The explosive nature and fast-paced gameplay in badminton often leave players with reduced ground contact preparation time after executing a jump smash. This study aims to investigate the relationship between jump height, smash performance (shuttlecock speed, racket speed, smash angle, shuttlecock-contact height, absolute shuttlecock-racket impact location), and landing parameters (ground reaction force, impulse, landing time, lower limb joints kinematics) during badminton jump smashes. Kinematic and kinetic data of 21 male sub-elite junior badminton players were collected simultaneously for each jump smash trial using 18 Qualisys cameras and 3 Kistler force plates. Pearson’s correlation analysis revealed that higher jumps increased shuttlecock-contact height (p=0.020) and resulted in steeper shuttlecock angles (p=0.025), enhancing smash effectiveness. However, greater jump heights also correlated with higher peak landing vertical ground reaction forces (absolute, p=0.013; normalised, p=0.023), shorter time to peak force (p=0.013), and greater landing impulse at 50 ms (p=0.003), indicating increased injury risk due to higher impact loads. Players achieving greater jump heights landed with a more extended knee (p=0.023), greater knee external rotation (p=0.001), and increased lateral trunk flexion toward the racket side (p=0.001). The findings suggest that while higher jumps improve smash performance by allowing steeper shots, they also place knee at more extended position and potentially impose greater stress on the lower limbs during landing. Coaches should prioritise proper landing techniques to mitigate injury risks while maintaining performance benefits. Future research should explore the landing mechanics for different types of smashes i.e., jump-out smash to mitigate injury risks.
This study utilized the “double three-phase” method to analyze the technical and tactical performance of elite table tennis player Wang Chuqin during the 2023 World Table Tennis Championships. A total of 16 matches were analyzed using video observation to record tactical statistics across three match stages (opening, middle, and final) and three technical phases (serve-and-attack, receive-and-attack, and rally). The analysis revealed that Wang Chuqin exhibited a consistently high scoring rate in the serve-and-attack phase across all match stages. In the opening stage, while rally play had the highest usage rate, the serve-and-attack phase yielded the highest scoring efficiency. During the middle and final stages, his primary scoring weapon remained the serve-and-attack, characterized by high-quality spin and varied placement that pressured opponents. However, his performance in rallies was more passive, particularly in high-stakes matches where his scoring rate declined significantly when forced into extended exchanges. Wang Chuqin is classified as a highly effective offensive player whose success is heavily predicated on the first three strokes. The double three-phase method proved to be a systematic tool for establishing a performance baseline and identifying specific tactical vulnerabilities in elite competition.
This study aimed to compare body composition and aerobic capacity across sex and competitive levels, and to examine the relationships between body composition (BMI and fat percentage) and aerobic capacity (VO₂ max) among sub-elite youth badminton athletes. This research utilized a cross-sectional design, sampling on state-level athletes with (males n=22; females n=15); aged (10–16 years). The athletes underwent anthropometric measurements for the BMI, fat percentage and aerobic testing (VO2 max estimated via 2.4 km Cooper Run). The results revealed a significant gender disparity with males exhibiting higher VO2 max (52.37 vs 48.12 ml/kg/min) and lower fat percentages (11.73% vs. 22.50%) compared to females. A moderate positive correlation between BMI and VO2 max was observed in males (r = 0.412, p = 0.029), but no significant associations emerged for fat percentage or among females. The competitive levels (Podium, Elite, Trial) did not show any significant associations with body composition in relation to VO2 max, with podium athletes displaying the highest aerobic capacity (52.56 ml/kg/min) despite marginally higher BMI and fat percentage. These findings suggest that while sex and competitive level influence aerobic performance, body composition variables play showed a limited association with aerobic capacity among youth badminton athletes. In conclusion, the study highlighted the Sport-specific training adaptations and physiological factors may exert a stronger influence on aerobic fitness than anthropometric characteristics alone.
Tennis-related elbow pain is commonly attributed to lateral epicondylitis, although several other disorders may produce similar symptoms, including medial epicondylitis, olecranon bursitis, and peripheral nerve loading or entrapment syndromes. The aim of this paper is to analyze painful elbow conditions in tennis players that may be mistaken for lateral epicondylitis and to identify possible biomechanical and technical trigger mechanisms associated with tennis practice. The study is based on a narrative analysis of the literature, integrated with considerations from functional anatomy and tennis technique. Particular attention is given to movement patterns that may increase tensile, shear, compressive, or neural stress at the elbow. From a practical perspective, the findings highlight the importance of differential diagnosis and suggest that technical optimization, load management, and multidisciplinary evaluation may contribute to reducing the risk of elbow pathologies in tennis players, especially in amateur athletes.
Recent advances of artificial intelligence, data science, sensors and wearable technologies have fostered the emergence of an increasing number of tools, services and approaches for collecting and processing sports data by promising information that can be used to improve athletes’ performance in a variety of contexts. Recent sports research publications indicate a trend on topics related to artificial intelligence (AI) and computer vision for data acquisition and analysis. However, this trend also raises questions from practitioners and researchers that are not familiar with the underlying concepts and technologies of such tools and approaches since their experience and education are mostly focused on the sports domain. Some of such questions are: what is the difference between these aforementioned technologies and techniques? What are common mistakes and caveats when trying to apply them in practice? Which of them better fits to overcome the challenges related to sports science from data collection to its analysis? With those questions in mind, this paper aims to provide an overview of the fundamental concepts of artificial intelligence and statistical data analysis, with a particular focus on their application to badminton-related challenges. We cover from classic statistics-based analysis to state-of-the-art deep learning models, addressing issues such as data acquisition and processing, and the limitations of each technique. We also discuss the interpretation of complex data outputs, since users must be aware of the limitations and potential biases of the algorithms to ensure that the insights provided by the results are relevant. We aim this knowledge can empower sports professionals to make informed decisions and effectively leverage technology to improve athletic performance and sports organizations.
