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A Baseball Batter Evaluation Model using Genetic Algorithm

  • Lee, Su-Hyun (Dept. of Computer Engineering, Changwon National University) ;
  • Jung, Yerin (Research Institute, HiBrain.Net) ;
  • Moon, Hyung-Woo (Institute of Industrial Technology Research Center, Changwon National University) ;
  • Woo, Yong-Tae (Dept. of Computer Engineering, Changwon National University)
  • Received : 2019.01.21
  • Accepted : 2019.01.29
  • Published : 2019.01.31

Abstract

In this paper, we propose a new batter evaluation model that reflects the skill of the opponent pitcher in Korean professional baseball. The model consists of evaluation factors such as Run Value, Contribution Score and Ball Consumption considering the pitcher grade. These evaluation factors are calculated as different data. In order to include the evaluation factors having different characteristics into one model, each evaluation factor is weighted and added. The genetic algorithms were used to calculate the weights, and the data were based on the 2016 records of Korea Professional Baseball and the salary data of the players of 2017. As a result of calculation of the weight, the weight of the Run Value was high and the weight of the Contribution Score was very low. This means that when calculating the annual salary, it reflects much of the expected score according to the batting result of the batter. On the other hand, the contribution score indicating the degree to which the batting result contributed to the victory of the team according to the state of the economy is not reflected in the salary or point system.

Keywords

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Fig. 1. A Batter Evaluation Model[2]

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Fig. 2. Procedure for Calculating the Weights

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Fig. 3. R Code for Genetic Algorithm

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Fig. 4. Fitness Values by Iteration

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Fig. 5. Result of Genetic Algorithm

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Fig. 6. Result of Genetic Algorithm(CassPoint)

Table 1. Run Values[6]

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Table 2. Win Expectation Values[13]

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Table 3. Average Pitches per Game[2]

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Table 4. Pitcher Weights by Grade[2]

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References

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