Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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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)
  • Received : 2019.01.31
  • Accepted : 2019.02.25
  • Published : 2019.02.28
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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

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Fig. 1. Deep Learning Neural Network Model

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Fig. 3. Compared ReLU activate function and sigmoid activate function

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Fig. 4. Win/Loss prediction model

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Fig. 5. Deep network model for prediction of score

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Fig. 6. Deep network model for prediction of loss

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Fig. 7. Compared the actual percentage of win, pythagorean expectation and proposed model

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Fig, 2. Compared using drop-out algorithm (a) Before using drop-out algorithm, (b) After using drop-out algorithm,

Table 1. Correlation between sabermetrics data and team score

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Table 2. A team data processing result using moving average

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Table 3. A team era data convert to Z-score

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Table 4. August 2, 2011 games score/loss prediction by team

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Table 5. August 2, 2011 games win probability by team

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Table 6. Compared the actual percentage of win, pythagorean expectation and proposed model

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