## 2 Materials and Methods
### Statistical Analysis  
Data were analyzed and visualized with IBM SPSS Statistics (version 29 for Windows) [37], Jamovi (version 2.6.23.0 for Windows) [38], and JASP (version 0.19.3.0 for Windows) [39]. Descriptive statistics were computed for all variables, including means, medians, standard deviations, interquartile ranges, and 95% confidence intervals (CI). To examine the impact of scoring the first goal on match outcomes and the temporal distribution of goals, Chi-square goodness-of-fit tests were conducted to compare observed frequencies against expected equal proportions. Normality of distribution was assessed using the Shapiro-Wilk test. Variables meeting normality assumptions were analyzed with parametric tests, while non-normally distributed variables were analyzed using non-parametric methods. Effect sizes (ES) were calculated in accordance with [PERSON]'s [40] guidelines, utilizing \(\eta^{2}\) for parametric data and \(\varepsilon^{2}\) for non-parametric data. The thresholds for interpreting effect sizes were categorized as small (0.01-0.06), moderate (0.06-0.14), and large (>0.14). A one-way Analysis of Variance (ANOVA) was conducted to examine differences in possession percentage and other normally distributed performance metrics across match outcomes categorized as loss, draw, and win. Where significant effects were identified, post hoc Tukey tests were applied for pairwise comparisons. For variables violating normality assumptions, Kruskal-Wallis tests were employed to assess differences across match outcomes. Significant Kruskal-Wallis results were further explored with Dwass-Steel-Critchlow-Figner pairwise comparisons to identify specific group differences.  
To determine the variables that significantly influence match results, a generalized linear modeling approach was applied, specifically logistic regression, with the binary outcome variable coded as win versus draw or loss, consistent with methodologies employed in earlier research [1; 2; 8; 11]. Binomial logistic regression analyses were performed to model the likelihood of match success (win vs. non-win) based on key match performance variables. Nine successive models (M\({}_{0}\) to M\({}_{9}\)) were constructed, progressively incorporating predictors to evaluate their individual and combined effects on winning probability. Model fit was assessed by changes in deviance, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and pseudo R\({}^{2}\) indices (McFadden, Nagelkerke, Tjur, Cox and Snell). Multicollinearity was evaluated using tolerance and variance inflation factors (VIF), with VIF values below 5 considered acceptable. The final logistic regression model's discriminative ability was examined through Receiver Operating Characteristic (ROC) curve analysis, with the area under the curve (AUC) indicating predictive accuracy. Classification metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed at the optimal cutoff point determined by Youden's Index. Additionally, supplementary ROC curve analyses for individual predictors were performed using the DiagROC module in Jamovi [38; 41; 42; 43; 44; 45; 46], to further assess their diagnostic accuracy and establish practical threshold values for match outcome discrimination. Statistical significance was set at \(p\leq 0.05\) for all tests.