## 5 Discussion
Since the movement of SQ, as well as its variations in the face of different manifestations of strength, appears in the literature as an optional evaluation measure and predictive mechanism [16; 17; 18; 19], our results are in line with this, presenting a coefficient of determination capable of explaining 35% of the variations in anaerobic performance when we consider the gross external load (R\({}^{2}\) = 0.357), increasing its associative capacity to 64% when relativized by BM\({}^{1.0}\) (R\({}^{2}\) = 0.644). The prediction of\(\rm V_{Peak}\), however, only presented associative potential when relativized by BM1.0 (SQ/BM vs. \(\rm V_{Peak}-R^{2}=0.614\)), explaining 61% of the variations in aerobic performance. Furthermore, apparently, in a way that we are unaware of having been performed before, we based our secondary hypothesis on the perspective that the combination of external loads in the DL and SQ movements could better predict both performances compared to the measurement of strength alone. This justification originated from the idea that both movements produce specific (higher) torque on a given joint, as suggested by [PERSON] et al. [25]. However, our hypothesis H2 was refuted, because despite the viability of its use as a performance predictor mechanism, the sum of strength measurements did not show superiority in the predictive model of anaerobic (R2 = 0.45) and aerobic (R2 = 0.27) performance compared to the strength models alone, therefore perhaps making it unnecessary to use two distinct assessments. Comparison with the literature proved to be complex given its unique condition, since we do not have similar studies that carried out the force summation strategy. However, it is possible to make inferences about prediction analyses in different modalities, such as in CrossFit. Previous studies analyzed the sum of maximum strength variables in DL, \"clean\" (snatch) and \"press\" movements (any overhead pushing movements) and observed that the sum of the variables produced better predictive responses than the exercises alone, distancing themselves from our results [35]. The authors also observed a negative correlation between \(\rm VO_{2}Max\) (r = \(-\)0.79; \(p=0.001\)), the maximum load lifted in the clean movement (r = \(-\)0.86; \(p=0.001\)), squat (r = \(-\)0.65; \(p=0.008\)) and front squat (r = \(-\)0.79; \(p=0.002\)) and the sum of the absolute workloads (r = \(-\)0.78; \(p=0.001\)) with performance in the Open 15.5 workout [36]. Additionally, [PERSON], [PERSON] and [PERSON] [37; 38] are aligned with these statements, highlighting that the sum of the strengths of their personal records (PRs) in the main basic movements can be used as a measure of success to predict physical performance. Our hypothesis H3 only reinforces the perspective observed on the subject in the literature. Reliability analysis has been extensively discussed for years, and we already know that although it varies from study to study, or from movement to movement, [PERSON], [PERSON], [PERSON] and [PERSON] [39] admit in their systematic review that, in view of 32 studies on the test-retest reliability of the 1 RM assessment (n = 1595), they observed that 92% of the studies with moderate to excellent methodological quality presented ICC \(\geq\) 0.90. In our study, the results were not different from the literature, presenting excellent stability for the DL movement (CCI = 0.99; 117.2 \(\pm\) 32.7 kg vs. 120.2 \(\pm\) 34.4 kg), as well as for the SQ movement (CCI = 0.99; 91.4 \(\pm\) 24.6 kg vs. 93.2 \(\pm\) 22.3 kg). Finally, the second part of our study aimed to perform cross-validation of the generated equations. We believed that the equations would show a significant association between the actual and predicted measures (H1). However, our hypothesis was partially accepted. The predictive equations using DL and DL/BM showed a strong association between the actual and predicted MART performance measures (r = 0.766 and r = 0.757 \(-\)\(p=0.001\), respectively), with an estimated measurement error of 5.5 and 6.0%, respectively.