A Study on Prediction of Attendance in Korean Baseball League Using Artificial Neural Network

인경신경망을 이용한 한국프로야구 관중 수요 예측에 관한 연구

  • 박진욱 (연세대학교 컴퓨터과학과) ;
  • 박상현 (연세대학교 컴퓨터과학과)
  • Received : 2017.07.17
  • Accepted : 2017.08.17
  • Published : 2017.12.31


Traditional method for time series analysis, autoregressive integrated moving average (ARIMA) allows to mine significant patterns from the past observations using autocorrelation and to forecast future sequences. However, Korean baseball games do not have regular intervals to analyze relationship among the past attendance observations. To address this issue, we propose artificial neural network (ANN) based attendance prediction model using various measures including performance, team characteristics and social influences. We optimized ANNs using grid search to construct optimal model for regression problem. The evaluation shows that the optimal and ensemble model outperform the baseline model, linear regression model.


Supported by : 한국연구재단


  1. Young-Hoon Lee, "Time-Series Analysis on Attendance in the Korean Professional Baseball League," Korean Journal of Sport Science, Vol.13, No.4, pp.85-102, 2002.
  2. Box, Jenkins, and Gwilym M. Jenkins. "Reinsel. time series analysis, forecasting and control," Tercera. NJ: Prentice Hall, Englewood Cliffs, NJ, USA, 1994.
  3. Min Cheol Kim, "Model study to predict the number of pro-baseball spectator by time series analysis: About Busan Lotte Giants' spectator," Korean Journal of Sport Management, Vol.14, No.1, pp.17-25, 2009.
  4. Min Sin Sul, Doo Yong Park and Mi Jeng Lee, "Forecast Study of Korea Pro Baseball Spectators by Using Time Series Analysis (2011-2015)," Journal of Sport and Leisure Studies, Vol.45, No.1, pp.375-387, 2011.
  5. Hyung-Don Kim and Jin-Seok Chae, "Prediction of the Number of Spectators for the Pro-baseball Club Using a Time Series Model," The Korean Journal of Measurement and Evaluation in Physical Education and Sport Science, Vol.14, No.3, pp.57-68, 2012.
  6. Jin-Seok Chae, "Prediction Model for Korean Professional Baseball Spectators," Korean Journal of Sport Science, Vol. 23, No.4, pp.892-905, 2012.
  7. Ga-Hee Han, Jigyu Chung, and Jae Keun Yoo, "A study on prediction for attendances of Korean probaseball games using covariates," Journal of the Korean Data & Information Science Society, Vol.25, No.6, pp.1481-1489, 2014.
  8. Jangtaek Lee and Soyoung Bang, "Forcasting attendance in the Korean professional baseball league using GARCH models," Journal of the Korean Data & Information Science Society, Vol.21, No.6, pp.1041-1049, 2010.
  9. Jangtaek Lee, "Long term trends in the Korean professional baseball," Journal of the Korean Data & Information Science Society, Vol.26, No.1, pp.1-10, 2015.
  10. Young Hoon Lee, "The Decline of Attendance in the Korean Professional Baseball League," Journal of Sports Economics, Vol.7, No.2, pp.187-200, 2006.
  11. Hyeuk Kim, "Prediction of the number of attendances in the home team according to the visiting team and the day in Korean Baseball League," Korean Journal of Sport Management, Vol.21, No.6, pp.85-96, 2016.
  12. J. A. Winfree, J. J. McCluskey, R. C. Mittelhammer, and R. Fort, "Location and attendance in major league baseball," Applied Economics, Vol.36, No.19, pp.2117-2124, 2004.
  13. Juho Lee, Keunhyuk Song, Hongjun Park, and Joonkeun Yum, "A Study on Determinants in Korean Pro-Baseball Spectators," Journal of the Korean Data Analysis, Vol.12, No.6(B), pp.3507-3517, 2010.
  14. J. W. Meehan Jr., R. A. Nelson, and T. V. Richardson, "Competitive balance and game attendance in major league baseball," Journal of Sports Economics, Vol.8, No.6, pp.563-580, 2007.
  15. Jang-Taek Lee and Hyun-Sik Cho, "An Analysis on the Home-Field Advantage in Korean Pro-Baseball with Logistic Regression Model," Journal of the Korean Data Analysis, Vol.11, No.1(B), pp.533-543, 2009.
  16. William A. Young, William S. Holland, and Gary R. Weckman, "Determining hall of fame status for major league baseball using an artificial neural network," Journal of Quantitative Analysis in Sports, Vol.4, Issue 4, Article 4, 2008.
  17. A. McCabe, and J. Trevathan, "Artificial intelligence in sports prediction," in International Conference on Information Technology: New Generations, pp.1194-1197, 2008.
  18. Seung-hoon Jeong, "Professional Baseball Spectator's Analysis and Prediction by Using Artificial Neural Networks Model and Logistic Regression Model," Korean Journal of Sport Science, Vol.26, No.1, pp.104-121, 2015.
  19. B. Karlik and A. V. Olgac, "Performance analysis of various activation functions in generalized MLP architectures of neural networks," International Journal of Artificial Intelligence and Expert Systems, Vol.1 No.4, pp.111-122, 2011.
  20. 2016 KBO Year Book [Internet],
  21. Naver Sports [Internet], 2011.
  22. J. Han, M. Kamber, and J. Pei, "Data Mining: Concepts and Techniques," MA: Morgan Kaufmann, 2012.
  23. T. Hegazy, O. Moselhi, and P. Fazio, "Developing practical neural network applications using back-propagation," Journal of Microcomputers in Civil Engineering, Vol.9, No. 2, pp.145-159, 1994.
  24. H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio, "An empirical evaluation of deep architectures on problems with many factors of variation," in Proceedings of the 24th International Conference on Machine learning, ACM, 2007.
  25. A. Y. Ng., "Feature selection, L1 vs. L2 regularization, and rotational invariance," in Proceedings of the 21st International Conference on Machine learning, 2004.
  26. R. J. Hyndman, and B. K. Anne, "Another look at measures of forecast accuracy," International Journal of Forecasting, Vol.22, No.4, pp.679-688, 2006.
  27. Tamas D. Gedeon, "Data mining of inputs: analysing magnitude and functional measures," International Journal of Neural Systems, Vol.8, No.2, pp.209-218, 1997.