## 3 Results (First Collection)
### Primary Outcomes  
For linear regression analysis, the prerequisites of linearity between the predicted and predictor variables, as well as homoscedasticity and normality of distribution of residuals, were accepted. The regression model for participants with high strength scores did not predict vMART performance for either the DL (r = 0.193; R\({}^{2}\) = 0.037; F(1,15) = 0.581; \(p\) = 0.458) or DL/BM (r = 0.057; R\({}^{2}\) = 0.003; F(1,15) = 0.048; \(p\) = 0.829) exercise or the SQ (r = 0.106; R\({}^{2}\) = 0.011; F(1,15) = 0.170; \(p\) = 0.686) or SQ/BM (r = 0.076; R\({}^{2}\) = 0.006; F(1,15) = 0.087; \(p\) = 0.772) exercise. Thus, the DL RM test does not explain the variations in vMART performance, suggesting that strength from a certain level does not influence anaerobic running performance. Conversely, linear regression demonstrated a positive result for predicting vMART performance in the face of lower strength scores. The regression model presented significant results for DL (r = 0.694; R\({}^{2}\) = 0.482; F(1,15) = 13.95; p = 0.002), DL/BM (r = 0.874; R\({}^{2}\) = 0.764; F(1,15) = 48.493; \(p\) < 0.001) and SQ (r = 0.597; R\({}^{2}\) = 0.357; F(1,15) = 8.328; \(p\) = 0.011) and SQ/BM (r = 0.803; R\({}^{2}\) = 0.644; F(1,15) = 27.154; \(p\) < 0.001). Figure 1A,B present the regression plots for vMART performance for DL movement and Figure 2A,B present the regression analyses between vMART and SQ. Regarding aerobic power analysis, the linear regression also did not demonstrate a significant association between the movements of DL (r = 0.158; R\({}^{2}\) = 0.025; F(1,15) = 0.384; \(p\) = 0.545), DL/BM (r = 0.265; R\({}^{2}\) = 0.070; F(1,15) = 1.123; \(p\) = 0.306), SQ (r = 0.123; R\({}^{2}\) = 0.018; F(1,15) = 0.267; \(p\) = 0.613), SQ/BM (r = 0.102; R\({}^{2}\) = 0.010; F(1,15) = 0.157; \(p\) = 0.698) and V\({}_{\rm Peak}\) for participants with high training scores. The regressive model using DL movement (r = 0.467; R\({}^{2}\) = 0.218; F(1,15) = 4.192; \(p\) = 0.059) to predict V\({}_{\rm Peak}\) did not show statistical significance. In contrast, SQ was able to predict V\({}_{\rm Peak}\) performance (r = 0.555; R\({}^{2}\) = 0.309; F(1,15) = 6.692; \(p\) = 0.021). When we used allometric relativization, the model was also positive for DL/BM (r = 0.586; R\({}^{2}\) = 0.343; F(1,15) = 7.841; \(p\) = 0.013) predicting V\({}_{\rm Peak}\) performance. Finally, the regression of V\({}_{\rm Peak}\) by the predictor variable SQ/BM was positive, presenting significant results between the measured and predictor variables (r = 0.786; R\({}^{2}\) = 0.618; F(1,15) = 24.264; \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**High Strength Score**} & \multicolumn{4}{c}{**Low Strength Score**} \\ \hline  & **DL** & **DL/BM** & **SQ** & **SQ/BM** & **DL** & **DL/BM** & **SQ** & **SQ/BM** \\ \hline  & **(kg)** & **(kg/kg)** & **(kg)** & **(kg/kg)** & **(kg)** & **(kg/kg)** & **(kg)** & **(kg/kg)** \\ \hline Mean & 145.3 & 2.0 & 111.1 & 1.5 & 105.4 * & 1.4 * & 86.3 * & 1.1 * \\ SD & 21.6 & 0.1 & 16.4 & 0.3 & 27.9 & 0.3 & 22.7 & 0.2 \\ \hline \hline \end{tabular} SD = standard deviation; DL = deadlift; SQ = squat; DL/BM = deadlift load relative to body mass; SQ/BM = squat load relative to body mass; * significant differences between the group with the lowest strength score and the highest strength score.  
\end{table} 
Table 2: Stratification by strength level in DL and SQ movements, relativized or not by BM. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**High Strength Score**} & \multicolumn{4}{c}{**Low Strength Score**} \\ \hline  & **VO\({}_{2}\)Max** & **V\({}_{\rm Peak}\)** & **VO\({}_{2}\)** & **MART** & **VO\({}_{2}\)Max** & **V\({}_{\rm Peak}\)** & **VO\({}_{2}\)** & **vMART** \\ \hline  & **mL**-**kg**\({}^{-1}\)**-**min**\({}^{-1}  
\(p<0.001\)). Figure 3A,B show the regression graphs for the V\({}_{\rm Peak}\) performance for both investigated movements.