The The sports betting industry has proven to be one of the most influential areas in the service sector. Despite its significance, the Korean sports betting industry has been neglected which calls for research scrutiny. This research applies machine learning algorithms (Logistic Regression, Random Forest, AdaBoost, GradientBoost, Light-GBM, Multi-Layer Perceptron, Extra GradientBoost) to predict the results of Keirin competition along with sports betting methods. All of the race data generated in 「Gwangmyeong Speedome」 from 2016 to 2022 were collected and preprocessed for empirical analysis using Python. The results imply that the Logistic Regression had the highest accuracy performance among the machine learning algorithms, with an accuracy of 61.18% for the win prediction, 78.51% for perfecta, 42.37% for the quinella, 31.33% for the exacta, 31.63% for the trio, 22.10% for quinella place, and 14.30% for trifecta bet. Light-GBM and GradientBoost demonstrated the second-highest performance among the machine learning algorithms. In conclusion, this research provides an analysis of the machine learning application of Keirin competition based on sports betting methods. We believe this attempt may contribute to the service management research domain by providing actual prediction results of the sports game to consumers that may to sports betting industry expansion.
Keywords : machine learning, sports analytics, sports prediction, sports betting, sports service management
링크 : https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11440236
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