# Application of Machine Learning Models for Baseball Outcome Prediction
## 1 Introduction  
Big Data, or \"massive data,\" refers to datasets so large and complex that traditional analytical methods prove inadequate. Advanced technologies such as artificial intelligence, machine learning, and predictive analytics are necessary to derive meaningful insights. Big Data analytics has become a global trend, widely applied across industries for forecasting and decision-making [1]. In sports, it has revolutionized traditional strategies by leveragingcomputational models to enhance athlete performance [2]. The film _Moneyball_ exemplifies this shift, illustrating how data-driven approaches can challenge conventional wisdom and optimize team value [3]. Given the uncertainty and numerous variables in sports, predicting match outcomes has gained prominence in sports analytics [4]. While audiences often focus on final scores, fewer examine the intricate progression of games [5; 6]. Play-by-play records objectively capture on-field actions, offering coaches and players valuable feedback. When systematically quantified, these statistics reveal causal links between gameplay elements and outcomes. Modern analytics companies have enhanced traditional metrics using mathematical transformations to better reflect game realities. For example, fielding independent pitching (FIP) isolates a pitcher's performance by eliminating the impact of relief pitchers, offering a more accurate evaluation than the earned run average (ERA). In basketball, the true shooting percentage (TS%) integrates two-point, three-point, and free-throw data to more comprehensively assess scoring efficiency [7; 8]. Such advanced statistics often incorporate weighted formulas, enhancing their predictive power and practical value [4]. Unlike traditional statistical models, which emphasize causal inference, machine learning prioritizes predictive accuracy--even at the expense of interpretability. For instance, ref. [9] reported that logistic regression achieved only a 61.11% accuracy, while Artificial Neural Networks (ANNs) improved the performance to 72.22%. Similarly, ref. [10] tested regression, Bayesian classification, support vector machines (SVMs), and ANNs on five years of NBA game data, finding that the regression yielded the highest accuracy at 69.67%. These results suggest that logistic regression typically provides moderate prediction rates ranging from 60% to 70%. The ANN has gained popularity due to its flexibility in modeling nonlinear patterns and minimal reliance on strict statistical assumptions. A study on college basketball reported an 89.42% accuracy using an ANN [11], while another analyzing Serbian league data achieved 80.96% [12]. In baseball, ref. [13] used a dataset with 26 batter and 34 pitcher variables to predict MLB outcomes and found that an ANN, with five-fold cross-validation, outperformed other models, reaching 93.91% accuracy. Decision tree models have also been applied, achieving a 78.2% accuracy with decision pruning and identifying key variables [14]. Further extending these findings, ref. [9] again applied both logistic regression and an ANN to forecast Yankees vs. Red Sox outcomes, reporting a 69.23% and 72.22% accuracy, respectively. Other research employed SVMs with 16 input features, noting that metrics like the slugging percentage and double plays aligned well with predictions from a Gaussian RBF kernel, achieving a 69% accuracy [15]. In another study, ref. [16] reduced a 60-variable MLB dataset to 15 key features, attaining a 60% prediction accuracy using an SVM. Building on this, ref. [13] analyzed 4858 MLB games from 2019, using both starting and total pitching data. Among 1D convolutional neural networks (1 DCNNs), ANNs, and SVMs, ANNs delivered the best results--a 94% accuracy with all pitchers included, versus 92% using only starters. A comparative study by [6] evaluated multiple models before and after feature selection, finding the SVM to be the most accurate at 65.75%. A major limitation of high-performing models like the ANN and SVM lies in their opacity.