## 6 Conclusions  
Maximum strength levels did not show a significant relationship between aerobic and anaerobic performance for individuals with high strength levels. However, for participants with lower strength scores, the associations between maximum and relative strength of DL and SQ were significant. The strength for both DL and SQ exercises relative to BM showed higher coefficients of determination compared to the predictive model by absolute external load. It is worth noting that the DL movement had greater predictive prominence for the anaerobic MART, explaining 76% of the variations in running performance. In contrast, for aerobic performance, SQ movement relative to BM explained the variations in running performance better.  
Despite the apparent feasibility of using the sum of DL and SQ workloads as a predictor of running performance, these were not superior to the individual predictive model. The ICC analysis indicated excellent stability of the measurement (ICC = 0.99) between the sessions performed for the DL movement.  
Finally, cross-validation showed significant accuracy of DL/BM to estimate MART performance, but the same did not occur for V\({}_{\text{Peak}}\) estimates, presenting significant divergences.  
In practical terms, if coaches, physiologists and running enthusiasts wish to promote an improvement in running performance, both aerobic and anaerobic, we suggest introducing the deadlift and squat exercises into their training programs, but with light loads. Regarding the use of strength loads to predict running performance, we suggest using models normalized by body mass, which presented higher coefficients of determination.  
Specifically for anaerobic running, the deadlift exercise proved to be more robust, while for aerobic running, the squat exercise appears to be a better predictor. This is unique knowledge and can be applied to runners whose goal is to improve, evaluate and monitor performance. We suggest that future research investigate the associations tested in the present study in different populations and, therefore, develop new equations to predict running performance, based on strength variables.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15020693/s1](https://www.mdpi.com/article/10.3390/app15020693/s1)]([https://www.mdpi.com/article/10.3390/app15020693/s1](https://www.mdpi.com/article/10.3390/app15020693/s1)), Figure S1: Bland-Altman concordance analysis for vMART performance, real vs. predicted; Figure S2: Bland-Altman concordance analysis for VPeak performance, real vs. predicted.  
Conceptualization: M.M.S. and A.S.S.F.; Methodology: J.O.F., I.O.-S. and P.S.L.; Document validation and statistical analysis: A.S.S.F., R.M.C., V.A. and G.R.C.; Data collection and preliminary writing: R.M.C. and P.A.I.; Supervision: A.S.S.F., M.M.S., P.S.L. and R.A.B.L.-M.; Different review steps: R.P.V., G.R.C. and V.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Evangelical University of Goias, approval number: #3.858.767-CAAE: 26916819.9.0000.5512--approved in 2022.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study. Written informed consent was obtained from the patient(s) to publish this paper.  
Data are contained within the article.  
The authors declare no conflicts of interest.