# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 2 Literature Review
### Predicting the Outcome of a Match  
Sports analysis technology has been developing at a rapid pace, and it is generally regarded as one of the most popular topics in analysis technology [24]. Most studies have used traditional machine learning methods for analysis and prediction. For example, in 2019, [PERSON] et al. [25] proposed ensemble methods to improve an SVM for predicting the outcomes of National Hockey League matches and improve recruitment and salary decisions. The average accuracy obtained was >90%. In 2019, [PERSON] et al. [26] proposed a hybrid ensemble learning framework combining bagging and the random subspace method for predicting the outcome of the 2016-2017 Chinese Basketball Association season. The data set included 20 teams and 380 games. The final prediction results obtained were compared with those obtained using naive Bayes and ANN models; the proposed hybrid ensemble learning framework had a higher accuracy (of 84%) and a higher F1-Score (of 82%). In 2020, [PERSON] et al. [10] proposed the use of a self-coding 2 DCNN to predict NBA players' performance and the outcomes of matches. The prediction accuracies of the model for all NBA team data from 2014 to 2017 were 91.78% and 91.64%, respectively. [PERSON] [27] proposed an improved back-propagation neural network (BPNN) in 2020 to predict the outcome of the main tournament of the Union of European Football Associations, which is the Champions League, and compared the results with those obtained using multiple linear regression (MLR) and detrended cross-correlation analysis. The improved BPNN had a prediction error of almost zero with high accuracy and reliability. Therefore, a neural network prediction model can provide a certain theoretical basis for sports competition and analysis. In 2004, [PERSON] and [PERSON] [6] collected data from 179 MLB games in the 2001 regular season. The ratio of the winning percentages of the two teams, ratio of the two teams' overall batting averages, ratio of the ERAs between the two SPs, and home field advantage were used as variables for the proposed two-stage Bayesian model. They were effective in predicting the MLB division winner. In 2013, [PERSON] et al. [18] collected MLB competition data from 2007 to 2012 using five machine learning methods: random forest, SVM, adaptive boosting (AdaBoost), logistic boosting (LogitBoost), and MLR. Competition data from 2007-2011 were used for training, and 2012 data were used for testing. The SVM exhibited the highest prediction accuracy of 59.6%. In 2014, [PERSON] [7] used an ANN to construct a model for predicting the winning team in a match; they used logistic regression analysis to construct a prediction model for the two teams and compared their model predictions. They used data from the games of the New York Yankees and the Boston Red Sox from 2006 to 2013 and used 127 games between 2006 and 2102 to train the model. The results of the study showed that the ANN achieved an accuracy rate of 72.22%; subsequently, they used 19 games in 2013 to verify the prediction performance of the model, and the prediction accuracy was 73.68%. In 2016, [PERSON] [8] used four machine learning methods--an SVM, an ANN, a decision tree, and a k-NN algorithm--and used them for predicting the outcomes of MLB matches using data between 2005 and 2014 of the 30 teams. The four methods were employed for classification and regression, and the outcome of a match was verified using tenfold cross-verification. The highest accuracy was obtained with the SVM classification method (59%). In 2016, [PERSON] and [PERSON] [20] used an SVM to develop an MLB championship prediction model. SVMs with a linear kernel, quadratic kernel, cubic kernel, and Gaussian radial basis function (RBF) kernel were employed to predict the American League champions, National League champions, and the World Series champions for the 2015 season.