Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Big Data Analysis of the Correlation between Average Daily Temperature and Batting Power

빅데이터를 활용한 타자의 장타력과 일일 평균 기온 간의 상관관계 분석

  • Kim, Semin (Department of Computer Education, Jeonju National University of Education) ;
  • Shin, Chwacheol (Department of Innovation and Convergence, Hoseo University)
  • 김세민 (주교육대학교 컴퓨터교육과) ;
  • 신좌철 (호서대학교 혁신융합학부)
  • Received : 2020.07.23
  • Accepted : 2020.08.20
  • Published : 2020.08.28
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Abstract

The KBO League is held over a long period of time due to the large number of games. Also, Korea has a diverse and distinct climate. Therefore, this study analyzed the relationship between the daily average temperature and the record of batting power such as home runs, triples, doubles, number of bases, batting percentage, and net batting percentage, and a third baseball record was defined. For this study, the correlation between the daily average temperature data and the batter who entered the standard at-bat in the KBO League in 2019 was analyzed through the SEMMA method. From the results of this study, it was found that the average daily temperature had an effect on a batter's hitting power. In particular, it was found that a batter's hitting power decreased on the day of temperatures recorded between 20.0 degrees and 24.9 degrees, and it was discussed that this may have been related to the physical condition of the pitcher the batter was facing. Therefore, it can be expected that players, coaching staff, and the front desk can use them in the game through conditions outside the game. In addition, it is expected that it will be a more useful analysis model by analyzing the records of pitching, base running, and defense as well as subsequent batting records.

KBO리그는 경기수가 많아서 오랜 기간에 걸쳐서 진행되고 있다. 또한 우리나라는 다양하고 뚜렷한 기후를 가지고 있다. 이에 본 연구에서는 야구기록 중 홈런, 3루타, 2루타, 루타수, 장타율, 순장타율 등 장타력에 관한 기록과 일일 평균 기온과의 상관 관계를 분석하고 야구 기록 중 3차 기록을 정의하였다. 본 연구를 위하여 일일 평균 기온 데이터와 2019년 KBO 리그에서 규정타석에 진입한 타자를 대상으로 SEMMA 기법을 통하여 상관관계를 분석하였다. 본 연구 결과를 통하여 일일 평균 기온이 타자들의 장타력에 영향을 주었다는 것을 알 수 있었다. 특히 20.0도에서 24.9도 사이를 기록한 날에 타자들의 장타력이 낮아졌다는 것을 알 수 있었으며, 타자가 상대하는 투수의 몸 상태와 관련있다고 논의하였다. 이에 경기 외적인 조건을 통하여 야구 경기에서 선수, 코칭스태프, 프런트가 경기에 활용할 수 있음을 기대할 수 있다. 또한 차후 타격 기록 뿐만 아니라 투구, 주루, 수비 등의 기록을 함께 분석하면 더욱 유용한 분석 모델이 될 수 있을 것으로 기대한다.

Keywords

References

  1. Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. Springer-Plus, 5(1), 1-13. DOI: https://doi.org/10.1186/s40064-016-3108-2
  2. Y. H. Kim. (2020). Analysis of football tactics and formation patterns based on big data analysis. Master's Thesis, The Graduate School of Choongang University, Seoul.
  3. J. Y. Hong. (2019), The effect of golf pre shot routine on club and ball data, Master's Theis, The Graduate School of Choongang University, Seoul.
  4. H. J. Yun (2018), A real-time players evaluation model development based on social big data in korea professional baseball : sentiment analysis using machine learning, Doctoral Dissertation, The Graduate School of Korea National Sport University, Seoul.
  5. S. M. Kim. (2020), The effect of daily average temperature on the batter's performance in baseball game : focused on big data analysis, Master's Thesis, The Graduate School of Hoseo University, Asan, Chungnam.
  6. E. Wassermann, D. R. Czech, M. J. Wilson and A. B. Joyner. (2005). An examination of the moneyball theory: a baseball statistical analysis, The Sport Journal, 19(1), 1-7.
  7. W. Gin. (2017). Big data and labor: what baseball can tell us about information and inequality, Journal of Information Technology & Politics, 15(1), 66-79. DOI: https://doi.org/10.1080/19331681.2017.1377136
  8. W. Raghupathi and V. Raghupathi. (2014). Big data analytics in healthcare: promise and potential, Health Information Science and Systems, 2(3), unpaginated. DOI: https://doi.org/10.1186/2047-2501-2-3
  9. BizHospital Website : http://m.bizhospital.co.kr/02_info/knowhow_view.php?no=308&start=10&key=&keyfield=&PHPSESSID=8cebab49c958101d4e3f18937b330f5c.
  10. J. H. Park. (2006). Effects of Offense of University Baseball Team on the Winning Record, Master's Thesis, The Graduate School of Keimyung University.
  11. Y. J. Kang. (2016). How to spend the period of inactivity? coach Lee's tip'weight training'. Sports Seoul. http://www.sportsseoul.com/news/read/341703.
  12. J. Y. Lee and H. G. Kim. (2016). Suggestion of batter ability index in Korea baseball : focusing on the sabermetrics statistics WAR, The Korean Journal of Applied Statistics, 29(7), 1271-1281. DOI: https://doi.org/10.5351/KJAS.2016.29.7.1271
  13. J. T. Lee. (2015). Long term trends in the Korean professional baseball, Journal of the Korean Data & Information Science Society, 26(1), 1-10. DOI: http://dx.doi.org/10.7465/jkdi.2015.26.1.1
  14. S. H. Yu. (2019). [Joint Health 365] Increase in catchball cases and watch out for rotator cuff tears with the opening of professional baseball, Health Digest Website. http://www.ikunkang.com/news/articleView.html?idxno=26501
  15. K. S. Byun. (2006). Biomechanical understanding of baseball pithing motion and upper extremity injuries in baseball pitchers, Doctoral Dissertation, The Graduate School of Sungkyunkwan University.