## 3 Results
### Performance Metrics and CR-ICC
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Team Level**} \\ \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**AIC**} & \multirow{2}{*}{**BIC**} & \multirow{2}{*}{\(\mathbf{R^{2}_{N}}\)} & \multicolumn{2}{c}{**Global Model Test**} & \multirow{2}{*}{**Cut-Off**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**AUC**} \\  & & & \(\mathbf{\chi^{2}}\) & & \(\mathbf{g.l.}\) & \(p\)**-Value** & & & & & \\ \hline CR-ICC & 58.2 & 62.5 & 0.547 & 33.6 & 1 & \(<\)0.001 & 0.41 & 0.797 & 0.80 & 0.786 & 0.871 \\ \hline Pto & 43.0 & 47.3 & 0.714 & 48.7 & 1 & \(<\)0.001 & 0.50 & 0.859 & 0.889 & 0.821 & 0.939 \\ \hline Eff A & 45.5 & 49.8 & 0.690 & 46.2 & 1 & \(<\)0.001 & 0.51 & 0.875 & 0.889 & 0.857 & 0.930 \\ \hline Eff R & 87.4 & 95.7 & 0.006 & 0.31 & 1 & 0.575 & 0.44 & 0.547 & 0.556 & 0.536 & 0.560 \\ \hline Balance & 67.4 & 71.8 & 0.423 & 24.3 & 1 & \(<\)0.001 & 0.54 & 0.688 & 0.714 & 0.667 & 0.823 \\ \hline \multicolumn{11}{c}{**Player level**} \\ \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**AIC**} & \multirow{2}{*}{**BIC**} & \multirow{2}{*}{\(\mathbf{R^{2}_{N}}\)} & \multicolumn{2}{c}{**Global Model Test**} & \multirow{2}{*}{**Cut-Off**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**AUC**} \\  & & & \(\mathbf{\chi^{2}}\) & & \(p\)**-Value** & & & & \\ \hline CR-ICC & 1167 & 1177 & 0.042 & 27.5 & 1 & \(<\)0.001 & 0.49 & 0.575 & 0.565 & 0.584 & 0.607 \\ \hline Pto & 1179 & 1189 & 0.023 & 15.2 & 1 & \(<\)0.001 & 0.51 & 0.559 & 0.646 & 0.475 & 0.567 \\ \hline Eff A & 1173 & 1182 & 0.033 & 21.6 & 1 & \(<\)0.001 & 0.49 & 0.552 & 0.551 & 0.553 & 0.588 \\ \hline Eff R & 1194 & 1204 & \(\begin{matrix}4.45\times\\ 10^{-5}\end{matrix}\) & 0.0287 & 1 & 0.866 & 0.51 & 0.487 & 0.580 & 0.397 & 0.502 \\ \hline Balance & 1164 & 1173 & 0.046 & 30.7 & 1 & \(<\)0.001 & 0.52 & 0.569 & 0.710 & 0.434 & 0.604 \\ \hline \hline \end{tabular} Note. AIC—Akaike criterion; BIC—Bayesian criterion; R\({}^{2}\)N\(-\)R\({}^{2}\) de Nagelkerke; \(\chi^{2}\)—goodness of fit; g.l—degrees of freedom; \(p\)-value—significance value; CR-ICC—critical coefficient; Pto—difference in points scored by all players of both teams; Eff A—attack efficiency; Eff R—reception efficiency.  
\end{table} 
Table 2: Binomial logistic regression for the fit of each model and predictive analysis according to the cut-off point. Figure 2: Predictive ability of each variable, represented by the ROC curves and area under the curve (expressed as a percentage). AUC—area under the curve ROC; CR-ICC—critical coefficient; Pto—points scored by each player (errors excepted); balance—difference between points and errors; Eff A—attack efficiency; Eff R—reception efficiency. When considering individual players, all variables yielded very low prediction values close to 50%, indicating results similar to random. Notably, CR-ICC (AUC--60.7%) and balance (AUC--60.5%) exhibited the highest values, followed by Eff A (AUC--58.8%). On the other hand, the results obtained for the CR-ICC covariate based on various contextual factors (Table 3) demonstrated statistical significance (\(p<0.001\)). Furthermore, a positive estimate of 0.136 was observed, with a 95% confidence interval for OR \(>1\). This indicates that the probability of winning the set increases by 1.143 times, when the values of CR-ICC are high. Regarding the contextual factors, significant differences were found in the final sets, i.e., in the third set (\(p<0.001\)) and in the fourth and fifth sets (\(p<0.05\)), using the critical coefficient. Additionally, the competitive load variable \(CL_{CR}\), which identifies these crucial sets, also showed significance (\(p<0.05\)). The ranking analysis revealed significant differences between all teams compared to the top-ranked team (\(p<0.001\)), except for the third-ranked team.