# Application of Machine Learning Models for Baseball Outcome Prediction
## 3 Result  
Results presented in Table 3 indicated that the logistic regression and XGBoost consistently outperformed the other models in classification tasks. These models achieved high accuracy (0.89-0.93) and AUC values (0.97-0.98), reflecting an excellent discriminatory ability. Furthermore, they demonstrated well-balanced precision (0.90-0.94), recall (0.89-0.94), and F1 scores (0.90-0.92), indicating a reliable performance in identifying true positives while minimizing false positives. Their high NPVs (0.88-0.92) further underscored their robustness in reducing false negatives, a critical consideration in decision-making under uncertainty. The Random Forest also exhibited stable and competitive performance across all evaluation criteria, with accuracy and AUC values close to those of the logistic regression and XGBoost. While its F1 scores were marginally lower, it offered a practical trade-off between predictive performance and interpretability. The ANN model demonstrated the highest sensitivity (up to 0.96), surpassing all other models in identifying wins (positive class). However, this came at the cost of a lower specificity (as low as 0.75) and precision, suggesting a tendency to overpredict positive outcomes. Additionally, the ANN incurred the longest average execution time (46.8 s), potentially limiting its practicality in real-time or resource-constrained environments. The decision tree model yielded the lowest overall performance, with AUC values near 0.85 and more variability in accuracy (0.84-0.86). Nonetheless, its minimal computation time (0.2 s) and high interpretability position it as a viable option when efficiency and transparency are prioritized over predictive precision. Differences in model performance can be attributed to each algorithm's ability to capture nonlinear relationships and interactions among features. Gradient boosting models such as XGBoost and LightGBM are particularly well-suited for modeling such complexities, which likely accounts for their superior results. In contrast, simpler models like decision trees are less equipped to handle such intricacies, resulting in reduced accuracy. In summary, logistic regression and XGBoost emerged as the most effective models for predicting CPBL game outcomes, offering a strong balance between predictive accuracy, model stability, and practical utility. These findings support their application in sports analytics and real-time decision-making in professional baseball settings. Figure 2 presents the ROC curves for all six machine learning models, comparing their classification performance for game outcome predictions. The ROC curve plots the true  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **F1 Score** & \begin{tabular}{c} **Sensitivity** \\ **(Recall)** \\ \end{tabular}  & **Specificity** & \begin{tabular}{c} **PPV** \\ **(Precision)** \\ \end{tabular}  & **NPV** & \ \begin{tabular}{c} **ROC** \\ **AUC** \\ \end{tabular}  & \begin{tabular}{c} **Running** \\ **Time** \\ \end{tabular}  \\ \hline Decision & 0.86 & 0.86 & 0.83 & 0.90 & 0.89 & 0.84 & 0.86 & \\ Decision & 0.84 & 0.84 & 0.82 & 0.86 & 0  
positive rate against the false positive rate at various threshold settings, with the area under the curve (AUC) serving as the primary performance metric. Logistic regression achieved the highest AUC (0.970), demonstrating an exceptional discriminative ability. XGBoost and LightGBM followed closely with AUC values of 0.968, reflecting the strong performance of gradient boosting algorithms in capturing complex nonlinear relationships. Random Forest exhibited a competitive performance (AUC = 0.962), confirming the effectiveness of ensemble methods. The Artificial Neural Network achieved an AUC of 0.943, indicating a solid but comparatively lower performance. The decision tree yielded the lowest AUC (0.850), suggesting a limited predictive capability relative to other models.