# Application of Machine Learning Models for Baseball Outcome Prediction
## 4 Discussion  
This study proposes a research objective of identifying the best predictive model through the indicators of multiple evaluation models and then using the best model to determine the factors that influence competition outcomes. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are all optional validation metrics for evaluating model performance. However, accuracy is typically used as the primary evaluation metric. The higher the accuracy, the greater the model's reliability. When predicting the outcomes of sports competitions, data from more than three years of matches is often used to ensure a sufficient training volume, thereby reducing prediction errors. This study utilized match data from 2021 to 2023 and found that both XGBoost and Random Forest achieved up to 95% prediction accuracies when using advanced metrics. XGBoost achieves a high accuracy through improvements to decision trees, where each  
Figure 5: (**a**) A SHAP dependence plot of the interaction between the wRAA and PLO8%. (**b**) A SHAP dependence plot of the interaction between the wRC+ and WHIP. tree is interrelated, making it highly effective for predictions. On the other hand, Random Forest combines multiple decision trees for classification, and its predictive performance is not affected by the increased variability, making both models highly accurate [17]. Previous studies have shown that commonly used Artificial Neural Networks achieve an accuracy of about 75% [19; 20; 21; 22; 23]. Decision trees and support vector machines (SVMs) have been reported to achieve prediction accuracies of over 85% [24; 25; 26]. Recent research also supports the superior performance of XGBoost over neural networks in structured data settings. Ref. [27] compared XGBoost and multi-layer perceptron neural networks across five class-balanced datasets and found that XGBoost consistently outperformed neural networks in both accuracy and F1 scores. Particularly in datasets with overlapping or imperfect data distributions, XGBoost demonstrated a better generalization. The authors attributed this to XGBoost's ability to construct effective feature subspaces using tree-based ensembles, while neural networks struggled to optimize in high-dimensional discrete spaces. This finding aligns with our current results and offers empirical support for using ensemble methods like XGBoost in baseball outcome prediction tasks. The first factor influencing game outcomes is wRC+. This advanced metric is derived from the wOBA (weighted on-base average), incorporating adjustments for park factors and league averages. The advantage of wRC+ lies in its ability to compare players across leagues and eras [28; 29; 30]. Previous studies have explored changes in wRC+ using Statcast batted ball data. The findings revealed that the relationship between the exit velocity and launch angle significantly impacts wRC+. Moreover, 70% of the data from Statcast influences changes in wRC+. This highlights that a batter's performance in terms of the exit velocity and launch angle are both critical factors affecting run production [31]. The impact of wRC+ on a team's success is significant because it quantifies a player's ability to generate runs, the fundamental factor in winning games. A higher wRC+ indicates that a player contributes more effectively to run production, leading to increased scoring opportunities. The wRAA represents the expected run contribution, indicating how many more runs a hitter can generate for the team compared to the league average. This advanced metric has an impact of 7.7% on game outcomes. However, its value is influenced by the number of plate appearances. Since each time a batter reaches a base, the offensive team gains an additional opportunity to attack, it directly increases the chances of scoring runs. Therefore, during game strategy planning, coaches must focus on creating more plate appearances for their players.