A Multivariate Recognition Profiling and Classifying for Youth Elite Badminton Players Based on Anthropometric and Fitness Indicators

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With the exception of isolated traits, previous research has given little attention to the physiological and anthropometric characteristics that aid performance. Method: 26 elite youth male players aged between 12 and 16 participated in the research, and a set of validated physical and anthropometric evaluations were conducted. Exploratory Factor Analysis (EFA) determined the latent variables, while Discriminant Analysis (DA) determined the competition level classification of the players. Results: EFA identified three latent variables which accounted for 74.29 of the total variance, and these included: (1) Muscular Profile and Functional Leverage (limb circumferences, grip strength, and flexibility), (2) Aerobic Stability and Movement Control (VO₂max, sit-ups, and hand-eye coordination), and (3) Repetitive Stroke Capacity (push-up performance). DA the least flexibility and agility as discriminating variables with a 96.15% classification accuracy. Conclusion: The model, which greatly aids in athlete profiling, personalized training, and talent scouting, indicates that flexibility and agility are the foremost determinants of performance in badminton. Health sciences/Health care Biological sciences/Physiology youth players badminton profiling principal component analysis discriminant analysis performance classification anthropometric attributes Figures Figure 1 Figure 2 Figure 3 Introduction Badminton is a sport that possesses unique characteristics such as intermittent bursts of movement, swift action, and intense power output. It requires players to pivot and accelerate, perform multidirectional sprints and explosive jumps, and simultaneously sustain motion and manage recovery periods within a rally. Such mechanical and physiological demands are placed on the aerobic and anaerobic energy systems 1 , 2 . It has been demonstrated that fatigue impacts vertical leap performance as well as the ability to perform directional changes quickly, which are critical for adept stroke execution and efficient court coverage 3 , 4 . The developmental stage of youth elite players of a given age is considered the peak age, and therefore, the demands for this age group are remarkably tailored and age appropriate. This group is comprised of young athletes because of their age and their physiological systems are still maturing, which greatly differs from that of fully matured adults. The age of adolescence is a sensitive stage within the longitudinal athlete development (LTAD) model, and purposeful and appropriately tailored to age strategies must be employed to bolster performance structure 5 . Thus, the early adolescent stage has a remarkable advantage where physical attributes can be enhanced rapidly and training strategies can be appropriately tailored to align the training and athlete performance. In addition to physiological attributes, concentration, stress regulation, and quick decision-making under pressure during high-stakes moments also factor into performance in badminton, which has psychological and cognitive underpinnings. These dimensions are the most unstable among youth players, as they are often coupled with psychosocial transitions and advancing in the sport 6 , 7 . This highlights the need to take into consideration anthropometric and physiological factors, capturing a more comprehensive set of factors that determine performance in young badminton players. Further, physiological data demonstrates the considerable demands placed on adolescent players. For example, during competitive matches, adolescent male players were documented with a mean heart rate of 151 ± 12 bpm (82% of HRmax) and oxygen consumption of 39.2 ± 3.9 mL·kg⁻¹·min⁻¹ 8 . These findings reflect significant metabolic strain, even at the youth level, especially emphasizing the importance of having systematic profiling and evaluation frameworks within developmental pathways. The previous research indicates that badminton performance is deeply linked to a player’s anthropometry and physical fitness, with height, limb length, body composition, somatotype, agility, muscular strength, and coordination often serving as distinguishing markers between elite and non-elite players 9 – 11 . Still, much of the available research has only focused on one attribute at a time instead of as part of a greater whole as an intricate, interconnected, multidimensional system. As a result, there is a considerable lack of research from multiple disciplines that use associative statistical methods to analyze how various anthropometric and physical fitness variables cumulatively influence performance classification in adolescent athletes. To address this gap, the current study aims to construct a multivariate model to develop recognition profiles and classify youth elite badminton players based on certain anthropometric and physical fitness parameters. More specifically, the study has two primary aims: first, to determine the ability of the indicators to create latent constructs with exploratory factor analysis (EFA); and second, to evaluate the ability of the extracted factors to differentiate performance levels with discriminant analysis (DA). Using a multivariate approach, this study develops a profiling model with immediate applications for the identification of talent, customized training plans, and the ongoing evaluation of developing athletes. Coaches and sports scientists, as well as the frameworks for talent development, are expected to gain the most from the results. Such findings would, in turn, facilitate the development of a well-informed approach to training and developing young elite badminton players. Material and Methods Participants The participants in the study were 26 male elite youth badminton players aged between 12 and 16 years (M = 14.4, SD = 1.3) who were enrolled in national or state-level development programs meant to funnel talent into higher competition levels. The sample size exceeded the widely accepted minimum threshold of 17 participants for research with adolescent athletes, thereby providing sufficient statistical power for the analyses undertaken 12 . Ethical approval for the study was granted by the ‘Jawatankuasa Etika Penyelidik’ (JKEP), Universiti Pertahanan Nasional Malaysia (UPNM) register under JKEP: 17/2025 prior to data collection. Informed written consent was provided by the participants and their guardians, ensuring data collection was voluntary. Procedures for data collection were carried out in strict compliance with standardized and validated protocols that were tailored to the study context, thus ensuring all ethical, scientific, and rigor standards were met throughout the research process. Physical and Anthropometric Assessments A set of validated field-based assessments was carried out to determine the anthropometric and physical fitness characteristics of youth elite badminton players. The assessments focused on the specific physiologic and biomechanical features of badminton and the sport’s requirements for growing players. All protocols were age-appropriate and standardizes across participants to ensure methodological coherence and contextual relevance. Cardiovascular endurance was assessed with the 20 meter multistage shuttle run test (beep test). This test measures aerobic fitness which is important for maintaining multiple high-intensity bursts of activity and for recovering during the match. Participants ran to two markers which were set at a distance of 20 meters, back and forth, and matched to audio cues that gradually increased in pace until they either chose to stop or failed to perform the set number of repetitions. Maximal oxygen uptake (VO₂max) was estimated using the formula VO₂max = (distance × 0.0084) + 36.4 13 . The test is reliable in predicting VO₂max and has strong test–retest reliability in adolescent samples (ICC = 0.89). Muscular endurance was assessed with 60-second sit-up and push-up tests. These tests measure trunk endurance and upper body endurance specific to muscle groups as they pertain to stroke and rally generation as well as defensive posture control and maintenance. Each participant's best result, defined as performance that consisted of completed repetitions, was noted and entered into the study database. The sit-up test showed to be reliable (ICC = 0.92) 14 , and the push-up test displays excellent reliability (ICC = 0.99) 15 . Muscle power of the lower body that is explosive and assists in the performance of jumping smashes, quick lunges, and acceleration in several movement directions is measured with the vertical jump test. The Relative Power is calculated with the formula: Relative Power = [(60.7 × jump height) + (45.3 × body mass) − 2055] ÷ body mass as proposed by 16,17 . This test is well accepted and is known to have good reliability (ICC = 0.97 − 0.99) 18 . The degree of flexibility in the lower back and hamstrings was measured using the V-sit-and-reach test. Flexibility facilitates greater range of motion and efficient lunging, as well as lower injury risk. Each participant performed two trials, and the best was recorded in centimeters. The V-sit-and-reach test is common in youth populations and has been shown to be reliable (ICC = 0.95) 19 . Muscular strength was assessed with a digital handgrip dynamometer, as grip strength was correlated with racket control, stroke precision, and endurance in high-frequency shots. The dominant hand was tested using two maximal-effort trials, and the best score in kilograms was recorded. The test has good reliability (ICC = 0.91) and is accepted as a reliable measure of overall upper body strength 20 , 21 . The test of hand wall toss assessed the hand-eye coordination which is important for timing, reflexes, and shuttle interception. Each participant was timed for 30 seconds while alternating throws and catches of a tennis ball, and the total successful catches were recorded. The test is reliable (ICC = 0.875) and has been frequently employed in athletic profiling 22 . Two drills from 4 were used to measure badminton agility: the lateral side step test and the four-corner agility test. The former mimics a player’s lateral defensive recovery movement, and the latter mimics the match-play sprinting movement across predetermined court zones. Although 4 did not formally report ICC values, these tests are commonly used and accepted as having ecological validity for badminton and are reliable for movement profiling. The anthropometric characteristics were measured in accordance with the ISAK guidelines 23 . These included: sleeve length, arm span, leg length, and the upper and lower arm, thigh, and calf circumferences. These were measured with a non-stretchable tape and were recorded to the nearest 0.1 cm while the participant stood relaxed and resting. These indicators provide insight into segmental mass distribution and limb proportions, and outline the athlete's physical attributes that could be advantageous in reach and balance, as well as mechanically efficient biomechanics in the course of stroke execution and court movement. Result The dataset underwent an initial assessment to determine its suitability for performing a factor analysis. The Kaiser‑Meyer‑Olkin (KMO) measure of sampling adequacy gave an aggregate figure of 0.730, which denotes a meritorious level of adequacy 24 . This means that the correlation matrix was dense enough for factor extraction to be carried out. The majority of the variables met the KMO recommended cutoff of 0.60, in particular Lower Arm Circumference (0.849), Handgrip Strength (0.827), and Upper Arm Circumference (0.780). KMO values for Sit‑Up and Push‑Up were 0.282 and 0.397, respectively, which are low relative to the rest of the variables. This disparity may be due to the unique physiological nature of these items. Sit‑Up assesses core muscular endurance, and Push‑Up assesses endurance of the upper body traits that are unlikely to be strongly correlated with structural anthropometric measurements such as limb circumferences or flexibility. 25 evidenced that core and upper body endurance variables are weakly related to anthropometric measurements in regression models. Therefore, these variables may be considered as independent latent constructs with low sampling adequacy 26 . The results are provided in Table 1 . Table 1 Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy for Each Variable and Overall Dataset Sleeve Length/Cm 0.766 Length Spanning Arms/Cm 0.772 Leg Length/Cm 0.699 Upper Arm Circumference/Cm 0.780 Lower Arm Circumference/Cm 0.849 Calf Circumference/Cm 0.777 Thigh Circumference/Cm 0.689 Sit Up 0.282 Push Up 0.397 Relative Power Double Leg (W/Kg) 0.767 V Sit And Reach 0.511 Handgrip/Kg 0.827 Hand Wall Tost 0.790 Sideway Agilty (Sec) 0.702 Four Corner Agilty(Sec) 0.637 Aerobic Vo2max 0.759 KMO 0.730 The scree plot was used to determine the optimal number of components. The eigenvalues associated with each component are plotted in Fig. 1. There is a noticeable inflection point or “elbow” after the third component, which suggests that the amount of variance explained by the remaining components is significantly lower. According to the eigenvalue‑greater‑than‑one rule and the elbow criterion, a three‑factor extraction was sufficient and served as a strong foundation for the subsequent principal component analysis. Table 2 shows the factor loadings resulting from Varimax rotation for the three components derived together with their eigenvalues and explained variance. Following 27 criteria, a factor loading of 0.50 and above was considered a meaningful threshold for interpretation. The three components together contributed 74.29% of the total variance which is above the minimum criteria usually required in more than univariate analyses. The first component (eigenvalue = 9.291; 58.07% variance explained) was defined by strong upper arm circumference (0.928), lower arm (0.952), thigh (0.935), calf (0.901) circumference, and handgrip strength (0.863) loadings with some additional support from arm span, leg length, sleeve length, and some flexibility. This dimension was labeled muscular profile and functional leverage, illustrating the extent to which muscular development and segmental size facilitate the generation of power, enable greater reach and provide effective control on the court physically. The second component (eigenvalue = 1.489; 9.31% variance explained) include primarily sit-up performance (0.853), VO₂max (0.582), and hand wall toss (0.545). This component labeled as aerobic stability and movement control is defined by the contribution of trunk endurance, aerobic capacity, and neuromuscular coordination to the maintenance of posture, balance, and execution of technique during very strong rallies. The third component (eigenvalue = 1.106; 6.91% variance explained) had a strong negative loading for the push-up performance, which drove the distinct repetitive stroke capacity factor. This component captures the ability to repeat complex upper body movements such as smashes, clears, and defensive strokes with power and precision over prolonged periods of competition. Table 2 Factor Loadings After Varimax Rotation for Three Extracted Components of Physical and Anthropometric Attributes D1 D2 D3 Sleeve length/cm 0.542 0.507 0.437 Length spanning arms/cm 0.764 0.459 0.310 Leg length/cm 0.559 0.542 0.400 Upper arm circumference/cm 0.928 0.092 0.090 Lower arm circumference/cm 0.952 0.095 0.064 Calf circumference/cm 0.901 0.079 0.239 Thigh circumference/cm 0.935 0.137 0.040 Sit up -0.180 0.853 -0.121 Push up -0.048 0.051 -0.922 Relative power double leg (W/Kg) 0.564 0.352 0.276 V sit and reach 0.538 0.234 0.055 Handgrip/kg 0.863 0.219 0.210 Hand wall toss 0.559 0.545 0.325 Sideway agility (sec) -0.687 -0.462 -0.112 Four corner agility(sec) -0.486 -0.523 -0.088 Aerobic Vo2max 0.563 0.582 -0.039 Eigenvalues 9.291 1.489 1.106 Variability (%) 58.071 9.308 6.912 Cumulative % 58.071 67.379 74.292 These relationships are further elucidated in Fig. 2, which shows the factor loading plot for Dimensions 1 and 2, which together account for 64.09% of the total variance. Variables pertaining to the muscular profile and functional leverage distinctly grouped along Dimension 1, whereas those reflecting aerobic stability and control of movements (e.g. Sit-Up, VO₂max, Hand Wall Toss) positioned along Dimension 2. It is also noteworthy that Push-Up was plotted in the negative quadrant which further strengthens its isolation and association with the third dimension, repetitive stroke capacity. Taken together, the results presented in Table 2 and Fig. 2 strongly confirm a three-factor solution and corroborate the multi-faceted framework of the physical and anthropometric attributes of adolescent badminton players. The distribution of young badminton players according to competitive performance categories is shown in Table 3 . Most players were eliminated at the Round of 32 stage (57.69%), 26.92% progressed to the Round of 16 to Quarterfinals, and only 15.39% advanced to the semifinals or earned medals. This stratification was useful in determining whether the players’ physiological and anthropometric measurements could distinguish the different levels of competitive achievement. Table 3 Distribution of Youth Badminton Players by Competitive Performance Category Variable Categories % performance R16 - QF 26.92 R32 57.69 medalist 15.39 The univariate analyses were performed, and the results are displayed in Table 4 . Performance groups differed for three variables with significant differences: V Sit and Reach (p = 0.020), Sideway Agility (p = 0.039), and Four-Corner Agility (p = 0.006). These results illustrate the crucial role of flexibility and multidirectional agility for badminton performance. Players who advanced to later rounds displayed flexibility and agility, which are vital for quick directional changes, efficient court coverage, and maintaining posture during intense high-speed rallies. On the other hand, other physical and anthropometric measurements like strength, endurance, and segmental circumferences showed no significant differences between the groups (p > 0.05), indicating these variables had a relatively weak influence on distinguishing outcomes of competition levels. Table 4 Results of Univariate Tests for Differences in Physical and Anthropometric Variables Across Performance Groups Variable Lambda F DF1 DF2 P-Value Sleeve Length/Cm 0.917 1.046 2 23 0.368 Length Spanning Arms/Cm 0.809 2.718 2 23 0.087 Leg Length/Cm 0.896 1.334 2 23 0.283 Upper Arm Circumference/Cm 0.849 2.045 2 23 0.152 Lower Arm Circumference/Cm 0.863 1.820 2 23 0.185 Calf Circumference/Cm 0.827 2.410 2 23 0.112 Thigh Circumference/Cm 0.893 1.381 2 23 0.271 Sit Up 0.967 0.392 2 23 0.680 Push Up 0.805 2.785 2 23 0.083 Relative Power Double Leg (W/Kg) 0.949 0.614 2 23 0.550 V Sit And Reach 0.711 4.669 2 23 0.020 Handgrip/Kg 0.862 1.838 2 23 0.182 Hand Wall Tost 0.786 3.130 2 23 0.063 Sideway Agilty (Sec) 0.754 3.759 2 23 0.039 Four Corner Agilty(Sec) 0.636 6.574 2 23 0.006 Aerobic Vo2max 0.952 0.578 2 23 0.569 To confirm the results, discriminant function analysis was performed. The discriminant plot in Fig. 3 shows the distribution of players over the two extracted functions which made up 100% of the total variance (Function 1: 65.35%; Function 2: 34.65%). Within each category, clear clustering of participants was noted, and the group centroids were well separated. The R16–QF and Medalist groups were distinctly positioned away from the R32 group, which illustrates how well the discriminant functions captured the physical attributes differentiating the performance tiers. The strength of the model was validated through classification accuracy results (Table 5 ). Out of 26 participants, 25 were accurately classified, resulting in overall accuracy of 96.15%. Both the R16–QF and Medalist groups recorded perfect classification rates (100%), while the R32 group had a substantial accuracy of 93.33% with one case misclassified. The model’s high predictive validity strengthens the overall claim while depicting the agility and flexibility’s fierce discriminative strength in profiling youth badminton players relative to the competitive results. Table 5 Classification Accuracy of Discriminant Analysis Model Based on Confusion Matrix from \ to R16 - QF R32 medalist Total % correct R16 - QF 7 0 0 7 100.00% R32 1 14 0 15 93.33% medalist 0 0 4 4 100.00% Total 8 14 4 26 96.15% Discussion This research aimed to construct a multivariate profiling model which classifies youth elite badminton players according to their anthropometric and fitness attributes. With Exploratory Factor Analysis (EFA), three latent components emerged: (1) Muscular Profile and Functional Leverage, (2) Aerobic Stability and Movement Control, and (3) Repetitive Stroke Capacity. These components together capture the intricate blend of the badminton specific physiological and biomechanical factors necessary to excel in the sport. Alongside this, discriminant analysis (DA) regarding performance levels showed that flexibility and agility distinguished the groups with a classification accuracy of 96.15%, exhibiting strong discriminative power. Muscular Profile and Functional Leverage, the first component, was characterized by anthropometric and strength attributes of fitness like circumferences of limbs, handgrip strength, and flexibility. This aligns with prior research demonstrating that anthropometric appreciation aids in stroke range, balance, and spatial dominance in badminton 10 , 11 . However, contrary to prior literature which focused on morphological predictors, this study showed that these attributes were unlikely to sharply differentiate competitive levels due to a homogeneous sample. The second component, Aerobic Stability and Movement Control, integrates trunk endurance, aerobic fitness, and neuromotor coordination as indicated by sit-up performance, VO₂max, and hand wall toss. These findings corroborate 2 , who emphasized endurance and accuracy as critical for maintaining technical skill execution during lengthy rallies. The third component, Repetitive Stroke Capacity, driven by push-up performance, reiterates 4 , who highlighted the importance of upper-body endurance for multiple overhead strokes. The robustness of the factor model suggests only flexibility (V Sit and Reach) and agility (Sideway Agility and Four-Corner Agility) differentiated performance groups. This is in alignment with 28 , who emphasized agility and economical movement in relation to racket sports, and also supports 2 who highlighted the importance of speed in the change of direction in badminton. In the opposite direction, handgrip strength, other anthropometric measures, and upper body endurance did not create differentiated groups. This may stem from the biological and maturational uniformity, as proposed by 5 , who suggested that growing up tends to blur the predictive value structural measures have in profiling young athletes. The study’s novelty is its combination of exploratory factor analysis (EFA) and discriminant analysis (DA) to develop a profiling model with unparalleled classification precision. Prior research has either concentrated on specific physical assessments or on anthropometric predictors in isolation. This study identifies critical physiological and anthropometric predictors of competitive success and validates their predictive utility using latent component extraction and discriminant classification. This is a methodological improvement in badminton talent identification and corresponds with the movement toward evidence-based multidimensional profiling in sports science 29 . Conclusion This research highlighted three latent constructs related to elite youth badminton performance: Muscular Profile and Functional Leverage, Aerobic Stability and Movement Control, and Repetitive Stroke Capacity. These factors derived from exploratory factor analysis (EFA) and confirmed via discriminant analysis (DA) contribute to understanding the competitive badminton’s multidimensional and complex physiological and biomechanical requirements. Of all the measured variables, flexibility and agility proved to be the strongest discriminant enabling the model to classify players accurately by 96.15%. This research is innovative in applying EFA and DA to build a robust profiling model that integrates latent structural dimensions and strong predictive accuracy. This is unlike other studies that focused on singularized anthropometric or physical characteristics of the youth athletes, reinforcing the argument for a more holistic, multi-faceted, rigorous evidence-based framework for determining competitive level among the athletes. The findings in this research emphasize agility and flexibility in talent identification and encourage these factors to be more prioritized in the training prescription and the player’s long-term development plan. The model can be leveraged by coaches and sport scientists for more effective athlete evaluation, targeted training, and for more strategically guided talent development pathways in badminton. Declarations Funding This study did not receive any specific funding or grants. Author Contribution Mohammad Firdaus Mohd Israj¹ contributed to the conceptualization of the study, data collection, statistical analysis, and preparation of the original manuscript draft. Nur Syazwani Ibrahim² assisted in data curation, visualization, and manuscript editing. Nor Ikhmar Madarsa³ contributed to the methodology design, validation, and critical review of the results. Mohar Kassim⁴ contributed to literature review support, resources, and coordination of data acquisition. Rozita Binti Abdul Latif⁵ contributed to the review of theoretical background, editing, and refinement of the manuscript structure. Ahmad Bisyri Husin Musawi Maliki⁶ provided supervision, project administration, conceptual guidance, and final approval of the manuscript as the corresponding author. All authors have read and approved the final version of the manuscript. Acknowledgement The authors would like to express their sincere gratitude to all participating players and their coaches for their enthusiastic commitment and engagement throughout the study. Appreciation is also extended to the National and State Badminton Development Programmes for providing access to athletes and training facilities. Special thanks are due to the research assistants and volunteers for their valuable help during data collection and logistical coordination. The authors gratefully acknowledge the Universiti Pertahanan Nasional Malaysia (UPNM) for providing institutional facilities, administrative support, and covering the publication cost of this article. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Cabello Manrique, D. & González-Badillo, J. J. Analysis of the characteristics of competitive badminton. Br. J. Sports Med. 37 , 62–66 (2003). Phomsoupha, M. & Laffaye, G. The Science of Badminton: Game Characteristics, Anthropometry, Physiology, Visual Fitness and Biomechanics. 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Talent Identification in Sport: A Systematic Review. Sports Medicine vol. 48 97–109 Preprint at (2018). https://doi.org/10.1007/s40279-017-0803-2 Additional Declarations No competing interests reported. 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. 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Pertahanan Nasional Malaysia (UPNM)","correspondingAuthor":false,"prefix":"","firstName":"Nur","middleName":"Syazwani","lastName":"Ibrahim","suffix":""},{"id":539739077,"identity":"3fea7db7-96ab-41cd-bc74-ae4e5b1cf5b1","order_by":2,"name":"Nor Ikhmar Madarsa","email":"","orcid":"","institution":"Universiti Pertahanan Nasional Malaysia (UPNM)","correspondingAuthor":false,"prefix":"","firstName":"Nor","middleName":"Ikhmar","lastName":"Madarsa","suffix":""},{"id":539739078,"identity":"5aed03c5-a725-4357-af7b-f034a8ce5036","order_by":3,"name":"Mohar Kassim","email":"","orcid":"","institution":"Universiti Pertahanan Nasional Malaysia (UPNM)","correspondingAuthor":false,"prefix":"","firstName":"Mohar","middleName":"","lastName":"Kassim","suffix":""},{"id":539739079,"identity":"19f33e6b-972f-41fb-a2bd-da7180ba7166","order_by":4,"name":"Rozita Abdul Latif","email":"","orcid":"","institution":"Universiti Teknologi MARA (UiTM)","correspondingAuthor":false,"prefix":"","firstName":"Rozita","middleName":"Abdul","lastName":"Latif","suffix":""},{"id":539739080,"identity":"173d1213-8741-44b8-b8d0-d95f4218595a","order_by":5,"name":"Ahmad Bisyri Husin Musawi Maliki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACe2YQaWAD5bIRocWwGawljQQtBgfA1GFSbGlnfyZdUXA+cbt0dwLDh7LDDLozEvBrsWfmMZM8Y3A7ceecsxsYZ5w7zGB2g4AWw2YeNskGoJYNN3I3MPO2EaHF4DD7M6CWcxAtf4nTwmAG1HIAooWRGC1AhxlbNhgkG++ckbvhYM+5dB6zMw/wa7HnP/7wZsMfO9ntErkbH/wos5YzO07AFiBgkQC7EIgPADEPgwBhLcwfYFoggP8AQS2jYBSMglEwsgAAUCBIH+HzG+UAAAAASUVORK5CYII=","orcid":"","institution":"Universiti Pertahanan Nasional Malaysia (UPNM)","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"Bisyri Husin Musawi","lastName":"Maliki","suffix":""}],"badges":[],"createdAt":"2025-10-16 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2","display":"","copyAsset":false,"role":"figure","size":50619,"visible":true,"origin":"","legend":"\u003cp\u003eFactor Loading Plot After Varimax Rotation Displaying Variable Contributions to Dimensions 1 and 2\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7878552/v1/30b5186a7c680902b034dd25.png"},{"id":95291975,"identity":"ca2c5b5a-a842-42f0-af89-1059f6e14e8c","added_by":"auto","created_at":"2025-11-06 11:09:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61444,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminant Function Plot Showing Group Separation Among Performance Categories\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7878552/v1/04f672c77e69df76e0908143.png"},{"id":95529686,"identity":"946d103a-fac9-4586-935e-3f1d85045f4d","added_by":"auto","created_at":"2025-11-10 10:17:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":818376,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7878552/v1/69e7ac5b-265d-49ca-b9be-234876b62040.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multivariate Recognition Profiling and Classifying for Youth Elite Badminton Players Based on Anthropometric and Fitness Indicators","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBadminton is a sport that possesses unique characteristics such as intermittent bursts of movement, swift action, and intense power output. It requires players to pivot and accelerate, perform multidirectional sprints and explosive jumps, and simultaneously sustain motion and manage recovery periods within a rally. Such mechanical and physiological demands are placed on the aerobic and anaerobic energy systems \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It has been demonstrated that fatigue impacts vertical leap performance as well as the ability to perform directional changes quickly, which are critical for adept stroke execution and efficient court coverage \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe developmental stage of youth elite players of a given age is considered the peak age, and therefore, the demands for this age group are remarkably tailored and age appropriate. This group is comprised of young athletes because of their age and their physiological systems are still maturing, which greatly differs from that of fully matured adults. The age of adolescence is a sensitive stage within the longitudinal athlete development (LTAD) model, and purposeful and appropriately tailored to age strategies must be employed to bolster performance structure \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Thus, the early adolescent stage has a remarkable advantage where physical attributes can be enhanced rapidly and training strategies can be appropriately tailored to align the training and athlete performance.\u003c/p\u003e\u003cp\u003eIn addition to physiological attributes, concentration, stress regulation, and quick decision-making under pressure during high-stakes moments also factor into performance in badminton, which has psychological and cognitive underpinnings. These dimensions are the most unstable among youth players, as they are often coupled with psychosocial transitions and advancing in the sport \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This highlights the need to take into consideration anthropometric and physiological factors, capturing a more comprehensive set of factors that determine performance in young badminton players.\u003c/p\u003e\u003cp\u003eFurther, physiological data demonstrates the considerable demands placed on adolescent players. For example, during competitive matches, adolescent male players were documented with a mean heart rate of 151\u0026thinsp;\u0026plusmn;\u0026thinsp;12 bpm (82% of HRmax) and oxygen consumption of 39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 mL\u0026middot;kg⁻\u0026sup1;\u0026middot;min⁻\u0026sup1; \u003csup\u003e8\u003c/sup\u003e. These findings reflect significant metabolic strain, even at the youth level, especially emphasizing the importance of having systematic profiling and evaluation frameworks within developmental pathways.\u003c/p\u003e\u003cp\u003eThe previous research indicates that badminton performance is deeply linked to a player\u0026rsquo;s anthropometry and physical fitness, with height, limb length, body composition, somatotype, agility, muscular strength, and coordination often serving as distinguishing markers between elite and non-elite players \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Still, much of the available research has only focused on one attribute at a time instead of as part of a greater whole as an intricate, interconnected, multidimensional system. As a result, there is a considerable lack of research from multiple disciplines that use associative statistical methods to analyze how various anthropometric and physical fitness variables cumulatively influence performance classification in adolescent athletes.\u003c/p\u003e\u003cp\u003eTo address this gap, the current study aims to construct a multivariate model to develop recognition profiles and classify youth elite badminton players based on certain anthropometric and physical fitness parameters. More specifically, the study has two primary aims: first, to determine the ability of the indicators to create latent constructs with exploratory factor analysis (EFA); and second, to evaluate the ability of the extracted factors to differentiate performance levels with discriminant analysis (DA).\u003c/p\u003e\u003cp\u003eUsing a multivariate approach, this study develops a profiling model with immediate applications for the identification of talent, customized training plans, and the ongoing evaluation of developing athletes. Coaches and sports scientists, as well as the frameworks for talent development, are expected to gain the most from the results. Such findings would, in turn, facilitate the development of a well-informed approach to training and developing young elite badminton players.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThe participants in the study were 26 male elite youth badminton players aged between 12 and 16 years (M\u0026thinsp;=\u0026thinsp;14.4, SD\u0026thinsp;=\u0026thinsp;1.3) who were enrolled in national or state-level development programs meant to funnel talent into higher competition levels. The sample size exceeded the widely accepted minimum threshold of 17 participants for research with adolescent athletes, thereby providing sufficient statistical power for the analyses undertaken \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Ethical approval for the study was granted by the \u0026lsquo;Jawatankuasa Etika Penyelidik\u0026rsquo; (JKEP), Universiti Pertahanan Nasional Malaysia (UPNM) register under JKEP: 17/2025 prior to data collection. Informed written consent was provided by the participants and their guardians, ensuring data collection was voluntary. Procedures for data collection were carried out in strict compliance with standardized and validated protocols that were tailored to the study context, thus ensuring all ethical, scientific, and rigor standards were met throughout the research process.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhysical and Anthropometric Assessments\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA set of validated field-based assessments was carried out to determine the anthropometric and physical fitness characteristics of youth elite badminton players. The assessments focused on the specific physiologic and biomechanical features of badminton and the sport\u0026rsquo;s requirements for growing players. All protocols were age-appropriate and standardizes across participants to ensure methodological coherence and contextual relevance. Cardiovascular endurance was assessed with the 20 meter multistage shuttle run test (beep test). This test measures aerobic fitness which is important for maintaining multiple high-intensity bursts of activity and for recovering during the match. Participants ran to two markers which were set at a distance of 20 meters, back and forth, and matched to audio cues that gradually increased in pace until they either chose to stop or failed to perform the set number of repetitions. Maximal oxygen uptake (VO₂max) was estimated using the formula VO₂max = (distance \u0026times; 0.0084)\u0026thinsp;+\u0026thinsp;36.4 \u003csup\u003e13\u003c/sup\u003e. The test is reliable in predicting VO₂max and has strong test\u0026ndash;retest reliability in adolescent samples (ICC\u0026thinsp;=\u0026thinsp;0.89).\u003c/p\u003e\u003cp\u003eMuscular endurance was assessed with 60-second sit-up and push-up tests. These tests measure trunk endurance and upper body endurance specific to muscle groups as they pertain to stroke and rally generation as well as defensive posture control and maintenance. Each participant's best result, defined as performance that consisted of completed repetitions, was noted and entered into the study database. The sit-up test showed to be reliable (ICC\u0026thinsp;=\u0026thinsp;0.92) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and the push-up test displays excellent reliability (ICC\u0026thinsp;=\u0026thinsp;0.99) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Muscle power of the lower body that is explosive and assists in the performance of jumping smashes, quick lunges, and acceleration in several movement directions is measured with the vertical jump test. The Relative Power is calculated with the formula: Relative Power = [(60.7 \u0026times; jump height) + (45.3 \u0026times; body mass)\u0026thinsp;\u0026minus;\u0026thinsp;2055]\u0026thinsp;\u0026divide;\u0026thinsp;body mass as proposed by \u003csup\u003e16,17\u003c/sup\u003e. This test is well accepted and is known to have good reliability (ICC\u0026thinsp;=\u0026thinsp;0.97\u0026thinsp;\u0026minus;\u0026thinsp;0.99)\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe degree of flexibility in the lower back and hamstrings was measured using the V-sit-and-reach test. Flexibility facilitates greater range of motion and efficient lunging, as well as lower injury risk. Each participant performed two trials, and the best was recorded in centimeters. The V-sit-and-reach test is common in youth populations and has been shown to be reliable (ICC\u0026thinsp;=\u0026thinsp;0.95) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Muscular strength was assessed with a digital handgrip dynamometer, as grip strength was correlated with racket control, stroke precision, and endurance in high-frequency shots. The dominant hand was tested using two maximal-effort trials, and the best score in kilograms was recorded. The test has good reliability (ICC\u0026thinsp;=\u0026thinsp;0.91) and is accepted as a reliable measure of overall upper body strength \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe test of hand wall toss assessed the hand-eye coordination which is important for timing, reflexes, and shuttle interception. Each participant was timed for 30 seconds while alternating throws and catches of a tennis ball, and the total successful catches were recorded. The test is reliable (ICC\u0026thinsp;=\u0026thinsp;0.875) and has been frequently employed in athletic profiling \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTwo drills from \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e were used to measure badminton agility: the lateral side step test and the four-corner agility test. The former mimics a player\u0026rsquo;s lateral defensive recovery movement, and the latter mimics the match-play sprinting movement across predetermined court zones. Although \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e did not formally report ICC values, these tests are commonly used and accepted as having ecological validity for badminton and are reliable for movement profiling.\u003c/p\u003e\u003cp\u003eThe anthropometric characteristics were measured in accordance with the ISAK guidelines \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. These included: sleeve length, arm span, leg length, and the upper and lower arm, thigh, and calf circumferences. These were measured with a non-stretchable tape and were recorded to the nearest 0.1 cm while the participant stood relaxed and resting. These indicators provide insight into segmental mass distribution and limb proportions, and outline the athlete's physical attributes that could be advantageous in reach and balance, as well as mechanically efficient biomechanics in the course of stroke execution and court movement.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe dataset underwent an initial assessment to determine its suitability for performing a factor analysis. The Kaiser‑Meyer‑Olkin (KMO) measure of sampling adequacy gave an aggregate figure of 0.730, which denotes a meritorious level of adequacy \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This means that the correlation matrix was dense enough for factor extraction to be carried out. The majority of the variables met the KMO recommended cutoff of 0.60, in particular Lower Arm Circumference (0.849), Handgrip Strength (0.827), and Upper Arm Circumference (0.780).\u003c/p\u003e\u003cp\u003eKMO values for Sit‑Up and Push‑Up were 0.282 and 0.397, respectively, which are low relative to the rest of the variables. This disparity may be due to the unique physiological nature of these items. Sit‑Up assesses core muscular endurance, and Push‑Up assesses endurance of the upper body traits that are unlikely to be strongly correlated with structural anthropometric measurements such as limb circumferences or flexibility. \u003csup\u003e25\u003c/sup\u003e evidenced that core and upper body endurance variables are weakly related to anthropometric measurements in regression models. Therefore, these variables may be considered as independent latent constructs with low sampling adequacy \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy for Each Variable and Overall Dataset\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleeve Length/Cm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength Spanning Arms/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeg Length/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper Arm Circumference/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower Arm Circumference/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalf Circumference/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThigh Circumference/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSit Up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePush Up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative Power Double Leg (W/Kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV Sit And Reach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHandgrip/Kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHand Wall Tost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSideway Agilty (Sec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFour Corner Agilty(Sec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAerobic Vo2max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKMO\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.730\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe scree plot was used to determine the optimal number of components. The eigenvalues associated with each component are plotted in Fig.\u0026nbsp;1. There is a noticeable inflection point or \u0026ldquo;elbow\u0026rdquo; after the third component, which suggests that the amount of variance explained by the remaining components is significantly lower. According to the eigenvalue‑greater‑than‑one rule and the elbow criterion, a three‑factor extraction was sufficient and served as a strong foundation for the subsequent principal component analysis.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the factor loadings resulting from Varimax rotation for the three components derived together with their eigenvalues and explained variance. Following \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e criteria, a factor loading of 0.50 and above was considered a meaningful threshold for interpretation. The three components together contributed 74.29% of the total variance which is above the minimum criteria usually required in more than univariate analyses.\u003c/p\u003e\u003cp\u003eThe first component (eigenvalue\u0026thinsp;=\u0026thinsp;9.291; 58.07% variance explained) was defined by strong upper arm circumference (0.928), lower arm (0.952), thigh (0.935), calf (0.901) circumference, and handgrip strength (0.863) loadings with some additional support from arm span, leg length, sleeve length, and some flexibility. This dimension was labeled muscular profile and functional leverage, illustrating the extent to which muscular development and segmental size facilitate the generation of power, enable greater reach and provide effective control on the court physically.\u003c/p\u003e\u003cp\u003eThe second component (eigenvalue\u0026thinsp;=\u0026thinsp;1.489; 9.31% variance explained) include primarily sit-up performance (0.853), VO₂max (0.582), and hand wall toss (0.545). This component labeled as aerobic stability and movement control is defined by the contribution of trunk endurance, aerobic capacity, and neuromuscular coordination to the maintenance of posture, balance, and execution of technique during very strong rallies.\u003c/p\u003e\u003cp\u003eThe third component (eigenvalue\u0026thinsp;=\u0026thinsp;1.106; 6.91% variance explained) had a strong negative loading for the push-up performance, which drove the distinct repetitive stroke capacity factor. This component captures the ability to repeat complex upper body movements such as smashes, clears, and defensive strokes with power and precision over prolonged periods of competition.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor Loadings After Varimax Rotation for Three Extracted Components of Physical and Anthropometric Attributes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eD2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eD3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleeve length/cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength spanning arms/cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeg length/cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper arm circumference/cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower arm circumference/cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalf circumference/cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThigh circumference/cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSit up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePush up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.922\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative power double leg (W/Kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV sit and reach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHandgrip/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHand wall toss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSideway agility (sec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFour corner agility(sec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAerobic Vo2max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEigenvalues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariability (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThese relationships are further elucidated in Fig.\u0026nbsp;2, which shows the factor loading plot for Dimensions 1 and 2, which together account for 64.09% of the total variance. Variables pertaining to the muscular profile and functional leverage distinctly grouped along Dimension 1, whereas those reflecting aerobic stability and control of movements (e.g. Sit-Up, VO₂max, Hand Wall Toss) positioned along Dimension 2. It is also noteworthy that Push-Up was plotted in the negative quadrant which further strengthens its isolation and association with the third dimension, repetitive stroke capacity. Taken together, the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;2 strongly confirm a three-factor solution and corroborate the multi-faceted framework of the physical and anthropometric attributes of adolescent badminton players.\u003c/p\u003e\u003cp\u003eThe distribution of young badminton players according to competitive performance categories is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Most players were eliminated at the Round of 32 stage (57.69%), 26.92% progressed to the Round of 16 to Quarterfinals, and only 15.39% advanced to the semifinals or earned medals. This stratification was useful in determining whether the players\u0026rsquo; physiological and anthropometric measurements could distinguish the different levels of competitive achievement.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Youth Badminton Players by Competitive Performance Category\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eperformance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR16 - QF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emedalist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe univariate analyses were performed, and the results are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Performance groups differed for three variables with significant differences: V Sit and Reach (p\u0026thinsp;=\u0026thinsp;0.020), Sideway Agility (p\u0026thinsp;=\u0026thinsp;0.039), and Four-Corner Agility (p\u0026thinsp;=\u0026thinsp;0.006). These results illustrate the crucial role of flexibility and multidirectional agility for badminton performance. Players who advanced to later rounds displayed flexibility and agility, which are vital for quick directional changes, efficient court coverage, and maintaining posture during intense high-speed rallies. On the other hand, other physical and anthropometric measurements like strength, endurance, and segmental circumferences showed no significant differences between the groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating these variables had a relatively weak influence on distinguishing outcomes of competition levels.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Univariate Tests for Differences in Physical and Anthropometric Variables Across Performance Groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLambda\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDF1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDF2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleeve Length/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength\u003c/p\u003e\u003cp\u003eSpanning Arms/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeg Length/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper Arm Circumference/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower Arm Circumference/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalf Circumference/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThigh Circumference/Cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSit Up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.680\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePush Up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative Power Double Leg (W/Kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV Sit And Reach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHandgrip/Kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHand Wall \u003c/p\u003e\u003cp\u003eTost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSideway Agilty (Sec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFour Corner Agilty(Sec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAerobic Vo2max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo confirm the results, discriminant function analysis was performed. The discriminant plot in Fig.\u0026nbsp;3 shows the distribution of players over the two extracted functions which made up 100% of the total variance (Function 1: 65.35%; Function 2: 34.65%). Within each category, clear clustering of participants was noted, and the group centroids were well separated. The R16\u0026ndash;QF and Medalist groups were distinctly positioned away from the R32 group, which illustrates how well the discriminant functions captured the physical attributes differentiating the performance tiers.\u003c/p\u003e\u003cp\u003eThe strength of the model was validated through classification accuracy results (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Out of 26 participants, 25 were accurately classified, resulting in overall accuracy of 96.15%. Both the R16\u0026ndash;QF and Medalist groups recorded perfect classification rates (100%), while the R32 group had a substantial accuracy of 93.33% with one case misclassified. The model\u0026rsquo;s high predictive validity strengthens the overall claim while depicting the agility and flexibility\u0026rsquo;s fierce discriminative strength in profiling youth badminton players relative to the competitive results.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClassification Accuracy of Discriminant Analysis Model Based on Confusion Matrix\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003efrom \\ to\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eR16 - QF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eR32\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003emedalist\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e% correct\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR16 - QF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e93.33%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emedalist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e96.15%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research aimed to construct a multivariate profiling model which classifies youth elite badminton players according to their anthropometric and fitness attributes. With Exploratory Factor Analysis (EFA), three latent components emerged: (1) Muscular Profile and Functional Leverage, (2) Aerobic Stability and Movement Control, and (3) Repetitive Stroke Capacity. These components together capture the intricate blend of the badminton specific physiological and biomechanical factors necessary to excel in the sport. Alongside this, discriminant analysis (DA) regarding performance levels showed that flexibility and agility distinguished the groups with a classification accuracy of 96.15%, exhibiting strong discriminative power.\u003c/p\u003e\u003cp\u003eMuscular Profile and Functional Leverage, the first component, was characterized by anthropometric and strength attributes of fitness like circumferences of limbs, handgrip strength, and flexibility. This aligns with prior research demonstrating that anthropometric appreciation aids in stroke range, balance, and spatial dominance in badminton \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, contrary to prior literature which focused on morphological predictors, this study showed that these attributes were unlikely to sharply differentiate competitive levels due to a homogeneous sample.\u003c/p\u003e\u003cp\u003eThe second component, Aerobic Stability and Movement Control, integrates trunk endurance, aerobic fitness, and neuromotor coordination as indicated by sit-up performance, VO₂max, and hand wall toss. These findings corroborate \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, who emphasized endurance and accuracy as critical for maintaining technical skill execution during lengthy rallies. The third component, Repetitive Stroke Capacity, driven by push-up performance, reiterates \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, who highlighted the importance of upper-body endurance for multiple overhead strokes.\u003c/p\u003e\u003cp\u003eThe robustness of the factor model suggests only flexibility (V Sit and Reach) and agility (Sideway Agility and Four-Corner Agility) differentiated performance groups. This is in alignment with \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, who emphasized agility and economical movement in relation to racket sports, and also supports \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e who highlighted the importance of speed in the change of direction in badminton. In the opposite direction, handgrip strength, other anthropometric measures, and upper body endurance did not create differentiated groups. This may stem from the biological and maturational uniformity, as proposed by \u003csup\u003e5\u003c/sup\u003e, who suggested that growing up tends to blur the predictive value structural measures have in profiling young athletes.\u003c/p\u003e\u003cp\u003eThe study\u0026rsquo;s novelty is its combination of exploratory factor analysis (EFA) and discriminant analysis (DA) to develop a profiling model with unparalleled classification precision. Prior research has either concentrated on specific physical assessments or on anthropometric predictors in isolation. This study identifies critical physiological and anthropometric predictors of competitive success and validates their predictive utility using latent component extraction and discriminant classification. This is a methodological improvement in badminton talent identification and corresponds with the movement toward evidence-based multidimensional profiling in sports science \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis research highlighted three latent constructs related to elite youth badminton performance: Muscular Profile and Functional Leverage, Aerobic Stability and Movement Control, and Repetitive Stroke Capacity. These factors derived from exploratory factor analysis (EFA) and confirmed via discriminant analysis (DA) contribute to understanding the competitive badminton\u0026rsquo;s multidimensional and complex physiological and biomechanical requirements. Of all the measured variables, flexibility and agility proved to be the strongest discriminant enabling the model to classify players accurately by 96.15%.\u003c/p\u003e\u003cp\u003eThis research is innovative in applying EFA and DA to build a robust profiling model that integrates latent structural dimensions and strong predictive accuracy. This is unlike other studies that focused on singularized anthropometric or physical characteristics of the youth athletes, reinforcing the argument for a more holistic, multi-faceted, rigorous evidence-based framework for determining competitive level among the athletes. The findings in this research emphasize agility and flexibility in talent identification and encourage these factors to be more prioritized in the training prescription and the player\u0026rsquo;s long-term development plan. The model can be leveraged by coaches and sport scientists for more effective athlete evaluation, targeted training, and for more strategically guided talent development pathways in badminton.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study did not receive any specific funding or grants.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMohammad Firdaus Mohd Israj\u0026sup1; contributed to the conceptualization of the study, data collection, statistical analysis, and preparation of the original manuscript draft. Nur Syazwani Ibrahim\u0026sup2; assisted in data curation, visualization, and manuscript editing. Nor Ikhmar Madarsa\u0026sup3; contributed to the methodology design, validation, and critical review of the results. Mohar Kassim⁴ contributed to literature review support, resources, and coordination of data acquisition. Rozita Binti Abdul Latif⁵ contributed to the review of theoretical background, editing, and refinement of the manuscript structure. Ahmad Bisyri Husin Musawi Maliki⁶ provided supervision, project administration, conceptual guidance, and final approval of the manuscript as the corresponding author. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their sincere gratitude to all participating players and their coaches for their enthusiastic commitment and engagement throughout the study. Appreciation is also extended to the National and State Badminton Development Programmes for providing access to athletes and training facilities. Special thanks are due to the research assistants and volunteers for their valuable help during data collection and logistical coordination. The authors gratefully acknowledge the Universiti Pertahanan Nasional Malaysia (UPNM) for providing institutional facilities, administrative support, and covering the publication cost of this article.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCabello Manrique, D. \u0026amp; Gonz\u0026aacute;lez-Badillo, J. J. Analysis of the characteristics of competitive badminton. \u003cem\u003eBr. J. Sports Med.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 62\u0026ndash;66 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhomsoupha, M. \u0026amp; Laffaye, G. 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Talent Identification in Sport: A Systematic Review. \u003cem\u003eSports Medicine\u003c/em\u003e vol. 48 97\u0026ndash;109 Preprint at (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40279-017-0803-2\u003c/span\u003e\u003cspan address=\"10.1007/s40279-017-0803-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"youth players, badminton profiling, principal component analysis, discriminant analysis, performance classification, anthropometric attributes","lastPublishedDoi":"10.21203/rs.3.rs-7878552/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7878552/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFor the purpose of profiling youth athletes, it is necessary to have a multidimensional understanding of the sport of badminton. With the exception of isolated traits, previous research has given little attention to the physiological and anthropometric characteristics that aid performance. Method: 26 elite youth male players aged between 12 and 16 participated in the research, and a set of validated physical and anthropometric evaluations were conducted. Exploratory Factor Analysis (EFA) determined the latent variables, while Discriminant Analysis (DA) determined the competition level classification of the players. Results: EFA identified three latent variables which accounted for 74.29 of the total variance, and these included: (1) Muscular Profile and Functional Leverage (limb circumferences, grip strength, and flexibility), (2) Aerobic Stability and Movement Control (VO₂max, sit-ups, and hand-eye coordination), and (3) Repetitive Stroke Capacity (push-up performance). DA the least flexibility and agility as discriminating variables with a 96.15% classification accuracy. Conclusion: The model, which greatly aids in athlete profiling, personalized training, and talent scouting, indicates that flexibility and agility are the foremost determinants of performance in badminton.\u003c/p\u003e","manuscriptTitle":"A Multivariate Recognition Profiling and Classifying for Youth Elite Badminton Players Based on Anthropometric and Fitness Indicators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 11:09:14","doi":"10.21203/rs.3.rs-7878552/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":"214b2d2c-4f5a-4803-8c2a-4f27ad8fbaa5","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57404832,"name":"Health sciences/Health care"},{"id":57404833,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2025-11-10T07:39:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 11:09:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7878552","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7878552","identity":"rs-7878552","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}