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

  • 제24권1호
  • /
  • Pages.41-47
  • /
  • 2019
  • /
  • 1598-849X(pISSN)
  • /
  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
권호 표지
DOI QR코드

DOI QR Code

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)
  • 투고 : 2019.01.21
  • 심사 : 2019.01.29
  • 발행 : 2019.01.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

CPTSCQ_2019_v24n1_41_f0001.png 이미지

Fig. 1. A Batter Evaluation Model[2]

CPTSCQ_2019_v24n1_41_f0002.png 이미지

Fig. 2. Procedure for Calculating the Weights

CPTSCQ_2019_v24n1_41_f0003.png 이미지

Fig. 3. R Code for Genetic Algorithm

CPTSCQ_2019_v24n1_41_f0004.png 이미지

Fig. 4. Fitness Values by Iteration

CPTSCQ_2019_v24n1_41_f0005.png 이미지

Fig. 5. Result of Genetic Algorithm

CPTSCQ_2019_v24n1_41_f0006.png 이미지

Fig. 6. Result of Genetic Algorithm(CassPoint)

Table 1. Run Values[6]

CPTSCQ_2019_v24n1_41_t0001.png 이미지

Table 2. Win Expectation Values[13]

CPTSCQ_2019_v24n1_41_t0002.png 이미지

Table 3. Average Pitches per Game[2]

CPTSCQ_2019_v24n1_41_t0003.png 이미지

Table 4. Pitcher Weights by Grade[2]

CPTSCQ_2019_v24n1_41_t0004.png 이미지

참고문헌

  1. E. Wassermann, D. R. Czech, M. J. Wilson and A B. Joyner, "An Examination of the Moneyball Theory: A Baseball Statistical Analysis," The Sports Journal, January 2005.
  2. Yerin Jung, "Korean Professional Baseball Batter Estimation Model Reflecting Big Data Analysis and Pitcher Ability," Master dissertation, Changwon National University, 2016. (in Korean)
  3. Jang Taek Lee, "Measurements for hitting ability in the Korean pro-baseball," Journal of The Korean Data Analysis Society, Vol. 25, No. 2, pp. 349-356, March 2014. (in Korean)
  4. Young Suk Cho and Young Ju Cho, "A study on OPS and runs from Korean baseball league," Journal of The Korean Data Analysis Society, Vol. 7, No. 1, pp. 221-231, February 2005. (in Korean)
  5. Hyuk Joo Kim, "Effects of on base and slugging ability on run productivity in Korean professional baseball," Journal of The Korean Data & Information Analysis Society, Vol. 23, No. 6, pp. 1165-1174, December 2012. (in Korean) https://doi.org/10.7465/jkdi.2012.23.6.1165
  6. Jin-Sang Jung, "Efficient estimation model of hitter using Big Data analysis in Korea Baseball League," Master dissertation, Changwon National University, 2014. (in Korean)
  7. G. B. Johnson, "Evaluation and ranking of minor-league hitters using a statistical model," Doctoral dissertation, Kansas State University, 2006.
  8. P. A. Yates, "Estimating Situational Effects on OPS," Journal of Quantitative Analysis in Sports, Vol. 4, No. 2, February 2008.
  9. B. B. McShane, A. Braunstein, J. Piette and S. T. Jensen, "A Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics," Journal of Quantitative Analysis in Sports, Vol. 7, No. 4, November 2009.
  10. S. L. Rubin, "Market Efficiency of Major League Baseball Player Salaries: A Look at the Moneyball Hypothesis Ten Years Later," Doctoral dissertation, University of Delaware, 2013.
  11. J. H. Holland, "Adaptation in natural and artificial system," University of Michigan Press, 1975.
  12. D. E. Goldberg, "Genetic algorithm in search, optimization & Machine Learning," Addison Wesley, 1989.
  13. Hyung Woo Moon, Yong Tae Woo and Yang Woo Shin, "Run expectancy and win expectancy in the Korea Baseball Organization(KBO) League," The Korean Journal of Applied Statistics, Vol. 29, No. 2, pp. 321-330, February 2016. (in Korean) https://doi.org/10.5351/KJAS.2016.29.2.321
  14. Com2uS Pro-Baseball Point, http://cpbpoint.mbcplus.com