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A Baseball Batter Evaluation Model using Genetic Algorithm

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(1), pp.41-47
  • DOI : 10.9708/jksci.2019.24.01.041
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 21, 2019
  • Accepted : January 29, 2019
  • Published : January 31, 2019

Su-Hyun Lee 1 Yerin Jung 2 Hyung-Woo Moon 1 Yong-Tae Woo 1

1창원대학교
2하이브레인넷

Accredited

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.

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