Background: Blood flow restriction (BFR) combined with high-intensity interval training (HIIT) is increasingly recognized as an effective strategy for enhancing aerobic capacity and muscle strength in athletes. However, there is no consensus on the effects of BFR combined with HIIT on anaerobic capacity. Objective: This study aims to examine the effects of BFR combined with Tabata training (BFR-Tabata), a type of HIIT, on anaerobic capacity in male badminton players. Methods: Thirty male badminton players (age: 20.4 ± 1.2 years) were randomized to the BFR-Tabata group (n = 15) or the Tabata group (n = 15). Both groups performed 6 weeks (3 times per week) of Tabata training (20 s maximal effort/10 s rest x 8 sets x 4 rounds). Pre and post-intervention assessments included a 30-s Badminton-specific endurance test, countermovement jump (CMJ), squat jump (SJ), and Wingate anaerobic test (peak power [PP], average power [AP], fatigue index [FI], time to peak [TTP]). A two-way repeated measures ANOVA (time × group) was used to analyze training effects, with Bonferroni post hoc tests. Effect sizes were reported as partial eta squared ( η p 2 ) or Cohen’s d, with significance set at p < 0.05. Results: The BFR-Tabata training intervention significantly improved anaerobic endurance among male badminton athletes. In comparison to the Tabata group, the BFR-Tabata group exhibited statistically significant differences in badminton-specific endurance (p = 0.02, d = 0.92, moderate effect) and multiple anaerobic performance indicators assessed by the Wingate test, including PP (p = 0.01, d = 1.11, moderate effect), AP (p < 0.01, d = 1.51, large effect), and TTP (p < 0.01, d = 2.10, very large). However, no statistically significant difference was observed in lower limb explosive strength measures, including CMJ (p = 0.50, d = 0.25, small effect), SJ (p = 0.56, d = 0.21, small effect), or during the Wingate test the FI (F = 0.138, p = 0.71, η p 2 = 0.005, trivial effect). Conclusion: Blood flow restriction combined with Tabata training is an effective strategy for improving anaerobic capacity in male badminton players, but it has limited enhancement of lower-limb explosive strength.

Backcourt forehand clear stroke is a fundamental technique in badminton, but the biomechanical differences between novice and experienced players are not fully understood. This study aimed to compare the kinematic characteristics of body segment acceleration during the backcourt forehand clear stroke between these two groups. Forty-five participants (22 novice players with less than 2 years of experience and 23 experienced players with over 5 years of competitive experience) were placed with wireless accelerometers on key body segments (foot, calf, thigh, hip, shoulder, upper arm, forearm). Each participant performed 5 backcourt forehand clear strokes, and acceleration data were collected at 100 Hz. The mean values from the 5 repetitions were used for statistical analysis to ensure reliability and reduce variability. The results revealed significant differences in movement patterns between the groups. Novices exhibited greater ranges of motion (ROMs) in most body segments, particularly at the hip, thigh, calf, and shoulder, across the x, y, and z axes (p < 0.05). Additionally, novices showed higher maximum and minimum accelerations (p < 0.05). These findings suggest that experienced players achieve more efficient movement patterns through refined motor control and reduced excessive motion. This research provides valuable insights for coaching interventions and technique refinement in badminton training programs, particularly for developing players.

Quantifying external load using kinematic variables from inertial devices provides crucial insights into player performance. This study analyzed load variables in men's singles badminton matches, differentiating between set outcomes (winners vs. losers) across different score gaps (0-5, 6-10, >10 points). Data were collected from 18 highly trained players (110 sets) using the Catapult Vector S7 microtechnology units, housed with a 100hz accelerometer, gyroscope and magnetometer. The results indicated that set winners exhibited a lower player load (estimated difference [ED] and 95% CI = -14.1 [-26.20, -1.78], p = 0.023), covered less distance (ED = -134.84 [-248.99, -17.01], p = 0.02), performed fewer explosive efforts (defined as the sum of high-intensity accelerations, decelerations, and changes of direction [COD]) (ED = -19.75 [-31.85, -7.33], p = 0.002), CODs (ED = -21.02 [-34.18, -7.67], p = 0.003), and accelerations (ED = -7.03 [-13.16, -0.73], p = 0.03) than set losers. Notably, when the score gap was narrow (0–5 points), set winners performed more explosive efforts and CODs than set losers (adjusted p = 0.0412 and 0.0499, respectively). However, as the score gap widened (6–10 and >10 points), set winners exhibited fewer explosive efforts and CODs (all adjusted p < 0.05). Furthermore, when the score gap exceeded 10 points, set winners demonstrated a lower player load, covered less distance, and performed fewer right-side CODs (all adjusted p < 0.05). These findings suggest that winners generally have a lower external load in men's singles badminton matches. However, when opponents are evenly matched, on-court movement may play a pivotal role in determining the outcome.

Purpose: This study compared the effects of flywheel resistance training (FRT) versus traditional resistance training (TRT) on reactive strength, dynamic balance, and sport-specific change-of-direction (COD) ability in elite badminton players. Methods: Twenty-four athletes (mean age: 21.2 ± 2.2 years) were randomly allocated to an FRT group (n = 12) or a TRT group (n = 12). Over 6 weeks, both groups performed twice-weekly sessions of squats, deadlifts, and lunges at a rating of perceived exertion ≈8. Performance was assessed before and after the intervention using the SEMO agility test, modified 5–10-5 COD test, 10-m sprint, reactive strength index (RSI), and Y-Balance test. Data were analyzed with a two-way mixed ANOVA (p < 0.05). Results: Mixed ANOVA revealed significant main effects of time for the SEMO test, modified 5–10-5 COD test, and 10-m sprint (all p < 0.001, η ² = 0.56–0.79), indicating performance improvements at post-test in both training groups. No significant group × time interactions were observed for these variables ( p = 0.07–0.23). Within-group effect sizes for these speed and COD measures were large in the FRT group (SEMO: Cohen’s d = 1.01; 5–10-5: d = 1.21; 10-m sprint: d = 1.27) and ranged from small to large in the TRT group (d = 0.40–0.87). For RSI, both the main effect of time and the group × time interaction were significant ( p < 0.001, η ² = 0.90 and 0.66, respectively). Within-group effect sizes were large in the FRT group (d = 2.43) and moderate in the TRT group (d = 0.79). Y-Balance Test improved over time for both dominant and non-dominant limbs ( p < 0.001, η ² = 0.53–0.57), with no significant group × time interaction ( p > 0.75). Conclusion: These findings suggest that FRT may provide additional advantages over TRT in enhancing reactive strength and COD ability, likely due to eccentric overload-induced neuromuscular adaptations. However, both training modalities were similarly effective in improving dynamic balance. These findings have practical implications for coaches: incorporating twice-weekly FRT into badminton-specific conditioning may optimize deceleration mechanics and multidirectional COD and improve transfer of strength gains to on-court performance.

This study investigated differences in energy-expenditure (EE) modeling between badminton players of varying competitive levels during aerobic training. It evaluated the impact of sensor quantity and sample size on prediction model accuracy and generalizability, providing evidence for personalized training-load monitoring. Fifty badminton players (25 elite, 25 enthusiasts) performed treadmill running, cycling, rope skipping, and stair walking. Data were collected using accelerometers (waist, wrists, ankles), a heart rate monitor, and indirect calorimetry (criterion EE). Multiple machine learning models (Linear Regression, Bayesian Ridge Regression, Random Forest, Gradient Boosting) were employed to develop EE prediction models. Performance was assessed using R², mean absolute percentage error (MAPE), and root mean square error (RMSE), with further evaluation via the Triple-E framework (Effectiveness, Efficiency, Extension). Elite athletes demonstrated stable, coordinated movement patterns, achieving the best values for R² and the smallest errors using minimal core sensors (typically dominant side). Enthusiasts required multi-site sensors to compensate for greater execution variability. Increasing sensors beyond three yielded no performance gains; optimal configurations involved 2–3 core accelerometers combined with heart rate data. Expanding sample size significantly enhanced model stability and generalizability (e.g., running task R² increased from 0.49 (N = 20) to 0.95 (N = 40)). Triple-E evaluation indicated that strategic sensor minimization coupled with sufficient sample size maximized predictive performance while reducing computational cost and deployment burden. Competitive level significantly influences EE modeling requirements. Elite athletes are suited to a “low-sensor, small-sample” scenario, whereas enthusiasts necessitate a “multi-sensor, large-sample” strategy.