## 3 Results
### Logistic Regression Analysis  
A logistic regression analysis (Table 1) was conducted to examine the association between multiple match performance metrics and match outcome (win vs. non-win, with win coded as 1). Nine successive models (M\({}_{0}\) to M\({}_{9}\)) were estimated, progressively incorporating additional predictors to evaluate their influence on the likelihood of winning. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Predictor** & **B** & **SE** & **Odds Ratio** & \
\begin{tabular}{c} **95\%** \\ **CI Lower** \\ \end{tabular} &
\begin{tabular}{c} **95\%** \\ **CI Upper** \\ \end{tabular} & **Wald** & \(p\) \\ \hline Attempts at Goal On Target & 0.333 & 0.118 & 1.395 & 1.102 & 1.754 & 8.001 & 0.005 * \\ \hline Defensive Pressures & \(-\)0.019 & 0.008 & 0.982 & 0.966 & 0.997 & 5.443 & 0.020 * \\ \hline Total Passes (Complete) & 0.009 & 0.003 & 1.009 & 1.003 & 1.015 & 8.624 & 0.003 * \\ \hline Total Distance Covered (km) & 0.140 & 0.067 & 1.150 & 1.008 & 1.268 & 4.333 & 0.037 * \\ \hline Receptions in Final Third & \(-\)0.015 & 0.006 & 0.985 & 0.973 & 0.998 & 5.376 & 0.020 * \\ \hline \multicolumn{8}{c}{Note: \({}^{*}p<0.05\).} \\ \end{tabular}
\end{table} 
Table 1: Significant predictors from logistic regression analysis for match outcome (win vs. draw/loss). Figure 8: Raincloud plots illustrating distribution of set-piece related match events according to match outcome (win, draw, lose). **(A)** Free kicks: Frequency of free kick occurrences. **(B)** Penalties: Incidence of penalty kicks awarded. **(C)** Corners: Number of corner kicks executed. **(D)** Throw ins: Frequency of throw-ins by teams. Symbols indicate statistically significant differences (\(p<0.05\)): $ Draw \(\
eq\) Lose. Across models, the decrease in deviance and the improvements in Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were minimal and statistically non-significant (all \(\Delta\chi^{2}\)\(p\)-values \(>\) 0.05), indicating limited enhancement in model fit with the inclusion of additional predictors. The pseudo R\({}^{2}\) statistics ([PERSON], [PERSON], [PERSON], and Cox and Snell) were consistently very low, suggesting that the models explained only a modest proportion of the variance in match outcomes. Despite the overall limited explanatory power, certain performance variables consistently emerged as significant predictors across models. Specifically, attempts at goal on target demonstrated a significant positive association with winning, as each additional shot on target increased a team's odds of winning by approximately 40%, reflecting the critical value of offensive efficiency. For instance, in the fully adjusted model (M\({}_{9}\)), each additional attempt on target increased the odds of winning by approximately 40% (OR = 1.40, 95% CI [1.10, 1.75], \(p\) = 0.005). This underscores the critical importance of offensive accuracy in determining match success. Defensive pressures were inversely associated with winning (OR \(\approx\) 0.98 across models, \(p\) < 0.05), suggesting that a higher frequency of defensive pressures corresponded to a decreased likelihood of victory. In practical terms, a team performing 10 additional defensive pressures would experience an approximate 17% reduction in their odds of winning, indicating that frequent defensive activity may be symptomatic of teams lacking sustained control. This finding may reflect the possibility that teams facing greater defensive pressure are more likely to concede possession or control. Total passes completed showed a modest but significant positive effect in later models, for example, in M\({}_{9}\) (OR = 1.009, 95% CI [1.003, 1.015], \(p\) = 0.003), indicating that efficient ball circulation contributes to positive match outcomes. Specifically, an increase of 100 completed passes was associated with a 9% greater chance of winning, supporting the value of accurate ball movement.