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http://dx.doi.org/10.5392/JKCA.2022.22.05.048

A Study on the Analysis of Factors for the Golden Glove Award by using Machine Learning  

Uem, Daeyeob (호서대학교 빅데이터AI학과)
Kim, Seongyong (호서대학교 빅데이터AI학과)
Publication Information
The Journal of the Korea Contents Association / v.22, no.5, 2022 , pp. 48-56 More about this Journal
Abstract
The importance of data analysis in baseball has been increasing after the success of MLB's Oakland which applied Billy Beane's money ball theory, and the 2020 KBO winner NC Dinos. Various studies using data in baseball has been conducted not only in the United States but also in Korea, In particular, the models using deep learning and machine learning has been suggested. However, in the previous studies using deep learning and machine learning, the focus is only on predicting the win or loss of the game, and there is a limitation in that it is difficult to interpret the results of which factors have an important influence on the game. In this paper, to investigate which factors is important by position, the prediction model for the Golden Glove award which is given for the best player by position is developed. To develop the prediction model, XGBoost which is one of boosting method is used, which also provide the feature importance which can be used to interpret the factors for prediction results. From the analysis, the important factors by position are identified.
Keywords
Baseball; Golden Glove Award; XGBoost; Feature Importance;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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