Hamstrings–quadriceps (H–Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico (simulation-based) study introduces a digital machine learning framework designed to estimate H–Q imbalance using biomechanical features derived entirely from synthetic running trials and conceptually mappable to inertial-sensor domains. Key biomechanical predictors included the dynamic hamstrings-to-quadriceps ratio (H:Qdyn), the knee moment limb symmetry index (LSI), and the early-stance co-contraction index (CCI), all standard indicators of muscular balance and coordination in sports medicine. A reduced musculoskeletal framework emulating flexor–extensor balance, limb symmetry, and co-contraction patterns generated 573 synthetic running trials for 160 virtual subjects across three speeds. These interpretable features trained a calibrated gradient-boosting classifier evaluated via ROC-AUC, PR-AUC, balanced accuracy, F1, and Brier score. Across all conditions, the model achieved ROC-AUC 0.933 (95% CI 0.908–0.958), balanced accuracy 0.943 (95% CI 0.924–0.962), PR-AUC 0.918 (95% CI 0.892–0.943), F1 0.940 (95% CI 0.919–0.958), and Brier 0.056 (95% CI 0.041–0.072), outperforming the logistic baseline. Dynamic H:Q ratio and knee moment symmetry were the dominant predictors, while co-contraction provided complementary biomechanical nuance. These results demonstrate that simulation-derived frameworks can reproduce IMU-relevant biomechanical variability, enabling interpretable machine learning for transparent assessment of muscular balance in sports medicine.

Hamstrings–quadriceps (H–Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico study introduces a simulation-derived machine-learning framework designed to estimate H–Q imbalance using biomechanical features conceptually mappable to inertial-sensor domains. A reduced musculoskeletal framework emulating flexor–extensor balance, limb symmetry, and co-contraction patterns generated 573 synthetic running trials for 160 virtual subjects across three speeds. These interpretable features trained a calibrated gradient-boosting classifier evaluated via ROC-AUC, PR-AUC, balanced accuracy, F1, and Brier score. Across all conditions, the model achieved ROC-AUC 0.933 (95% CI 0.908–0.958), balanced accuracy 0.943, PR-AUC 0.918, F1 0.940, and Brier 0.056, outperforming a calibrated logistic baseline. Dynamic H:Q ratio and knee-moment symmetry were the dominant predictors, while co-contraction contributed complementary nuance. These results indicate that simulation-derived digital frameworks can reproduce IMU-relevant biomechanical variability, enabling interpretable machine learning for objective assessment of muscular balance in sports medicine.

This review critically examines training and testing strategies in youth athletes, focusing on growth, maturation, and specialization. Early specialization, occurring in athletes younger than 12 years, is associated with training volumes above 15 h/week and with dropout rates of nearly 30–35% before age 15. The aim was to evaluate age- and maturity-specific protocols, identify limitations of adult-derived models, and provide evidence-informed recommendations. A structured search in PubMed, Scopus, and Web of Science between 2000 and 2025 identified 325 articles; after screening, 87 were included, with 19 addressing maturation, 16 testing, 18 training load, 15 motor skill development, and 19 specialization. Results show that growth variability, particularly during peak height velocity (10–12 y girls; 12–14 y boys), increases injury risk two- to threefold. Standardized adult testing (e.g., VO₂max treadmill) is often unreliable in adolescents, while field-based protocols such as the Yo-Yo IR1, CMJ, and FMS provide safer and more valid alternatives. Flexible periodization with deload cycles of 1–2 weeks and monitoring through RPE, GPS, and HR improves both safety and adaptation. Diversified sport participation further reduces burnout and supports long-term outcomes. Integrating maturity status into training and testing optimizes development, minimizes injury, and sustains lifelong athlete engagement.