## 4 Discussion  
This study aimed to evaluate the impact of neuromuscular asymmetries on hop test performance in elite junior badminton players and to assess the effectiveness of a novel biofeedback-based training intervention. In line with this, we formulated five research hypotheses: that inter-limb asymmetries in impulse, RFD, and average force would negatively correlate with jump performance metrics (H1-H3); that the experimental group (EG) would exhibit greater improvements than the control group (CG) post-intervention (H4); and that derived asymmetry indices such as FER and AII would be strong predictors of adaptation (H5). The findings support H4, with EG demonstrating statistically significant improvements in jump height, RSI, and peak power, with medium-to-large effect sizes, whereas CG showed no changes across these metrics. These results align with previous research on the efficacy of asymmetry-targeted training in reducing functional deficits and improving lower-limb explosiveness [1; 15; 25]. The effectiveness of the intervention is further substantiated by reduced coefficient of variation (CV) values for RSI and Jump Height in EG, indicating greater consistency and motor control after training. These improvements underscore the relevance of inter-limb asymmetry correction protocols in elite youth populations [3; 26]. Despite these promising functional outcomes, results did not confirm H1-H3. Correlational analyses revealed negligible or non-significant relationships between traditional  
Figure 3: (**A**) Receiver operating characteristic (ROC) curve of Random Forest model using FER and AII (median-split ARSI classification). (**B**) Confusion matrix for responder classification using Random Forest and LOOCV. asymmetry indices and performance variables. These findings challenge earlier reports suggesting that asymmetries beyond 10% negatively impact functional outputs [1,2]. Instead our results emphasize the variability and contextual dependence of asymmetry-performance links. This may reflect population-specific compensatory strategies or the limitations of single-metric asymmetry indicators [4]. The stratification analysis further corroborated this interpretation, as performance metrics did not differ significantly between High and Low Asymmetry subgroups. This prompted a shift toward multivariate modeling. While PCA reduced dimensionality effectively (explaining over 70% of variance), subsequent LASSO regression revealed no meaningful predictive weights for the extracted components. This failure of linear dimensionality reduction to capture performance variability supports the growing recognition that neuromechanical outputs emerge from nonlinear, multidimensional systems [22]. As such, reliance on linear regressors in elite cohorts may obscure complex adaptive patterns. The use of machine learning models to classify individual responsiveness represents a methodological innovation. Although the Random Forest model evaluated via LOOCV yielded poor predictive accuracy (AUC = 0.50), the attempt highlights the challenge of small-sample classification in elite sport science. Importantly, our classifier used derived asymmetry constructs (FER and AII), which in prior literature have been shown to outperform raw asymmetry metrics due to their capacity to integrate magnitude and direction [5]. However, the current study demonstrates that even such indices may not generalize across all populations without sufficient sample heterogeneity or training duration. Nonetheless, our application of LOOCV, advanced preprocessing (e.g., winsorization, log-transformation), and open-source software workflows (R, Jamovi, KNIME, Orange) exemplifies high standards in reproducibility and analytical transparency [21]. This computational rigor enhances the credibility of null results and provides a valuable template for similar investigations.