## 2 Materials and Methods
### Statistical Analysis  
A series of EFA was performed to identify the most appropriate factor solution that better describes the underlying structure of the measured variables. The EFA is a multivariate statistical approach that groups measurement items into latent factors based on item correlations. EFA is a statistical method that enables data reduction and the summarization of fewer factors that better represent the measured indicators [44; 45]. Multivariate regression was initially conducted to explore multicollinearity among variables using the variance inflation factor as a metric (cut-off value of 3) [44]. Time-to-peak (dominant lower limb: VIF 4.515; non-dominant lower limb: VIF 6.078) was removed in this phase because of high correlation values with peak VGRF and RFD; all the remaining variables showed a VIF below the cut-off value. An exploratory factor analysis (EFA) was then conducted on hamstring strength, hamstring strength (brake test), triple hop, prone bridge, dominant-side bridge, non-dominant-side bridge, Biering-Sorensen test, peak VGRF, RDF, COP length, COP SDx, COP SDy, LESS, quadriceps strength, abductors strength, quadriceps-hamstrings (Q-H) EMG ratio pre-landing, and Q-H EMG ratio post-landing.  
The appropriateness of data for EFA was examined using the [PERSON]'s test of sphericity and KMO (Kaiser-Meyer measurements of sampling adequacy) statistics. For suitability of the data, [PERSON]'s test of sphericity statistics should be significant, and KMO values above 0.50 are acceptable [45]. The extraction method of principal component analysis and Promax rotation method were used for the EFA [45; 46]. Eigenvalues greater than 1.0 rule and scree test were followed to determine the number of factors to retain.  
Communalities measure the between-item correlation. Items with low communalities values < 0.30 are candidates for removal after examining the pattern matrix. The pattern matrix is reviewed to identify the structure of extracted factors and the load of items on each factor. A measure item was removed when extreme cross-loading among factors was observed. An item should clearly load into only one factor. If cross-loading exists, the primary loading must be at least 0.20 larger than the second loading. The analysis of the dominant lower limb yielded a high cross-loading of the LESS among core stability, hamstring strength, quadriceps strength and Q-H EMG ratio. Further, high cross-loading was yielded at abductors strength with quadriceps strength, Q-H EMG ratio, and core stability. Consequently, the decision was made to exclude these variables from the exploratory factor analysis (EFA), which was subsequently conducted without them. For the non-dominant lower limb, the LESS was removed due to low communality and high cross-loadings among core stability, dynamic balance, and strength. Further, Biering-Sorensen was highly correlated with all the factors and removed. Additionally, hamstring strength and hamstring strength (brake test) were both also highly cross-loaded among strength(abductors and quadriceps), core stability, and GRF. Thus, the EFA was performed again without them. SPSS (v. 28) was used for data analysis.