Multivariate analysis of anthropometric determinants of training load in youth badminton

Background: Monitoring training load in youth athletes is essential for optimizing performance and reducing injury risk, yet limited research has examined how anthropometric characteristics influence load tolerance in badminton. This study investigated the association between training load measures and anthropometric profiles in competitive youth players. Methods: Fifty male and female athletes participated, with external workload captured via accelerometer sensors and anthropometric assessments conducted following standardized protocols. Louvain clustering was applied to classify players into different load groups, while multinomial logistic regression (MLR) identified key predictors of load classification. Results: Louvain clustering revealed three distinct load groups i.e., High Load (HL), Moderate Load (ML), and Low Load (LL) groups, reflecting natural patterns in external workload distribution. The MLR analysis demonstrated that height, weight, and leg length were significant predictors of load classification. Taller and heavier players were more likely to belong to the HL group, while longer leg length was positively associated with ML classification, potentially linked to stride mechanics and movement economy. Other circumferential measures (waist, hip, MUAC) showed minimal impact, and years of playing experience did not significantly predict load tolerance. Conclusion: These findings emphasize the value of combining network-based clustering with multivariate modeling to capture complex athlete load interactions. Practically, the results suggest that specific anthropometric traits particularly stature, body mass, and limb length, play an important role in shaping athletes’ ability to sustain training loads. Integrating individualized anthropometric assessment into load monitoring can support evidence-based coaching strategies that enhance performance and mitigate injury risk in developing badminton players.