# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 5 Conclusions and Suggestions  
We predicted the outcomes of MLB matches by collecting the match data of 30 teams in the 2019 season and using 1 DCNN, ANN, and SVM prediction models. The prediction accuracies of these models were then compared. We investigated whether the selection of input variables and the characteristics before and after feature selection had an impact on the prediction accuracy. The prediction results indicated that 1 DCNN, ANN, and SVM models achieved higher prediction accuracies when the data of all pitchers were considered than when only the data of the SP were considered. The three prediction models proposed in this study all achieved high prediction performance and can thus be used to provide some reference information for fans, team managers, and baseball enthusiasts. The proposed prediction models can also be used to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Input Variables** & **Method *** & **Accuracy** \\ \hline \multicolumn{4}{c}{Random forest} \\  & SVM & \\ [PERSON] et al. [18] & BA, RBI, OBP, ERA, H, E, and Win\% for each team & AdaBoost & 59.60\% \\  & LogitBoost & \\  & MLR & \\ \hline \multicolumn{4}{c}{[PERSON] [7]} & SO(A), GSc(H), BB(A), ER(A) & Logistic regression & \\ \multicolumn{4}{c}{SO(H), BB(A), SO(A), WHIP(H) **} & ANN & 73.68\% \\ \hline \multicolumn{4}{c}{isHomeClub, Log5, PE, WP, RC, HomeWonPrev,} \\ \multicolumn{4}{c}{Soto Valero [8]} & VisitorWonPrev, BABIP, FP, PitchERA, OBP, SLG, & \multicolumn{2}{c}{} \\ \multicolumn{4}{c}{VisitorLeague, HomeVersusVisitor, Stolen} & \multicolumn{2}{c}{} \\ \multicolumn{4}{c}{Lazy Learners(KNN)} \\ \hline \multicolumn{4}{c}{Random forest} \\ \multicolumn{4}{c}{Liferink [9]} & AB, AVG, OBP, SLG, OPS, BA/RISP, WHIP, RA & Linear model & 55.52\% \\ \multicolumn{4}{c}{XGBoost} \\ \hline \multicolumn{4}{c}{1D-CNN} \\ \multicolumn{4}{c}{This study} & AB, H, BB, PA, BA, OBP, SLG, OPS, Pit, Str, PO, IP, H, & \multicolumn{2}{c}{} \\ \multicolumn{4}{c}{BB, HR, ERA, BF, Pit, Str, Ctct, FB, Gsc, IR, IS, H/A} & ANN & 94.18\% \\ \multicolumn{4}{c}{SVM} \\ \hline \hline \end{tabular}  
*: Boldface represents the best prediction performance in this research **: H represents the home team, and A represents the away team.  
\end{table} 
Table 17: Comparison of prediction models for MLB matches. predict game outcomes for other sports; however, their prediction performance cannot be verified without experimental simulation. The contributions of this study are as follows:  
1. This is the first study to use a 1 DCNN model to predict the outcomes of MLB matches. 2. Unlike machine learning models, the 1 DCNN model automatically extracts features. 3. This study established two data sets to compare the accuracy of match outcome prediction when only data on the SP were considered with that when data on all the pitchers were considered. The prediction results demonstrate that using the game data of all pitchers yields higher accuracy. 4. This study used data of hitters and pitchers from Baseball-Reference.com, and the three proposed models outperformed models developed in related studies in terms of prediction accuracy for the outcomes of MLB matches. The limitations of this study are as follows:  
1. Most related studies have collected more than 5 years of match data to construct prediction models, whereas this study only used data from 2019. Thus, the amount of data was relatively small. 2. Only one feature selection method (ReliefF) was used in this study; thus, the performance of the prediction models when other feature selection methods are used remains unknown. Different feature selection methods should be used to verify the performance of the proposed models. The adjustments or modifications of the CNN model are encouraged to improve its prediction accuracy. In addition, we selected only technical variables to develop the prediction models.