## 2 Materials and Methods
### Statistical Analysis  
IBM SPSS Statistics v26.0 (IBM Corp., Armonk, NY, USA) was employed for conducting statistical tests and GraphPad prism v.9 (San Diego, CA, USA) was used for graphing. Data from preseason testing exhibited a normal distribution by using Shapiro-Wilk normality test. Participants exhibiting positive, negative, or no adaptations to training were identified based on the absolute change compared to the smallest worthwhile change (SWC = 0.2 - between-subject SD). A repeated-measures within-factors MANOVA test was carried out using Evaluation and Sex as predictive factors and BMI as covariate. A paired _t_-test was employed to detect the differences between pre- and mid-season testing. The magnitudes of estimated effects (Cohen's d) were calculated between the assessments and categorized as follows: \(\leq\)0.2 trivial, \(\geq\)0.2-0.6 small, \(\geq\)0.6-1.2 moderate, \(\geq\)1.2-2.0 large, and \(\geq\)2 very large [16]. The significance level was set at \(p\) < 0.05. The disequilibria of SNPs were estimated using the Hardy-Weinberg equilibrium (HWE) followed by the approach proposed by [PERSON] and [PERSON] [17]. The probability of having an optimal genotype for these polymorphisms was calculated using the \(\chi^{2}\) test with a fixed \(\alpha\) error of 0.05. To determine what genotype was associated with an unexpected distribution, the standardized residuals were calculated based on the difference between the observed and the expected values. Briefly, within each variable, a genotype was considered to have a statistically different distribution from the expected value when its distribution was greater than or less than the critical Z-score value (i.e., 1.96). The genotypic frequencies of the polymorphisms were compared between the responders' and non-responders', using a \(\chi^{2}\) test. Post hoc power analysis was carried out using free software (G*Power v3.1). With a sample size of 37 participants, assuming an alpha error of 0.05 and an effect size of 0.38 based on the mean and standard deviation of both assessments of the CMJ height and Pearson correlation between measurements of 0.92, the statistical power achieved was 0.982.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Symbol** & **Gene** & **dbSNP** & **Genomic** & **MAF Football** & **MAF (BIS) * & **HWE** & **FIS** \\ \hline _ACE_ & Angiotensin-converting enzyme & rs4646994 & 17293.3 & 21.6\% (I) & 36.7\% (I) ** & \(p\) = 0.089 & 0.53 \\ _ACTN3_ & alpha-actin-3 & rs1815739 & 11413.2 & 52.1\% (I) & 43.9\% (I) & \(p\) = 0.163 & -0.31 \\ _AMPD1_ & Adenosine monophosphate deaminase 1 & rs17602729 & 1913.2 & 16.2\% (I) & 14.0\% (I) & \(p\) = 0.844 & -0.08 \\ _CKM_ & Muscle-specific creatine kinase & rs1811989 & 19413.2 & 24.3\% (G) & 26.6\% (G) & \(p\) = 0.895 & -0.06 \\ _MLCK_ & Myosin light chain kinase & rs28497577 & 3q21.1 & 5.4\% (A) & 10.3\% (A) & \(p\) = 0.348 & 0.23 \\ _MLCK_ & Myosin light chain kinase & rs2700352 & 3q21.1 & 16.2\% (I) & 20.1\% (I) & \(p\) = 0.521 & 0.19 \\ Overall _SNPs_ & & & & \(p\) = 0.476 & 0.11 \\ \hline \hline \end{tabular}  
* IBS, Iberian population in Spain *; ** FIS, inbreeding coefficient; HWE, Hardy–Weinberg equilibrium; MAF, minor allele frequency; SNP, single-nucleotide polymorphism.  
\end{table}
Table 2: Genomic location and minor allele frequency (MAF) for selected genes in muscle injuries.