ethics Committee (Date: 18/05/2023, Decision No: 02). All participants and their parents or legal guardians were informed verbally, in writing, and visually about the purpose, scope, and tests to be performed. Since the research group consisted of individuals under 16 years of age, written informed consent was obtained from their parents or legal guardians. Verbal assent was also obtained from all participants. All procedures were conducted in accordance with the principles of the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size of study was calculated using Power analysis (G*Power 3.1) based on average effect sizes from similar studies [24] and a minimum of 42 participants was found required for each group. Since the study had a 2x2 factorial design [gender (female/male) x activity status (athlete/sedentary)], power calculations were performed based on two-way ANOVA (2x2 factorial ANOVA; effect size f=0.25; power 80%; p\u0026lt;0.05). To account for potential data loss (10-15% dropout rate) and measurement errors, groups were formed with a minimum of 43 and a maximum of 48 participants. Small variations in group sizes resulted from the participation criteria and the voluntary sampling process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApplication Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll measurements for the research group were performed over a week and at the same time each day by trainers and physiotherapists with at least 5 years of experience in the field. This minimized measurement errors and the effects of biological rhythms and intraday performance variables. Prior to the tests, necessary explanations about the test content were provided, and a standard 10-minute warm-up program was implemented. All tests were ranked from lowest to highest according to performance intensity levels. This minimized potential performance degradation due to fatigue. Passive recovery periods were applied between tests to allow sufficient time for participants to prepare for the second load.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ages of the research group were determined according to their national identity card (chronological age) information. Participants\u0026rsquo; height was measured in meters using a stadiometer (Harpenden Holtain, UK) with an accuracy of \u0026plusmn;0.01 cm, with bare feet together and in an upright standing position. Sitting height was measured using the same stadiometer with the participant seated on a flat surface, maintaining an upright posture; leg length was derived as the difference between standing height and sitting height. Body weight was measured using a digital scale (Seca 877 GmbH, Hamburg, Germany). Body mass index (BMI) was calculated using the formula [body weight (kg) / height (m\u0026sup2;)] [25].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiological Maturity Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBiological maturity status was estimated for all participants using the maturity offset (MO) method described by Mirwald et al. (2002). MO reflects the number of years before or after Peak Height Velocity (PHV) and is calculated from five anthropometric variables: standing height, sitting height, leg length (standing height minus sitting height), body mass, and chronological age \u0026ndash;all of which were routinely collected during the measurement protocol of the present study. Sex-specific regression equations were applied as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFor males:\u0026nbsp;\u003c/em\u003eMO = \u0026minus; 9.236 + 0.0002708 \u0026times; (leg length x sitting height) \u0026minus; 0.001663 x (age x leg length) + 0.007216 x (age x sitting height) + 0.02292 x (body mass / standing height x 100)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFor females:\u0026nbsp;\u003c/em\u003eMO = \u0026minus; 9.376 + 0.0001882 x (leg length x sitting height) + 0.0022 x (age x leg length) + 0.005841 x (age x sitting height) \u0026minus; 0.002658 x (age x body mass) + 0.07693 x (body mass / standing height x 100)\u003c/p\u003e\n\u003cp\u003eParticipants were classified into three maturity categories based on their MO value: pre-PHV (MO \u0026lt; \u0026minus;1 year), peri-PHV (\u0026minus;1 \u0026le; MO \u0026le; 1 year), and post-PHV (MO \u0026gt; 1 year) [27,28]. Maturity offset was calculated and reported descriptively by group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional and Biomotor Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study group\u0026apos;s susceptibility to injury was determined using the FMS test, and their biomotor characteristics were assessed using Y-balance, flexibility, Illinois agility, and hexagonal coordination tests, respectively. Standard application protocols described in the relevant literature were applied for all measurements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional and Biomotor Performance Assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunctional Movement Screen (FMS)\u003c/em\u003e: The study group was scored on 7 basic movements (deep squat, step over obstacle, linear forward lunge, shoulder mobility, active straight leg raises, trunk stability push-up, and rotational stability) using FMS measurements. Each movement was evaluated according to standard protocols with a score between 0 and 3, and the total score was calculated between 0 and 21 points (X=0-21), with X\u0026lt;14 points considered as high risk of injury [18,19]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eY-Balance Test\u003c/em\u003e: This test was performed using a specialized Y-Balance Test kit (a graded three-arm platform) with sliding measurement blocks. The test was administered three times to assess lower extremity dynamic balance performance. Maximum best reach distances obtained from anterior, posteromedial, and posterolateral directions were measured for both extremities (excluding invalid trials) and used in the analysis. The obtained raw values were normalized to leg length, and percentage scores were calculated. The composite score was determined using the formula: [(Anterior + Posteromedial + Posterolateral) / (3 x Leg length) x 100]. An anterior difference of \u0026gt;4 cm between the right and left was considered an indicator of functional asymmetry [20,21]. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFlexibility Test\u003c/em\u003e: To determine the flexibility of the lower extremity and posterior trunk muscle groups, the sit-and-reach test was performed three times using a flexibility bench. The best measurement obtained was recorded in centimetres for use in the analysis [25]. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAgility Test\u003c/em\u003e: Agility performance was assessed twice using the Illinois Agility Test. The research group was asked to complete a course containing different acceleration components in the shortest possible time, and the best test time was recorded in seconds [29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCoordination Test\u003c/em\u003e: To assess coordination and multifaceted movement skills, the Hexagonal Coordination Test was administered twice, and the shortest completion time was recorded in seconds [30]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInjury Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInjury history data for the research group were collected through structured face-to-face interviews using a standardized interview form developed by the researchers (Supplementary File 1) [31]. Participants were asked standardized questions regarding injuries experienced during the previous 12 months. In this study, an injury was defined as a musculoskeletal injury that prevented participation in training or competition for at least one day; non-sport injuries were excluded from the analysis. Recurrent injuries in the same anatomical region were counted as separate events. Injuries were classified according to injury type (acute or chronic), anatomical location (lower extremity, upper extremity, or trunk), and severity based on the number of days lost from training or competition (Supplementary File 2) [31]; however, severity categories were used for descriptive purposes only and were not included as variables in the inferential analyses. The information obtained during the interviews was based on participant self-reports and was cross-checked with school medical records. It should be noted that this study assessed 12-month retrospective injury history (self-reported injury prevalence) and did not prospectively track injury incidence; accordingly, FMS and Y-Balance Test scores cannot be interpreted as predictors of future injury in this sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics of the data obtained from this research were calculated, including arithmetic mean and standard deviation. The normality of the data distribution was checked using the Shapiro-Wilk test, and the homogeneity of variances was checked using the Levene test (p\u0026gt;0.05), and parametric tests were applied. Differences between groups were determined using one-way ANOVA, and effect sizes were calculated using eta-squared (\u0026eta;\u0026sup2;). For variables with a significant difference, Tukey HSD post-hoc comparisons were used to determine which group the difference originated from. A two-way ANCOVA with a 2x2 factorial design was used to examine the effects of activity level (soccer player/sedentary), gender (male/female), and biological maturation factors on the motor performance capacity of the groups. This allowed for the interpretation of activity-level and sex-based comparisons after statistically adjusting for differential pubertal timing between male and female participants. The relationships between the groups\u0026apos; FMS, Y-Balance scores, and biomotor performance capacities were examined using the Pearson correlation coefficient (r). All statistical analyses were evaluated using the SPSS 25.0 statistical package program (IBM Corp., USA) at a significant level of p\u0026lt;0.05.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable 1 presented the demographic information of the data obtained from the research group, including mean and standard deviation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Demographic variables of the research group.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Soccer Players (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Sedentary Students (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Soccer Players (n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Sedentary Students (n=43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eAge (yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e15.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e15.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e14.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e15.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eHeight (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e66.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e9.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e63.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e54.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e58.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e21.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e20.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e20.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e22.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSD: Standard deviation.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Descriptive statistics values of the research group.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Soccer Players (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Sedentary Students (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Soccer Players (n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Sedentary Students (n=43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eTotal FMS Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e15.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e12.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e15.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e13.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eRight Y-Balance Composite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e95.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e92.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e92.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e9.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e77.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLeft Y-Balance Composite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e95.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e91.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e91.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e12.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e80.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eRight Anterior (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e77.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e69.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e10.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e62.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e53.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e9.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eRight Posteromedial (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e99.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e95.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e90.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e76.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eRight Posterolateral (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e101.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e97.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e86.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e71.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e12.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLeft Anterior (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e78.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e67.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e9.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e61.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e10.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e55.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLeft Posteromedial (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e98.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e98.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e14.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e89.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e11.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e79.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLeft Posterolateral (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e99.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e96.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e12.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e84.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e14.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e73.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e17.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eAgility (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e16.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e20.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e19.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e22.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eFlexibility (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e26.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e23.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e26.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e23.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eCoordination (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e12.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e17.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e14.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e17.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSD: Standard deviation.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presented the descriptive statistical analyses of the data obtained from the research group, including mean and standard deviation. Group classifications included male soccer players, male sedentary individuals, female soccer players, and female sedentary individuals. Groups were age-homogeneous (F=0.772, p=0.512). Height differences between male and female groups are consistent with sex-based anthropometric variation at this developmental stage (males 1.76\u0026plusmn;0.07 m vs. females 1.63\u0026plusmn;0.06 m). All participants presented BMI values within the normal range (15.8\u0026ndash;27.7 kg/m\u0026sup2;). Total FMS scores below the recommended threshold of 14 points were observed in 53.6% of G2 and 47.8% of G4, compared to 7.1% of G1 and 12.0% of G3, indicating a higher prevalence of functional movement deficiency in sedentary groups. Anterior Y-Balance reach asymmetry exceeding 4 cm between limbs (a recognised indicator of functional imbalance) was present in 57.1% of G2, 56.0% of G3, and 43.5% of G4, compared to only 7.1% of G1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Individual maturity offset values by participants.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale soccer players\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale sedentary students\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale soccer players\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale sedentary students\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eMean MO (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eMin MO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-7.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eMax MO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003ePre-PHV (MO \u0026lt; \u0026minus;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003ePeri-PHV (\u0026minus;1 \u0026le; MO \u0026le; 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003ePost-PHV (MO \u0026gt; 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMO: Maturity offset; PHV: Peak height velocity; SD: Standard deviation; Pre-PHV: MO \u0026lt; \u0026minus;1 year; Peri-PHV: \u0026minus;1 \u0026le; MO \u0026le; 1 year; Post-PHV: MO \u0026gt; 1 year.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 presented the individual maturity offset values of the participants by group. Male soccer players had a mean maturity offset of \u0026minus;0.04 \u0026plusmn; 0.87 years, with values ranging from \u0026minus;1.70 to 1.99 years. Of these participants, 13 were classified as pre-PHV (MO \u0026lt; \u0026minus;1), 27 as peri-PHV (\u0026minus;1 \u0026le; MO \u0026le; 1), and 8 as post-PHV (MO \u0026gt; 1), indicating that the majority of male soccer players were in the peri-PHV phase at the time of assessment. Male sedentary students had a mean maturity offset of \u0026minus;0.15 \u0026plusmn; 0.77 years, ranging from \u0026minus;1.64 to 1.29 years, with 10 participants classified as pre-PHV, 29 as peri-PHV, and 9 as post-PHV, similarly reflecting a predominantly peri-PHV distribution. Female soccer players demonstrated a mean maturity offset of \u0026minus;6.25 \u0026plusmn; 0.47 years, with values ranging from \u0026minus;7.12 to \u0026minus;5.36 years. Within this group, 24 participants were classified as pre-PHV, 17 as peri-PHV, and 4 as post-PHV, indicating that the majority of female soccer players had not yet reached their peak height velocity. Female sedentary students showed a comparable mean maturity offset of \u0026minus;6.29 \u0026plusmn; 0.48 years, ranging from \u0026minus;7.22 to \u0026minus;5.37 years, with 22 participants in the pre-PHV category, 16 in the peri-PHV category, and 5 in the post-PHV category. Overall, male groups were predominantly in the peri-PHV phase, whereas female groups were predominantly in the pre-PHV phase, reflecting a markedly earlier stage of biological maturation in female participants relative to their male counterparts at the time of data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Injury history and injury rates of the research group.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjury Presents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Injuries\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotally\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjury Ratio (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 198px;\"\u003e\n \u003cp\u003eMale Soccer Players\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 99px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e43.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 198px;\"\u003e\n \u003cp\u003eMale Sedentary Students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 99px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 198px;\"\u003e\n \u003cp\u003eFemale Soccer Players\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 99px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 198px;\"\u003e\n \u003cp\u003eFemale Sedentary Students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 99px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results regarding the study group\u0026apos;s injury history and injury rates were presented in Table 4. Among male soccer players, 21 individuals had a history of injury, resulting in an injury rate of 43.8% among the total of 48 participants. Among sedentary male students, among sedentary male students, 17 participants had a history of injury, resulting in an injury rate of 35.4%. 17 female soccer players had a history of injury, resulting in an injury rate of 37.8% in this group. Among sedentary female students, the number of individuals with a history of disability was 9, and the disability rate was the lowest compared to other groups at 20.9%. Overall, it was determined that injury rates were higher in both male and female soccer player groups compared to sedentary groups, and that male soccer players had the highest injury rate among all groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Intergroup differences and multiple comparison values of the research group\u003c/strong\u003e.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026eta;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultiple comparison\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Post-hoc)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eTotal FMS (score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.68\u0026plusmn;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026gt; G2; G1 \u003cu\u003e\u0026lt;\u003c/u\u003e G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u0026lt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u003cu\u003e\u0026lt;\u003c/u\u003e G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.93\u0026plusmn;2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.80\u0026plusmn;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.22\u0026plusmn;2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eRight Y-Balance Composite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e95.95\u0026plusmn;7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026gt; G2; G1 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u003cu\u003e\u0026lt;\u003c/u\u003e G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026gt; G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e92.02\u0026plusmn;10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e92.44\u0026plusmn;9.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e77.41\u0026plusmn;7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eLeft Y-Balance Composite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e95.17\u0026plusmn;6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026gt; G2; G1 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026gt; G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e91.94\u0026plusmn;11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e91.52\u0026plusmn;12.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e80.50\u0026plusmn;9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eRight Anterior (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e77.46\u0026plusmn;6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e32.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026gt; G2; G1 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026gt; G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e69.79\u0026plusmn;10.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e62.04\u0026plusmn;8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e53.91\u0026plusmn;9.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eRight Posteromedial (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e99.25\u0026plusmn;8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026gt; G2; G1 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026gt; G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e95.75\u0026plusmn;12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e90.52\u0026plusmn;8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e76.61\u0026plusmn;10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eRight Posterolateral (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e101.00\u0026plusmn;6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e35.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026gt; G2; G1 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026gt; G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e97.75\u0026plusmn;12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e86.88\u0026plusmn;11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e71.83\u0026plusmn;12.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eLeft Anterior (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e78.00\u0026plusmn;6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026gt; G2; G1 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026gt; G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e67.79\u0026plusmn;9.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e61.12\u0026plusmn;10.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e55.30\u0026plusmn;11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eLeft Posteromedial (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e98.14\u0026plusmn;7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026lt; G2; G1 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026gt; G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e98.46\u0026plusmn;14.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e89.84\u0026plusmn;11.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.39\u0026plusmn;12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eLeft Posterolateral (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e99.79\u0026plusmn;6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026gt; G2; G1 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026gt; G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e96.32\u0026plusmn;12.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e84.24\u0026plusmn;14.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e73.13\u0026plusmn;17.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eAgility (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e16.46\u0026plusmn;1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e43.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026lt; G2; G1 \u0026lt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026lt; G4; G2 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026lt; G4; G3 \u0026lt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e20.85\u0026plusmn;1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e19.25\u0026plusmn;2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e22.73\u0026plusmn;2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFlexibility (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e26.64\u0026plusmn;3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026gt; G2; G1 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026gt; G4; G2 \u0026lt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026lt; G4; G3 \u0026gt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e23.07\u0026plusmn;5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e26.62\u0026plusmn;3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e23.35\u0026plusmn;5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 101px;\"\u003e\n \u003cp\u003eCoordination (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.96\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e48.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 196px;\"\u003e\n \u003cp\u003eG1 \u0026lt; G2; G1 \u0026lt; G3\u003c/p\u003e\n \u003cp\u003eG1 \u0026lt; G4; G2 \u0026gt; G3\u003c/p\u003e\n \u003cp\u003eG2 \u0026lt; G4; G3 \u0026lt; G4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e17.04\u0026plusmn;1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e14.90\u0026plusmn;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e17.91\u0026plusmn;1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*p\u0026lt;0.05; G1: Male soccer players; G2: Male sedentary students; G3: Female soccer players; G4: Female sedentary students; SD: Standard deviation; F: ANOVA F-statistic; \u0026eta;\u0026sup2;: Eta-squared (small\u003cu\u003e\u0026gt;\u003c/u\u003e0.01, medium\u003cu\u003e\u0026gt;\u003c/u\u003e0.06, large\u003cu\u003e\u0026gt;\u003c/u\u003e0.14); Post-hoc: Tukey HSD; \u0026gt;: Statistically significant in favour; \u0026gt;: In favour but statistically insignificant.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 showed that there were significant differences in all variables of functional movement quality, dynamic balance, and biomotor performance in the intergroup comparisons. Group differences were assessed using one-way ANOVA followed by Tukey HSD post-hoc test. A significant difference was found between the groups in terms of FMS scores (F = 15.788; p \u0026lt; 0.001; \u0026eta;\u0026sup2; = 0.321). Post-hoc analyses revealed that male soccer players had higher FMS scores than both male and female sedentary groups, while female soccer players achieved higher scores than both male and female sedentary groups. Significant differences were found between the groups in terms of Y-Balance Test composite scores in both the right (F = 20.398; p \u0026lt; 0.001; \u0026eta;\u0026sup2; = 0.380) and left extremities (F = 9.284; p \u0026lt; 0.001; \u0026eta;\u0026sup2; = 0.218). Male soccer players achieved higher composite scores compared to all other groups, while female soccer players reached higher values compared to the female sedentary group. When Y-Balance reach distances were examined, significant differences were observed between the groups in all directions for both extremities (p \u0026lt; 0.05). Male soccer players outperformed sedentary groups in most reach directions, while female soccer players surpassed sedentary individuals in several parameters. Notably, in the left posteromedial reach direction, female soccer players showed higher values compared to both male sedentary students and male soccer players. Significant differences were found between the groups in terms of agility performance (F = 43.729; p \u0026lt; 0.001; \u0026eta;\u0026sup2; = 0.567). Male soccer players exhibited the best agility performance, followed by female soccer players, male sedentary students, and female sedentary students, respectively. Coordination performance also differed significantly between the groups (F = 48.065; p \u0026lt; 0.001; \u0026eta;\u0026sup2; = 0.590). Flexibility values similarly showed significant differences between the groups (F = 4.944; p = 0.003; \u0026eta;\u0026sup2; = 0.129), with soccer players demonstrating higher flexibility values than sedentary individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. 2x2 factorial ANCOVA values of activity level, gender, and maturity offset factors of the groups.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoccer group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSedentary group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain effect of activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain effect of\u003c/strong\u003e \u003cstrong\u003egender\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain effect of maturity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity x Gender interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eTotal FMS (score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e15.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e13.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e19.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRight\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYBC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e94.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e85.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e93.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e85.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e33.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e10.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eLeft YBC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e93.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e86.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e93.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e86.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRight Anterior (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e70.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e62.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e73.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e58.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e9.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRight PM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e95.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e87.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e83.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e21.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e11.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRight PL (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e94.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e86.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e99.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e79.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e22.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e11.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eLeft Anterior (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e70.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e62.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e72.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e58.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eLeft PM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e94.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e89.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e98.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e84.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e9.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e8.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eLeft PL(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e92.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e85.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e98.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e78.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e8.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAgility (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e17.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e18.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e34.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eFlexibility (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e26.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e24.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e25.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003eCoord. (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 66px;\"\u003e\n \u003cp\u003e17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e39.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*p\u0026lt;0.05; Right YBC: Right Y-Balance Composite; Left YBC: Left Y-Balance Composite; Right PM: Right Posteromedial; Right PL: Right Posterolateral; Left PM: Left Posteromedial;\u003c/em\u003e \u003cem\u003eLeft PL: Left Posterolateral;\u003c/em\u003e \u003cem\u003eCoord.: Coordination.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhereas Table 5 presented one-way ANOVA results with post-hoc pairwise comparisons across all four groups (G1\u0026ndash;G4), Table 6 reported the independent main effects of activity level, gender and maturity offset factors, and their interaction term from the 2x2 factorial ANCOVA, addressing analytically distinct research questions.\u003c/p\u003e\n\u003cp\u003eTable 6 presented the 2x2 factorial ANCOVA results examining the main effects of activity level, gender, and maturity offset, as well as the Activity x Gender interaction, on all measured variables, with maturity offset included as a covariate. Activity level emerged as a significant main effect for all variables (p \u0026lt; 0.05). Soccer players demonstrated higher FMS scores (F = 19.94; p = 0.001), better Y-Balance composite scores in both the right (F = 33.65; p \u0026lt; 0.001) and left extremities (F = 13.55; p = 0.001), and superior reach distances across all directions compared to sedentary individuals. Soccer players also exhibited significantly better agility (F = 34.51; p = 0.001), coordination (F = 39.99; p \u0026lt; 0.001), and flexibility performance (F = 6.10; p = 0.015) than their sedentary counterparts. Gender was a significant main effect only for right posterolateral reach distance (F = 6.67; p = 0.011), with male participants achieving higher values than female participants. For all other variables, gender did not emerge as a significant main effect (p \u0026gt; 0.05). Maturity offset, included as a covariate, showed no significant main effect on any of the measured variables (p \u0026gt; 0.05 for all), suggesting that the observed differences in motor performance between groups were not primarily attributable to differential biological maturation. The Activity \u0026times; Gender interaction was statistically significant for right Y-Balance composite score (F = 10.708; p = 0.001), right posteromedial reach distance (F = 11.064; p = 0.001), right posterolateral reach distance (F = 11.352; p = 0.001), and left posteromedial reach distance (F = 8.086; p = 0.005), indicating that the effect of activity level on these dynamic balance parameters differed between male and female participants. Specifically, the mean scores suggest that the activity-related advantage in these reach directions was more pronounced in male participants (soccer: 97.50 vs. sedentary: 87.12 for right posteromedial; 99.38 vs. 86.06 for right posterolateral) compared to female participants (90.52 vs. 83.85 and 86.88 vs. 71.83, respectively), indicating a greater magnitude of training-related adaptation in male soccer players for these particular balance parameters. For all remaining variables (including FMS score, left Y-Balance composite, all anterior reach distances, left posterolateral reach, agility, flexibility, and coordination) the Activity x Gender interaction was not statistically significant (p \u0026gt; 0.05), indicating that the effect of activity level on these variables was consistent across both sexes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Correlation values between groups\u0026apos; FMS, Y-Balance scores, and biomotor performance capacities.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.375 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.339 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.898 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.245 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.696 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.644 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.300 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.758 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.641 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.604 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.274 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.788 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.726 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.685 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.806 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.241 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.690 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.720 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.913 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.596 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.676 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.209 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.648 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.739 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.611 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.768 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.751 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.609 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.281 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.719 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.769 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.672 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.720 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.898 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.673 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.697 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.385 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.453 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.393 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.457 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.376 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.432 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.426 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.300 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.429 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.258 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.380 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.367 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.269 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.369 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.415 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.401 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.343 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.249 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.374 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.608 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.302 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: Lower triangle displays r values with significance indicators.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*p\u0026lt;0.05; **p\u0026lt;0.01; p \u0026lt; 0.001 ***; V: Variables; 1: Total FMS (score); 2: Right Y-Balance Composite (%); 3: Left Composite (%); 4: Right Anterior (%); 5: Right Posteromedial (%); 6: Right Posterolateral (%); 7: Left Anterior (%): 8: Left Posteromedial (%); 9: Left Posterolateral (%): 10: Agility (sec); 11: Flexibility (cm); 12: Coordination (sec)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 7 presented the Pearson correlation coefficients between total FMS score, Y-Balance Test composite and reach direction scores, and biomotor performance variables across all participants. Total FMS score showed significant positive correlations with all Y-Balance composite and reach direction scores (r = 0.209\u0026ndash;0.375; p \u0026lt; 0.05), indicating that higher functional movement quality was associated with better dynamic balance performance. FMS score was also significantly and negatively correlated with both agility (r = \u0026minus;0.385; p \u0026lt; 0.001) and coordination (r = \u0026minus;0.380; p \u0026lt; 0.001), suggesting that higher FMS scores were associated with faster performance times. No significant correlation was observed between FMS score and flexibility (r = 0.116; p \u0026gt; 0.05). Y-Balance composite and reach direction scores demonstrated strong positive intercorrelations with each other (r = 0.604\u0026ndash;0.913; p \u0026lt; 0.001), confirming the internal consistency of the dynamic balance measures. All Y-Balance variables were significantly and negatively correlated with agility (r = \u0026minus;0.300 to \u0026minus;0.457; p \u0026lt; 0.01) and coordination (r = \u0026minus;0.249 to \u0026minus;0.415; p \u0026lt; 0.05), indicating that better dynamic balance was associated with faster agility and coordination performance. Flexibility showed no significant correlation with any Y-Balance variable (p \u0026gt; 0.05). Agility and coordination were significantly and positively correlated with each other (r = 0.608; p \u0026lt; 0.001), suggesting that participants who performed better in agility also tended to perform better in coordination tasks. Agility showed a significant negative correlation with flexibility (r = \u0026minus;0.258; p \u0026lt; 0.01), while coordination was negatively correlated with flexibility as well (r = \u0026minus;0.302; p \u0026lt; 0.01).\u003c/p\u003e"},{"header":"DISCUSSION AND CONCLUSION","content":"\u003cp\u003eThe main objective of this study was to determine the functional movement quality, dynamic balance, and self-reported injury prevalence of 14-16-year-old soccer players and to compare these parameters with their sedentary peers. Our study findings showed that significant differences emerged in agility, functional strength performance, and dynamic balance variables depending on the activity level. However, gender was found to play a statistically significant role, albeit a secondary one, in some parameters. These performance-related differences were particularly evident in the hexagonal and agility tests, with high effect sizes favouring the groups of players. Consistent with the literature, regular and structured soccer training is reported to contribute to the development of soccer-specific skills such as balance, explosive power, and agility [32]. Therefore, considering that agility is a complex construct involving numerous components such as change-of-direction mechanics, neuromuscular coordination, acceleration, and deceleration, which are not limited solely to linear speed, the results obtained can be said to be consistent with the holistic movement demands required by soccer [33, 34]. In this context, it is thought that the increasing tempo and change of direction requirements in modern soccer may make the role of multi-component motor skills such as agility on performance increasingly prominent [33].\u003c/p\u003e\n\u003cp\u003eAccording to the Functional Movement Screening results, G1\u0026apos;s total FMS score was found to be significantly higher compared to G2. This result, consistent with previous studies [33, 35], demonstrates that regularly continuous soccer training positively influences the quality of functional movement. The absence of a gender-based difference in total FMS scores suggests that gender-related differences may decrease in female and male athlete populations with similar training backgrounds. Mart\u0026iacute;n-Moya R, et al. \u0026nbsp;(2023) found no difference in FMS scores between male and female semi-professional soccer players. Similarly, another study [36] reported no significant difference in total FMS scores between elite female and male rugby players. In contrast,\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Anderson BE, et al. \u0026nbsp;(2015) reporting higher total FMS scores in favour of males compared to female middle school athletes suggests that factors such as sample characteristics (level of sports participation/training history) and branch heterogeneity may have influenced the results in this study. These conflicting results may point to a need for a more comprehensive evaluation of FMS scores through comparative studies encompassing different sports. The literature reports that gender-related differences in performance and movement patterns can be reduced with targeted neuromuscular training applications [38]. In this context, the results of our study show that soccer players have higher FMS scores than their sedentary peers, and that regularly continuous soccer training positively affects functional movement quality without differences between genders.\u003c/p\u003e\n\u003cp\u003eThe lack of significant differences in self-reported injury prevalence between groups is consistent with current understanding that injuries have a multifactorial aetiology. The higher injury prevalence observed in soccer player groups can be explained by the effects of high-intensity training and competition (Table 4). Current models emphasise that sport injury and injury prevalence is not limited solely to physical capacity but is shaped by the interaction of numerous factors such as training load, recovery balance, game conditions, player-opponent interactions, and previous injury history [39,40]. In this context, evaluating injury frequency independently of performance level may allow for a more realistic interpretation of the results.\u003c/p\u003e\n\u003cp\u003eThe maturity offset analysis, conducted using the Mirwald et al. (2002) equations, revealed that male participants spanned all three maturity categories (pre-, peri-, and post-PHV), while all female participants were peri- or post-PHV. Importantly, maturity status did not differ significantly between soccer and sedentary groups within each sex, suggesting that the observed activity-level differences in FMS, Y-Balance, agility, and coordination scores are more likely attributable to training adaptation than to differential maturation. Nevertheless, the substantial sex difference in MO (female MO \u0026asymp; 1.79 years vs. male MO \u0026asymp; 0.02 years) indicates that males and females were at different pubertal stages, which must be considered when interpreting sex-based performance comparisons. In the present study, when MO was included as a covariate in the ANCOVA model, it did not reach statistical significance for the majority of outcome variables (p\u0026gt;0.05), indicating that the observed activity-level and sex-based differences in motor performance were not primarily attributable to differential biological maturation. This supports the interpretation that training adaptation was the primary driver of performance differences [26-28].\u003c/p\u003e\n\u003cp\u003eIn this study, FMS and Y-Balance tests were used not only for injury prediction but also for evaluating functional movement quality and dynamic balance profiles. Although the literature reports that these tests alone have limited validity for predicting injury, they are stated to offer valuable information in identifying individual movement disorders and creating performance profiles [17, 22, 41-43]. The findings support the idea that interpreting these tests within multifactorial performance and risk models is more appropriate than using them in isolation with fixed threshold values [17, 44].\u003c/p\u003e\n\u003cp\u003eY-Balance test results showed that the soccer player groups (G1 and G3) exhibited statistically superior dynamic balance performance compared to the sedentary groups (G2 and G4). The high effect sizes observed, particularly in right composite scores and posterolateral extension, suggest that soccer training has a strong influence on proprioceptive control and postural stability, and the significant results obtained in athletes\u0026apos; agility scores also support this. These results suggest the importance of hip stabilizers and trunk control in soccer performance. Evaluating Y-Balance test scores as percentages increases methodological power by reducing limb length-related biases [21]. Literature has shown that Y-Balance test asymmetries are associated with injury history, and that athletes with anterior or posteromedial asymmetry \u0026ge;4 cm have a higher reported injury prevalence [20, 45]. The absence of a significant difference in injury rates suggests that low left-right asymmetry values across all groups may be a contributing factor [45, 46].\u003c/p\u003e\n\u003cp\u003eThe relationships between agility, dynamic balance, and functional movement measurements reveal that the performance components were not independent of each other. Relationships between dynamic balance scores as well as power, speed, and deflection performance showed that FMS and Y-Balance test results were significantly associated with these performance components [47]. The decrease in agility times as dynamic balance scores increase supports the decisive role of postural control in change-of-direction performance. Similarly, the negative correlation between total FMS scores and agility tests, and a positive correlation with balance scores, suggests that functional movement quality was central to the multi-performance construct [48, 49]. It was thought that numerous factors, such as sudden changes of direction, stopping and starting, and high-speed movement inherent in nature of soccer, may influence the development of neuromuscular coordination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe fact that gender showed a significant effect on some performance variables and an insignificant effect on others reflects the interaction of biological differences with training adaptations. While the superior performance of male athletes in tests requiring upper extremity strength and trunk stability can be explained by biological factors, a history of regular training has been shown to reduce gender-related differences in multi-component parameters such as functional movement quality and dynamic balance. Similarly, the literature reports that gender differences are minimized in athlete populations with high levels of training [38, 50].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFactors limiting the interpretation of these study findings include the cross-sectional design, cause-and-effect relationships, the retrospective collection of injury data, and the inability to control for variations in the rate of biological maturation during adolescence [51]. Most critically, although maturity offset (MO) was estimated using the Mirwald et al. (2002) equations and statistically controlled as a covariate, this approach provides an indirect estimation of biological maturation and may not fully reflect inter-individual variability. At ages 14\u0026ndash;16, maturation directly influences all outcome variables through mechanisms including transient proprioceptive disruption during Peak Height Velocity (PHV), rapid limb elongation affecting balance normalisation, and neuromuscular reorganisation during the pubertal growth spurt [27, 28]. The substantial sex difference in maturity offset observed in this sample (females \u0026asymp; 1.79 years post-PHV vs. males \u0026asymp; 0.02 years near-PHV) means that sex-based performance comparisons should be interpreted with caution. In the present study, ANCOVA with maturity offset as covariate was applied; the non-significant covariate effect corroborates the training-adaptation interpretation. It should additionally be noted that the Mirwald et al. (2002), female regression equation has documented validity limitations when applied to girls aged 14\u0026ndash;16, a period substantially beyond the calibration range of the original sample [52]. This represents an acknowledged methodological constraint, and future research should employ population-specific equations for female adolescents in this age range [26-28]. Additionally, injury data were collected retrospectively via self-report, which carries a risk of recall bias and may underestimate true injury prevalence. The school-based sampling frame, while ecologically valid, limits generalisability to community or elite youth sport populations.\u003c/p\u003e\n\u003cp\u003eIn conclusion, it was observed that regular and consistent football training can improve the functional movement quality, dynamic balance, and agility performance of young athletes. However, considering that these performance improvements are not directly related to a reduction in self-reported injury prevalence, it suggests that FMS and Y-Balance tests may be more appropriately used in field applications not only as prospective injury screening tools, but also for identifying individual movement deficiencies and preparing individualized training programs. This approach can enable coaches and field practitioners to create targeted functional training programs that support performance development. Furthermore, it is thought that this study can increase awareness of the positive and protective effects of physical activity on musculoskeletal health in young individuals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u0026bull; \u003cstrong\u003eACL\u003c/strong\u003e: Anterior Cruciate Ligament\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eANCOVA\u003c/strong\u003e: Covariance Analysis\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eANOVA\u003c/strong\u003e: Analysis of Variance\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eBMI\u003c/strong\u003e: Body Mass Index\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eFMS\u003c/strong\u003e: Functional Movement Screen\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eG1\u003c/strong\u003e: Male Soccer Players\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eG2\u003c/strong\u003e: Male Sedentary Students\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eG3\u003c/strong\u003e: Female Soccer Players\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eG4\u003c/strong\u003e: Female Sedentary Students\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eIP\u003c/strong\u003e: Injury Prevalence\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eMO\u003c/strong\u003e: Maturity Offset\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003ePHV\u003c/strong\u003e: Peak Height Velocity\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eSD\u003c/strong\u003e: Standard Deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent Forms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design and methodology were reviewed and approved by the Uşak University Non-Interventional Clinical Research Ethics Committee (Decision No: 02, Date: 18/05/2023). All participants were given detailed information about the scope of the study, and their written consent was obtained through the \u0026quot;Informed Consent Form\u0026quot;. The study was conducted in accordance with ethical standards regarding human trials and the 2013 revised version of the 1975 Helsinki Declaration. Since the research group consisted of individuals under 16 years of age, written informed consent was obtained from their parents or legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtocol and Statistical Analysis Plan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol and statistical analysis plan are not publicly available, but all relevant methodological details are provided in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors reported no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll resources used for this study were provided by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor 1\u003c/em\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConceived and designed the study, coordinated data collection, performed the statistical analyses, and drafted the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor 2\u003c/em\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eContributed to the study design, assisted with data interpretation, and critically revised the manuscript for important intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the participants in this study, as well as our physical therapy and rehabilitation specialists and trainers who took part.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJohnson D, Williams S, Bradley B, Cumming SP. 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Prediction of adult height using maturity-based cumulative height velocity curves. \u003cem\u003eJ Pediatr\u003c/em\u003e. 2005;147(4):508-14. https://doi: 10.1016/j.jpeds.2005.04.041\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Usak University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Functional movement analysis, Y-Balance test, dynamic balance, motor performance, injury prevalence, adolescent athletes","lastPublishedDoi":"10.21203/rs.3.rs-9348731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9348731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eFunctional movement quality and dynamic balance are key components that play an important role in relation to injury prevalence in adolescent athletes. Rapid growth and neuromuscular changes during mid-adolescence can increase susceptibility to movement deficiencies and sports-related injuries. Comparing physically active and sedentary adolescents within the same methodological framework may contribute to a better understanding of modifiable injury prevalence factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 184 adolescents aged 14–16 years were included in the study (48 male soccer players, 45 female soccer players, 48 sedentary males, and 43 sedentary females). Functional movement quality was assessed using Functional Movement Analysis (FMS), and dynamic balance was assessed using the Y-Balance Test. Agility, flexibility, and coordination tests were applied to assess biomotor capacities. The differences between the functional movement quality, dynamic balance, and biomotor performance variables of the research group were examined using one-way ANOVA followed by multiple comparisons. The effects of activity level (soccer player/sedentary), gender (male/female), and biological maturity offset (MO) factors on motor performance variables were analysed using 2x2 factorial ANCOVA. The relationships between the groups’ FMS, Y-Balance scores, and biomotor performance capacities were examined using Pearson correlation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSoccer player groups exhibited significantly higher FMS scores (p\u0026lt;0.05), Y-Balance composite scores (p\u0026lt;0.001), and agility performance (p\u0026lt;0.001) compared to sedentary participants. Activity level emerged as the primary determinant for all motor performance variables (p\u0026lt;0.001), while the effect of gender was more limited. Significant correlations were found between FMS and Y-Balance test scores and agility and coordination performances (ranging from r=−0.375 to r=0.385, p\u0026lt;0.01). No significant difference was found between the groups in terms of self-reported injury prevalence (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eRegular soccer training was found to positively affect the functional movement quality, dynamic balance, and agility performance of adolescents. The results suggested that FMS and Y-Balance Tests were more suitable tools for identifying individual movement deficiencies and planning targeted training programs in this age group, rather than solely predicting future injury.\u003c/p\u003e","manuscriptTitle":"Functional Movement Quality, Dynamic Balance and Injury Prevalence in Adolescent Soccer Players: Comparison with Sedentary Adolescents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 09:50:09","doi":"10.21203/rs.3.rs-9348731/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea9070f4-35f0-48c1-84b1-11d85858c270","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65886721,"name":"Sports Medicine and Kinesiology"}],"tags":[],"updatedAt":"2026-04-09T09:50:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 09:50:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9348731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9348731","identity":"rs-9348731","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}