## 3 Results
### Logistic Regression Analysis
Total distance covered exhibited a positive association with winning (OR = 1.15, 95% CI [1.008, 1.27], \(p\) = 0.037 in M\({}_{9}\)), which may reflect the physical effort and work rate associated with success. Receptions in final third were negatively related to winning in the final model (OR = 0.985, 95% CI [0.973, 0.998], \(p\) = 0.020), suggesting that, quantitatively, each additional reception in this zone was linked to a 1.5% decrease in the odds of winning. In practical terms, an increase of 10 receptions corresponded to a 14% lower likelihood of victory. This suggests that frequent entries into advanced areas, without productive final actions, may reflect ineffective possession in the final third. Other variables, such as defensive line breaks, forced turnovers, crosses, and zone 4 sprinting, did not achieve statistical significance throughout the modeling process. Tolerance and variance inflation factors (VIFs) indicated acceptable multicollinearity among included predictors, with VIF values ranging from 1.55 to 3.93, suggesting no severe collinearity issues that would compromise model estimates. The final model exhibited satisfactory classification metrics, with an overall accuracy of 80.2%, an area under the ROC curve (AUC) of 0.85, sensitivity of 65.7%, specificity of 88.5%, and an F-measure of 0.71. These indices suggest the model has good discriminative capability to correctly classify match outcomes based on the included performance variables. The ROC curve analysis (Figure 9) demonstrated the strong discriminative ability of the final logistic regression model, with an area under the curve (AUC) of 0.85, indicating excellent predictive accuracy. At the optimal classification threshold (cutoff = 0.5), the model achieved an overall accuracy of 80.2%, correctly classifying a large majority of match outcomes. The sensitivity, or true positive rate, reflecting the model's ability to correctly predict winning matches, was 65.7%, while specificity, the true negative rate indicating correct identification of non-winning matches, was notably higher at 88.5%. These performance metrics demonstrate the model's effectiveness in distinguishing between wins and non-wins based on the selected performance variables. Furthermore, multicollinearitydiagnostics yielded variance inflation factor (VIF) values ranging from 1.55 to 3.93, confirming that multicollinearity among predictors did not compromise the reliability of the coefficient estimates. Following the binomial logistic regression analysis, a supplementary diagnostic analysis was performed using the DiagROC module in Jamovi to further evaluate the classification performance of key match performance variables (Table 2 and Figure 10). This analysis involved constructing Receiver Operating Characteristic (ROC) curves for selected predictors to assess their ability to discriminate between match outcomes (win vs. non-win). Optimal cutoff points were identified using Youden's Index, and diagnostic accuracy measures including sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy were calculated. The ROC analysis complemented the regression results by providing practical thresholds and quantifying the discriminative power of individual performance metrics. It is worth highlighting that a threshold of four or more shots on target yielded 80% sensitivity and 60.7% specificity (AUC = 0.74), suggesting this is a practical benchmark for offensive productivity. Similarly, teams completing at least 478 passes were classified correctly in 76% of cases (AUC = 0.76), indicating that high passing volume is a strong indicator of winning performance. Receptions in the final third showed a cut-off point of 89.5, with high sensitivity (82.9%) but lower specificity (52.5%) and an AUC of 0.71, suggesting that while common in winning teams, this indicator may require combination with other metrics to improve predictive precision.