https://revistaseug.ugr.es/index.php/IJRSS <p>The International Journal of Racket Sports Science is an open-access online journal that publishes unpublished research articles, reviews, and letters in all areas related to racket sports and sports science. The journal's primary objective is to provide a comprehensive and reliable source of information on the latest advances in this field, positioning itself as a key resource for researchers, practitioners, and students interested in the scientific study of racket sports.</p> Editorial Universidad de Granada en-US International Journal of Racket Sports Science 2695-4508 https://revistaseug.ugr.es/index.php/IJRSS/article/view/36513 <p>Badminton performance in competition is affected by many things, including motivation, training load, and the environment. Studies from the past show that intrinsic factors, such as genetics, play an important role in determining a person’s natural ability in physical performance. BDNF gene polymorphism indicates potential in predicting an individual’s athletic performance. This study investigates whether BDNF Val66Met gene polymorphisms can be used as a predictor of badminton players’ performance. This is a descriptive and retrospective cross-sectional study. Gene samples of BDNF Val66Met were collected through buccal swab genes from well-trained male badminton players. Descriptive data on the athletes’ training and injury history in the past year were obtained to analyse their injury rate and severity. Information on the physical performance test includes back and leg strength test, handgrip, vertical jump, standing long jump, and 40-meter sprint, as well as simple and choice reaction test. Data was analysed using Spearman and Pearson correlation, multinomial logistic regression, Chi-Square, and one-way ANOVA. 101 volunteers were selected and completed all the assessments in the study. A positive correlation coefficient with simple reaction time, r = 0.230, p &lt; 0.05, and choice reaction time, r = 0.223, p &lt; 0.05. Furthermore, multinominal regression indicates that the ValVal allele carrier will have a faster simple reaction time (b = -0.072, p = 0.002, p &lt; 0.05) and choice reaction time (b = -0.018, p = 0.018, p &lt; 0.05) as compared to MetMet allele. This study’s findings also indicate a positive correlation with no recurring injury (r = 0.293, p = 0.03) and no chronic injury (r = 0.221, p = 0.026). The genetic factor BDNF Val66Met gene polymorphism have a beneficial impact on certain performance metrics such as simple and choice reaction time, long jump, and vertical jump. In addition, results indicate a significant relationship with a history of injuries. These findings may be useful among badminton communities in incorporating genetic factors as performance-enhancing strategies for athletes’ training and selection processes.</p> Muhammad Iqbal Shaharudin Ahmad Munir Che Muhamed Ernest Mangantig Hazwani Ahmad Yusof Copyright (c) 2026 Muhammad Iqbal Shaharudin, Ahmad Munir Che Muhamed, Ernest Mangantig, Hazwani Ahmad Yusof https://creativecommons.org/licenses/by/4.0 2026-09-07 2026-09-07 8 2 20 30 10.30827/ijrss.36513 https://revistaseug.ugr.es/index.php/IJRSS/article/view/34417 <p>Data analysis is an essential tool to understand and improve sports performance. In badminton, a racket sport characterized by high speed, intense physical demands, and tactical complexity, matches generate a large volume of information. Spatial, temporal, and notational data, such as shot sequences, court positioning, finishing patterns, and rally characteristics, must be efficiently organized and processed to produce relevant insights. In this context, data analysis tools are indispensable to transform information into knowledge applicable to training and decision-making. This study presents practical applications of sports data analysis using Power BI and Python, showing how these tools can be applied in a complementary way. The objective is not to discuss specific analytical results but to demonstrate how different statistical and computational approaches, including interactive dashboards, dynamic charts, analysis of variance (ANOVA), logistic regression, machine learning, and clustering techniques, can be implemented and applied to badminton datasets. By combining intuitive visualizations, automated analyses, and advanced statistical exploration, the study shows how these technologies can translate complex datasets into actionable insights for coaches, athletes, and analysts, even without specialized training in data science. This approach contributes to the expansion of strategic data use in sports, bridging the gap between computational analysis and court-side application, and promotes more consistent, evidence-based practices in the technical, tactical, and physical development of the sport.</p> Leyza Elmeri Baldo Dorini Lara Morgado da Silva Santos Fabio Prado Galvão Machado Layla Maria Campos Aburachid Copyright (c) 2026 Leyza Elmeri Baldo Dorini, Lara Morgado da Silva Santos, Fabio Prado Galvão Machado, Layla Maria Campos Aburachid https://creativecommons.org/licenses/by/4.0 2026-08-24 2026-08-24 8 2 1 19 10.30827/ijrss.34417