The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of the Korean Baseball League using a Markov Chain Model

마르코프 연쇄를 이용한 한국 프로야구 경기 분석

  • Moon, Hyung Woo (Department of Computer Engineering, Changwon National University) ;
  • Woo, Yong Tae (Department of Computer Engineering, Changwon National University) ;
  • Shin, Yang Woo (Department of Statistics, Changwon National University)
  • 문형우 (창원대학교 컴퓨터공학과) ;
  • 우용태 (창원대학교 컴퓨터공학과) ;
  • 신양우 (창원대학교 통계학과)
  • Received : 2013.05.15
  • Accepted : 2013.08.11
  • Published : 2013.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

We use a Markov chain model to analyze the Korean Baseball League. We derive the distributions of the number of runs scored and the number of batters that complete their turn at bat in a baseball game using the time inhomogeneous Markov chain. The model is tested with real data produced from the 2011 Korean Baseball League.

본 논문에서는 마르코프 연쇄로 모형을 이용하여 한국프로야구의 경기결과를 예측하고 분석하였다. 타자의 타격결과와 주자상태를 나타내는 확률과정을 구체적으로 정의하여 경기진행 상황을 동적으로 반영한 프로야구 경기를 마르코프 연쇄를 구성하여 실제 데이터를 바탕으로 주자 상태를 고려한 진루행렬과 각 선수별 타격 확률을 구하여 경기당 득점 분포와 타석에 서는 타자 수의 분포를 구하였다.

Keywords

References

  1. Bae, J. Y., Lee, J. M. and Lee, J. Y. (2012). Predicting Korea Pro-baseball rankings by principal component regression analysis, Communications of the Korean Statistical Society, 19, 367-379. https://doi.org/10.5351/CKSS.2012.19.3.367
  2. Bukiet, B., Harold, E. R. and Palacios, J. L. (1997). A Markov chain approach to baseball, Operations Research, 45, 14-23. https://doi.org/10.1287/opre.45.1.14
  3. Cho, Y. S., Cho, Y. J. and Shin, S. G. (2007). A study on winning and losing in Korean professional baseball league, Journal of the Korean Data Analysis Society, 9, 501-510.
  4. Choi, Y. G. and Kim, H. M. (2011). A statistical study on Korean baseball league games, The Korean Journal of Applied Statistics, 24, 915-930. https://doi.org/10.5351/KJAS.2011.24.5.915
  5. D'Esopo, D. A. and Lefkowitz, B. (1960). The distribution of runs in the game of baseball, SRI Internal report.
  6. Hirotsu, N. and Wright, M. (2003). A Markov chain approach to optimal pinch hitting strategies in a designated hitter rule baseball game, Journal of the Operations Research Society of Japan, 46, 353-371. https://doi.org/10.15807/jorsj.46.353
  7. Hirotsu, N. and Wright, M. (2005). Modelling a baseball game to optimise pitcher substitution strategies incorporating handedness of players, IMA Journal of Management Mathematics, 16, 179-194. https://doi.org/10.1093/imaman/dpi009
  8. Kim, H. (2011). Suggestion of a new method of computing percentage of victories for the Korean professional baseball, The Korean Journal of Applied Statistics, 6, 1139-1148. https://doi.org/10.5351/KJAS.2011.24.6.1139
  9. Lee, J. T. and Cho, H. S. (2009). Win-loss models when two teams meet using data mining in the Korean pro-baseball, Journal of the Korean Data Analysis Society, 11, 3417-3426.
  10. Shin, Y. W. (2011). Introduction to Stochastic Processes, Kyungmoon Publishers, Seoul.
  11. Sokol, J. S. (2003). A robust heuristic for batting order optimization under uncertainty, Journal of Heuristics, 9, 353-370. https://doi.org/10.1023/A:1025657820328
  12. Tesar, N. Estimating expected runs using a Markov model for baseball, https://www.edsolio.com/media/2/265/files/TesarFinal_Draft.pdf.

Cited by

  1. Comprehensive evaluation of baseball player's offensive ability by use of simulation vol.26, pp.4, 2015, https://doi.org/10.7465/jkdi.2015.26.4.865
  2. What Makes Sports Fans Interactive? Identifying Factors Affecting Chat Interactions in Online Sports Viewing vol.11, pp.2, 2016, https://doi.org/10.1371/journal.pone.0148377
  3. Run expectancy and win expectancy in the Korea Baseball Organization (KBO) League vol.29, pp.2, 2016, https://doi.org/10.5351/KJAS.2016.29.2.321