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http://dx.doi.org/10.9708/jksci.2019.24.02.017

A Win/Lose prediction model of Korean professional baseball using machine learning technique  

Seo, Yeong-Jin (Technical Support Team, Hiball Inc.)
Moon, Hyung-Woo (Institute of Industrial Technology Research Center, Changwon National University)
Woo, Yong-Tae (Dept. of Computer Engineering, Changwon National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.24, no.2, 2019 , pp. 17-24 More about this Journal
Abstract
In this paper, we propose a new model for predicting effective Win/Loss in professional baseball game in Korea using machine learning technique. we used basic baseball data and Sabermetrics data, which are highly correlated with score to predict and we used the deep learning technique to learn based on supervised learning. The Drop-Out algorithm and the ReLu activation function In the trained neural network, the expected odds was calculated using the predictions of the team's expected scores and expected loss. The team with the higher expected rate of victory was predicted as the winning team. In order to verify the effectiveness of the proposed model, we compared the actual percentage of win, pythagorean expectation, and win percentage of the proposed model.
Keywords
Win/Lose Prediction Model; Baseball Strategy; Machine Learning; Deep Learning; Baseball Data Analysis;
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