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http://dx.doi.org/10.14400/JDC.2020.18.8.225

Big Data Analysis of the Correlation between Average Daily Temperature and Batting Power  

Kim, Semin (Department of Computer Education, Jeonju National University of Education)
Shin, Chwacheol (Department of Innovation and Convergence, Hoseo University)
Publication Information
Journal of Digital Convergence / v.18, no.8, 2020 , pp. 225-230 More about this Journal
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.
Keywords
Big data; Data analysis; Baseball game data; Weather data; Batting power;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 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   DOI
2 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
3 BizHospital Website : http://m.bizhospital.co.kr/02_info/knowhow_view.php?no=308&start=10&key=&keyfield=&PHPSESSID=8cebab49c958101d4e3f18937b330f5c.
4 J. H. Park. (2006). Effects of Offense of University Baseball Team on the Winning Record, Master's Thesis, The Graduate School of Keimyung University.
5 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.
6 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   DOI
7 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   DOI
8 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
9 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.
10 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   DOI
11 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.
12 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.
13 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.
14 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.
15 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.