## 3 Results  
The ANOVA analysis (random effects) found significant differences in age (F\({}_{1,29}\) = 28.531, \(p\)\(\leq\) 0.001, \(\eta\)2 = 0.496, ES = small), body mass (F\({}_{1,29}\) = 16.750, \(p\)\(\leq\) 0.001, \(\eta\)2 = 0.366, ES = small), body mass index (F\({}_{1,29}\) = 24.473, \(p\)\(\leq\) 0.001, \(\eta\)2 = 0.452, ES = small), leg length (F\({}_{1,29}\) = 8.277, \(p\) = 0.007, \(\eta\)2 = 0.222, ES = small), soccer experience (F\({}_{1,29}\) = 4.740, \(p\) = 0.038, \(\eta\)2 = 0.140, ES = small) and KF ROM (F\({}_{1,29}\) = 18.354, \(p\)\(\leq\) 0.001, \(\eta\)2 = 0.388, ES = small). The descriptive analysis found only differences between both body sides in the MIS-KE (BF10 = 539.02, \(\delta\) = 0.80 (strong)) in the 31 soccer players (Table 1). The cluster analysis obtained two groups (LPG-LBK vs. HPG-LBK) that determined the LBK cut-off point (HPG-LBK) at a distance of 30 or more meters. A total of 17 players (54.8%) exceeded this distance when kicking the ball. In Table 2, differences were found between LPG-LBK and HPG-LBK in terms of age, body mass, body mass index, leg length and KF ROM (BF10 \(\leq\) 6.33; \(\delta\) \(\geq\) 0.86 (moderate or higher)). The values in this variable are higher in the HPG-LBK players than in the LPG-LBK players. A prediction model was created based on the variables that showed differences between LPG-LBK and HPG-LBK. The model summary (enter method) showed a significant relationship between the HPG-LBK and the predictor variable age for H1 (\(\chi^{2}\)(29) = 18.481; \(p\) < 0.001). The Akaike Information Criteria and Bayesian Information Criteria for model fit were 28.204 and 31.072, respectively. The statistical value R\({}^{2}\) showed a good model fit, 60.1% for [PERSON]'s R2, 43.3% for [PERSON]'s R2 and 49.7% for [PERSON]'s R2. The age (OR = 2.867 (large); 95% CI = 0.356 to 1.751; \(p\) = 0.003) was the predictor variables in the players. The model's performance diagnostics yielded a sensitivity of 0.765 (13 out of 17) and a specificity of 1.000 (14 out of 14). \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Dominant Limb** & **Non-Dominant Limb** & **BF10** & **\$ (95\% Credible Interval)** & **Evidence for H1** \\ \hline \multicolumn{6}{c}{**Anthroometrics**} \\ \hline Leg length (cm) & \(0.67\pm 0.05\) & \(0.68\pm 0.06\) & \(0.677\) & \(-0.28\) & Anecdotal \\ \hline \multicolumn{6}{c}{**Range of motion (degrees)**} \\ \hline HE ROM & \(18.13\pm 5.02\) & \(15.84\pm 5.26\) & \(1.24\) & \(0.34\) & Anecdotal \\ HF-KE ROM & \(76.94\pm 14.12\) & \(75.32\pm 13.16\) & \(0.31\) & \(0.22\) & Anecdotal \\  & & & & (\(-0.11\), 0.57) & Anecdotal \\ HF-KF ROM & \(125.48\pm 9.95\) & \(121.94\pm 13.82\) & \(0.64\) & \(0.58\) & Anecdotal \\  & & & & (\(-0.07\), 0.63) & Anecdotal \\ KF ROM & \(90.65\pm 21.94\) & \(88.23\pm 21.55\) & \(0.47\) & \(0.16\) & Anecdotal \\  & & & & (\(-0.17\), 0.51) & Anecdotal \\ \hline \multicolumn{6}{c}{**Torque (N*m/kg)**} \\ \hline HF MIS & \(0.96\pm 0.36\) & \(0.93\pm 0.40\) & \(2.43\) & \(0.12\) & Anecdotal \\  & & & & (\(-0.21\), 0.45) & Anecdotal \\  & & & & 0.40 & Anecdotal \\  & & & & (\(0.05\), 0.77) & Anecdotal \\  & & & & & \\ KF MIS & \(0.71\pm 0.28\) & \(0.64\pm 0.26\) & \(3.41\) & \(0.43\) & Anecdotal \\  & & & & (\(0.07\), 0.80) & Anecdotal \\
**KE MIS** & **1.87 \(\pm\) 0.39** & **1.50 \(\pm\) 0.33** & **539.02** & **(0.39, 1.23)** & **Extreme** \\ \hline \end{tabular}
\end{table} 
Table 1: Differences between the two sides of the body in terms of leg length, range of motion and maximal isometric strength (torque). The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was 0.853, indicating a good predictive model accuracy for the HPG-LBK, which was statistically significant (\(p=0.001\); Standard Error: 0.73; 95% CI: 0.710 to 0.996). Using the coordinates of the curves, the age that most accurately identified individuals at HPG-LBK was determined to be 13.5 years or older (sensitivity = 94.12%; specificity = 21.43%; Youden Index = 0.155) (Figure 3).