Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Estimating the determinants of victory and defeat through analyzing records of Korean pro-basketball

한국남자프로농구 경기기록 분석을 통한 승패결정요인 추정: 2010-2011시즌, 2011-2012시즌 정규리그 기록 적용

  • Kim, Sae-Hyung (Lab of Measurement and Evaluation in Physical Education, Korea National Sport University) ;
  • Lee, Jun-Woo (Basic Science Institute, Hoseo University) ;
  • Lee, Mi-Sook (School of Community Sport, Korea National Sport University)
  • 김세형 (한국체육대학교 체육측정평가실) ;
  • 이준우 (호서대학교 기초과학연구소) ;
  • 이미숙 (한국체육대학교 사회체육학과)
  • Received : 2012.08.31
  • Accepted : 2012.09.23
  • Published : 2012.09.30
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Abstract

The purpose of this study was to estimate the determinants of victory and defeat through analyzing records of Korean men pro-basketball. Statistical models of victory and defeat were established by collecting present basketball records (2010-2011, 2011-2012 season). Korea Basketball League (KBL) informs records of every pro-basketball game data. The six offence variables (2P%, 3P%, FT%, OR, AS, TO), and the four defense variables (DR, ST, GD, BS) were used in this study. PASW program was used for logistic regression and Answer Tree program was used for the decision tree. All significance levels were set at .05. Major results were as follows. In the logistic regression, 2P%, 3P%, and TO were three offense variables significantly affecting victory and defeat, and DR, ST, and BS were three significant defense variables. Offensive variables 2P%, 3P%, TO, and AS are used in constructing the decision tree. The highest percentage of victory was 80.85% when 2P% was in 51%-58%, 3P% was more than 31 percent, and TO was less than 11 times. In the decision tree of the defence variables, the highest percentage of victory was 94.12% when DR was more than 24, ST was more than six, and BS was more than two times.

한국남자프로농구 경기기록을 이용하여 승패결정요인을 분석하였다. 2010년 10월부터 2011년 3월까지, 2011년 10월부터 2012년 3월까지 치러진 정규리그 (540경기)의 기록을 분석하여 승패결정요인을 추정하였다. 한국농구연맹은 7개 공격변인과 7개 수비변인에 대한 자료를 제공하고 있다. 이들 자료 중에 공헌도와 공격력에 적용되는 6개 공격변인 (2점슛 성공률, 3점슛 성공률, 자유투 성공률, 공격리바운드, 어시스트, 턴오버)과 4개 수비변인 (수비리바운드, 스틸, 굿디펜스, 블록슛)이 승패에 미치는 영향을 통계적으로 분석하기 위해 로지스틱회귀분석과 의사결정나무분석을 적용하였다. 두 분석은 PASW와 Answer Tree 통계프로그램을 사용하였으며 모든 유의수준은 .05로 설정하였다. 로지스틱회귀분석 결과, 6개 공격변인 중 2점슛 성공률, 3점슛 성공률, 턴오버가 통계적으로 승패에 유의미한 영향을 미치고 4개 수비변인 중 굿디펜스를 제외한 수비리바운드, 스틸, 블록슛이 통계적으로 승패에 유의미한 영향을 미치는 것으로 나타났다. 그리고 공격변인 의사결정나무분석 결과에서는 2점슛 성공률이 51%-58%이며, 3P%가 31%를 초과하고 TO가 11개 이하일때 승리할 수 있는 확률이 80.85%로 가장 높게 나타났다. 이에 반해 수비변인 의사결정나무분석 결과, 수비리바운드가 24개를 초과하고 스틸이 6개를 초과하며, 블록슛이 2개를 초과할 때 승리할 수 있는 확률이 94.12%로 가장 높게 나타났다.

Keywords

References

  1. Choi, J. H. (2005). Data mining, Free Academy, Seoul.
  2. Gu, S. H. and Kim, H. S. and Jang, S. Y. (2009). A comparison study on the prediction models for the professional basketball games. Journal of Korea Institute of Sport Science, 20, 704-711. https://doi.org/10.24985/kjss.2009.20.4.704
  3. Hong, S. H. (2005). Bi-multi logistic regression, Kyoyookbook, Seoul.
  4. Hong, S. H. (2011). Bi-multi logistic regression program, Korea University 2-season education measurement and evaluation workshop, Seoul National University, Seoul.
  5. Kang, B. S. and Kim K. S. (2009). Statistics analysis of social science , Hannarae, Seoul.
  6. Kang, S. J. (2002). Sport research method and method of statistics, winter season workshop, Korea Sport Measurement and Evaluation, Seoul.
  7. Kang, T. H. (2010). Application of item response Theory, winter season workshop, Korea Sport Measurement and Evaluation, Seoul.
  8. Kim, C. Y. and Park, J. Y. (1999). Analysis of contribution of won-lost factor in the 98-99 season Korean basketball league. Journal of Korean Society of Sport and Leisure Studies, 12, 455-464.
  9. Kim, S. H. (2008). Developing estimate model of victory and defeat through analyzing the record of the pro-basketball , Master Thesis, Korea National Sport University, Seoul.
  10. Kim, S. H. and Kim, H. J. and Park, J. H. (2011). Development of model to evaluate handball shooting ability : Weight elicitation of shooting positions. Korean Journal of Measurement and Evaluation in Physical Education and Sport Science, 13, 77-87.
  11. Lee, Y. G. (2012). Data mining analysis, Korea applied statistics workshop, Chung-Ang University, Seoul.
  12. Long, S. (1997). Regression models for categorical and limited dependent variables, Sage, Thousand Oaks, CA.
  13. Park, D. K. (2012). Analyzing of contribution point in pro-basketball score, winter season workshop, Korea Sport Measurement and Evaluation, Seoul.
  14. Park, J. Y. (1997). The analysis of the factor for winning a game in the 1997 season Korean basketball league. Journal of Suwon University, 15, 311-318.
  15. Park, J. Y. (2001). The analysis of the factor for winning a game in the 2000-2001 season Korean basketball league. Journal of Korean Society of Sport and Leisure Studies, 16, 1215-1224.
  16. Park, J. Y. (2003). The analysis of the factor for winning a game in the 2002-2003 season Korean basketball league. Journal of Korean Alliance for Health Physical Education Recreation and Dance, 42, 793-893.
  17. Park, J. Y. (2004a). The analysis of the factor for winning a game in the 2003-2004 season Korean basketball league. Journal of Korean Society for the Study Physical Education, 9, 185-195.
  18. Park, J. Y. (2004b). The analysi s of the factors for winning a game in th 2004 winter season women's Korea basketball league. Journal of Suwon University, 22, 601-611.
  19. Park, J. Y. (2005a). The analysis of the factor for winning a game in the 2004-2005 season Korean basketball league. Journal of Suwon University, 23, 629-640.
  20. Park, J. Y. (2005b). The analysis of the factors for winning a game in the 28th Athens Olympic men's basketball game. Journal of Korean Society of Sport and Leisure Studies, 23, 561-570.
  21. Park, J. Y. (2007). The analysis of the factors for winning a game in the 15th Asian men's basketball. Journal of Korean Society of Sport and Leisure Studies, 30, 941-950.
  22. Park, J. Y. (2008). The analysis of the factor for winning a game in the 2000-2001 season Korean basketball league. Journal of Korean Society of Sports Science, 17, 129-138.
  23. Park, J. Y. (2009). The analysis of the factors for winning a game in the 29th Beijing Olympic men's basketball game. Journal of Korean Society of Sport and Leisure Studies, 37, 1425-1432.
  24. Park, J. Y. and Kim, C. Y. and Ji, E. B. (2000). Contribution of won-lost factor in th 99-2000 season Korean basketball league by decision tree analysis. Korean Society of Sport and Leisure Studies, 14, 327-338.
  25. Wood T. M. & Zhu, W. (2006). Measurement theory and practice in kinesiology, Human Kinetics, Champaign, IL.

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