Badminton is recognized as the fastest racket sport in the world based on the speed of the birdie which can travel up to 426 km per hour. On the badminton court, players are not only required to track the moving badminton birdie (visual tracking and information integration) but also must anticipate the exact timing to hit it back (temporal estimation). However, the association of training experience related to visuomotor integration or temporal prediction ability remains unclear. In this study, we tested this hypothesis by examining the association between training experience and visuomotor performances after adjusting for age, education, and cardiovascular fitness levels. Twenty-eight professional badminton players were asked to perform a compensatory tracking task and a time/movement estimation task for measuring visuomotor integration and temporal prediction, respectively. Correlation analysis revealed a strong association between training experience and performance on visuomotor integration, indicating badminton training may be promoted to develop visuomotor integration ability. Furthermore, the regression model suggests training experience explains 32% of visuomotor integration performances. These behavioral findings suggest badminton training may facilitate the perceptual–cognitive performance related to visuomotor integration. Our findings highlight the potential training in visuomotor integration may apply to eye–hand coordination performance in badminton sport.

The use of video analysis in sports training has revolutionized the way coaches and players evaluate performance and develop strategies. This paper presents a machine learning based approach for key frame detection in badminton swings aimed at improving the learning experience for beginners through visualization and real-time feedback. Our proposed method uses the MediaPipe framework to extract 3D coordinates of skeleton joints, which serve as input features for a machine learning based model that accurately predicts key frame positions in badminton swing videos. This model is integrated into a mobile app developed for Android tablets, allowing learners to record their swings and compare them with those of professional players, thereby enhancing badminton learning. Comparative studies show that models with graph convolutional networks, a prominent approach commonly used in skeleton-based human action recognition, outperform other existing methods in terms of accuracy and reliability. Experimental studies demonstrate the app’s positive impact on performance, motivation, and self-perception. One main contribution of this research is the development of a robust key frame detection model. Another main contribution is that we implement this key frame detection model in a tablet and verify it through extensive experiments.

Background/Objectives: Top badminton players must carefully schedule tournaments to perform well and improve their rankings. This study examines how players plan their tournament participation and whether their scheduling strategies affect their rankings and performance, especially during Olympic and non-Olympic years. Methods: Data were extracted from the Badminton World Federation (BWF) ranking system for the top 50 men’s and women’s singles players from May 2014 to May 2019. A computer-based simulation model and cluster analysis were applied to analyze tournament participation patterns, recovery intervals, and performance trends. Statistical analyses were conducted to identify correlations between these variables and ranking outcomes. Results: Top-ranked players participated in more tournaments during Olympic years, with those competing in more than nine tournaments achieving better results. Players performing well in higher-graded tournaments tended to take longer recovery breaks, whereas lower-performing players entered more tournaments. Cluster analysis revealed two distinct performance groups, with the top 20 men’s and top 12 women’s singles players adopting different strategies compared to lower-ranked counterparts. Conclusions: Strategic scheduling of tournaments, including balancing participation frequency and recovery intervals, is vital for optimizing performance and achieving sustained ranking success. These findings emphasize the importance of high-graded tournament selection and tailored schedules for elite players.

The road to the top is never easy. This study investigated whether the career trajectories of top-level men’s and women’s badminton players could be predicted by their age at first major debut and the time taken to reach the top 150. Data from the BWF from October 2009 to October 2022 were analyzed using a predictive linear regression model with Bayesian inference, adjusting for anthropometric factors. The results suggested that age at debut influences career high rankings among over 120 elite players. Additionally, observations challenged the Matthew effect in early-career success for young players, proposing that inherent talent may be more significant than early nurturing. The study also examined the potential impact of relative age and early specialization in sports.