@article{ART003197399},
author={Tai-Sung Hur and Minsuk Oh},
title={A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2025},
volume={30},
number={4},
pages={11-18},
doi={10.9708/jksci.2025.30.04.011}
TY - JOUR
AU - Tai-Sung Hur
AU - Minsuk Oh
TI - A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League
JO - Journal of The Korea Society of Computer and Information
PY - 2025
VL - 30
IS - 4
PB - The Korean Society Of Computer And Information
SP - 11
EP - 18
SN - 1598-849X
AB - This study developed and analyzed ensemble learning-based prediction models for forecasting attendance in the KBO League. Using KBO League data from 2022 to 2024, we collected variables such as team rankings, winning rates, consecutive wins/losses, search volume, stadiums, and home/away games, with the attendance ratio compared to stadium capacity set as the target variable. In the data preprocessing phase, Monday games were excluded, and the home/away attendance ratio was set to 7:3 to enhance model realism. Among various ensemble models compared, including Linear Regression, Random Forest, XGBoost, and LightGBM, the LightGBM model showed the best performance with an RMSE of 8.39 and R² Score of 0.783. Feature importance analysis revealed that online search volume (28.17%) and winning rate (25.17%) had the most significant impact on attendance, while team (10.57%) and day of the week (9.73%) also showed meaningful influence. Additionally, SHAP (SHapley Additive exPlanations) analysis provided insights into the directional impact of each variable on predictions, particularly revealing that the home/away factor had a stronger influence than expected through interactions with other variables. This study is significant in providing a practical prediction model that can assist KBO teams in establishing attendance strategies and making marketing decisions.
KW - KBO;Ensemble Learning;Attendance Prediction;LightGBM;SHAP analysis
DO - 10.9708/jksci.2025.30.04.011
ER -
Tai-Sung Hur and Minsuk Oh. (2025). A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League. Journal of The Korea Society of Computer and Information, 30(4), 11-18.
Tai-Sung Hur and Minsuk Oh. 2025, "A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League", Journal of The Korea Society of Computer and Information, vol.30, no.4 pp.11-18. Available from: doi:10.9708/jksci.2025.30.04.011
Tai-Sung Hur, Minsuk Oh "A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League" Journal of The Korea Society of Computer and Information 30.4 pp.11-18 (2025) : 11.
Tai-Sung Hur, Minsuk Oh. A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League. 2025; 30(4), 11-18. Available from: doi:10.9708/jksci.2025.30.04.011
Tai-Sung Hur and Minsuk Oh. "A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League" Journal of The Korea Society of Computer and Information 30, no.4 (2025) : 11-18.doi: 10.9708/jksci.2025.30.04.011
Tai-Sung Hur; Minsuk Oh. A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League. Journal of The Korea Society of Computer and Information, 30(4), 11-18. doi: 10.9708/jksci.2025.30.04.011
Tai-Sung Hur; Minsuk Oh. A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League. Journal of The Korea Society of Computer and Information. 2025; 30(4) 11-18. doi: 10.9708/jksci.2025.30.04.011
Tai-Sung Hur, Minsuk Oh. A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League. 2025; 30(4), 11-18. Available from: doi:10.9708/jksci.2025.30.04.011
Tai-Sung Hur and Minsuk Oh. "A Comparative Study of Ensemble Learning Models for Predicting Attendance in the KBO League" Journal of The Korea Society of Computer and Information 30, no.4 (2025) : 11-18.doi: 10.9708/jksci.2025.30.04.011
