Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
Major League Baseball (MLB) is the highest level of professional baseball in the world and accounts for some of the most popular international sporting events. Many scholars have conducted research on predicting the outcome of MLB matches. The accuracy in predicting the results of baseball games is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/10/4499 |
| _version_ | 1827692494644576256 |
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| author | Mei-Ling Huang Yun-Zhi Li |
| author_facet | Mei-Ling Huang Yun-Zhi Li |
| author_sort | Mei-Ling Huang |
| collection | DOAJ |
| description | Major League Baseball (MLB) is the highest level of professional baseball in the world and accounts for some of the most popular international sporting events. Many scholars have conducted research on predicting the outcome of MLB matches. The accuracy in predicting the results of baseball games is low. Therefore, deep learning and machine learning methods were used to build models for predicting the outcomes (win/loss) of MLB matches and investigate the differences between the models in terms of their performance. The match data of 30 teams during the 2019 MLB season with only the starting pitcher or with all pitchers in the pitcher category were collected to compare the prediction accuracy. A one-dimensional convolutional neural network (1DCNN), a traditional machine learning artificial neural network (ANN), and a support vector machine (SVM) were used to predict match outcomes with fivefold cross-validation to evaluate model performance. The highest prediction accuracies were 93.4%, 93.91%, and 93.90% with the 1DCNN, ANN, SVM models, respectively, before feature selection; after feature selection, the highest accuracies obtained were 94.18% and 94.16% with the ANN and SVM models, respectively. The prediction results obtained with the three models were similar, and the prediction accuracies were much higher than those obtained in related studies. Moreover, a 1DCNN was used for the first time for predicting the outcome of MLB matches, and it achieved a prediction accuracy similar to that achieved by machine learning methods. |
| first_indexed | 2024-03-10T11:24:53Z |
| format | Article |
| id | doaj.art-749a4f41706a4770bcaa0e04424c086d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:24:53Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-749a4f41706a4770bcaa0e04424c086d2023-11-21T19:46:00ZengMDPI AGApplied Sciences2076-34172021-05-011110449910.3390/app11104499Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball MatchesMei-Ling Huang0Yun-Zhi Li1Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 41170, TaiwanDepartment of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 41170, TaiwanMajor League Baseball (MLB) is the highest level of professional baseball in the world and accounts for some of the most popular international sporting events. Many scholars have conducted research on predicting the outcome of MLB matches. The accuracy in predicting the results of baseball games is low. Therefore, deep learning and machine learning methods were used to build models for predicting the outcomes (win/loss) of MLB matches and investigate the differences between the models in terms of their performance. The match data of 30 teams during the 2019 MLB season with only the starting pitcher or with all pitchers in the pitcher category were collected to compare the prediction accuracy. A one-dimensional convolutional neural network (1DCNN), a traditional machine learning artificial neural network (ANN), and a support vector machine (SVM) were used to predict match outcomes with fivefold cross-validation to evaluate model performance. The highest prediction accuracies were 93.4%, 93.91%, and 93.90% with the 1DCNN, ANN, SVM models, respectively, before feature selection; after feature selection, the highest accuracies obtained were 94.18% and 94.16% with the ANN and SVM models, respectively. The prediction results obtained with the three models were similar, and the prediction accuracies were much higher than those obtained in related studies. Moreover, a 1DCNN was used for the first time for predicting the outcome of MLB matches, and it achieved a prediction accuracy similar to that achieved by machine learning methods.https://www.mdpi.com/2076-3417/11/10/4499major league baseballone-dimensional convolutional neural networkartificial neural networksupport vector machineprediction model |
| spellingShingle | Mei-Ling Huang Yun-Zhi Li Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches Applied Sciences major league baseball one-dimensional convolutional neural network artificial neural network support vector machine prediction model |
| title | Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches |
| title_full | Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches |
| title_fullStr | Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches |
| title_full_unstemmed | Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches |
| title_short | Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches |
| title_sort | use of machine learning and deep learning to predict the outcomes of major league baseball matches |
| topic | major league baseball one-dimensional convolutional neural network artificial neural network support vector machine prediction model |
| url | https://www.mdpi.com/2076-3417/11/10/4499 |
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