# Application of Machine Learning Models for Baseball Outcome Prediction
## 4 Discussion
This suggests that a pitcher's ability to suppress runs is linked not only to skill and experience but also to mental resilience. Effective pitchers who prevent runners from scoring also tend to have lower ERAs, while pitchers with a LOB% below the league average may be more affected by luck rather than skill. When faced with high-pressure situations, pitchers with strong mental fortitude and experience are more likely to escape jams, turning the pressure back onto the opposing team. If the offensive team fails to score, they may become overly focused on preventing runs while on defense, which can inadvertently lead to defensive mistakes and allow runs. Therefore, a pitcher's ability to maintain a high LOB% is a critical determinant of success in competitive baseball. FIP is a crucial metric for evaluating a pitcher's performance, as it eliminates defensive factors and focuses solely on strikeouts, walks, and home runs--elements directly controlled by the pitcher. In this study's machine learning analysis, FIP was identified as a key variable influencing game outcomes, demonstrating its high predictive value for pitcher performance. Pitchers with lower FIP tend to deliver more consistent outings, reduce scoring risks, and ultimately contribute to higher team winning percentages [39]. Additionally, FIP complements other metrics such as the WHIP and PLOB%, providing a more comprehensive assessment of pitching ability. These findings further validate the predictive power of FIP in baseball analytics, and future research could incorporate additional game contexts and deep learning techniques to enhance the accuracy of pitcher performance evaluations. The SHAP analysis provided a clear interpretability framework, revealing that the wRC+ and PLOB% had the largest marginal contribution to the model's output. This implies that offensive productivity and the ability to suppress scoring opportunities are the two most decisive dimensions in predicting game outcomes. For coaches and analysts, this insight suggests that optimizing lineups based on high-wRC+ players, and strategically managing pitchers with a high PLOB% under pressure, may lead to significantly improved game outcomes. This study utilizes machine learning methods to analyze baseball data and compares different models' predictive performance to evaluate the impact of key statistical indicators on game outcomes. The results show that XGBoost and logistic regression perform the best across multiple evaluation metrics, with an average accuracy exceeding 0.91 and an ROC AUC above 0.97, demonstrating their superior predictive capability. In contrast, while the XGBoost model offers high interpretability, its overall predictive performance is relatively lower. Additionally, the Neural Network model exhibits greater fluctuations in some evaluation metrics, indicating that its stability requires further improvement. Further analysis of feature importance reveals that wRC+ is the most influential variable in the XGBoost model, emphasizing its crucial role in assessing a hitter's offensive capability. Consistent with the SHAP value analysis, the PLOB%, wRAA, and WHIP are also highly influential variables, highlighting their importance in predicting player performance. The SHAP values further indicate that pitching-related metrics, such as the PLOB%, WHIP, wRAA, and FIP, have a significant impact on model predictions, reinforcing their role in evaluating pitcher performance. Meanwhile, hitter-related statistics such as the wRC+ and wOBA also exhibit strong explanatory power regarding game outcomes, further validating the importance of these advanced hitting metrics. This study addresses these gaps by (1) comparing multiple machine learning models--including XGBoost, Random Forest, and logistic regression--across various evaluation metrics; (2) applying SHAP values to identify key offensive and pitching indicators that influence game outcomes; and (3) validating the critical role of advanced metrics such as the wRC+, wRAA, wOBA, and LOB% in predicting performance.