인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.6
- 수록면
- 157 - 192 (36page)
- DOI
- 10.15706/jksms.2023.24.2.007
이용수
초록· 키워드
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.
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목차
- Abstract
- I. 서론
- II. 이론적 배경
- III. 연구방법
- IV. 연구 결과
- V. 논의 및 결론
- 참고문헌