머신러닝 분류 모형을 이용한 FIBA 여자농구 아시안컵 대회의 승패 예측 및 요인 분석에 관한 연구 (A Study on Prediction and Factor Analysis of FIBA Women"s Basketball Asian Cup Competition Using Machine Learning Classification Model)

서지정보

· 발행기관 : 한국체육과학회
· 수록지 정보 : 한국체육과학회지 / 31권 / 5호 / 1009 ~ 1021페이지
· 저자명 : 예원진, 이성노

초록

The purpose of this study is to predict match results and analyze win/loss factors by combining big data and machine learning classification models using the box scores of the 2015-2021 women's basketball Asian Cup tournament. The subject of this study was a total of 200 game records among the records obtained through the official records of the 2015, 2017, 2019, and 2021 Women's Basketball Asian Cup tournaments, and a total of 22 variables were used to predict win/loss results and analyze win/loss factors. In order to predict the win/loss result of the Women's Basketball Asian Cup competition, five machine learning classification models are used, KNN, Decision Tree, SVM, Logistic Regression, and Random Forest, and predictive performance by model by predicting win/loss results. were comparatively analyzed. In addition, in order to analyze the factors affecting win/loss, the importance of each factor was analyzed using a random forest classification model.
First, when analyzing factors affecting win/loss using box score data, it was considered that total score and efficiency factors should be removed before analysis in order to obtain more accurate factor importance. Second, in the analysis of factors affecting victory and defeat after cleaning dirty data, the number of successful shots (FGM) was found to be the most important factor, followed by the shot success rate (FG%), the two-point success rate (2PTS%), and personal fouls (PF)，interception (STL), and so on. Third, in predicting win-loss results, the logistic regression model showed optimal prediction performance than the KNN, decision tree, SVM, and random forest models, and showed 95% prediction accuracy and 0.95 F1 score.

영어초록

The purpose of this study is to predict match results and analyze win/loss factors by combining big data and machine learning classification models using the box scores of the 2015-2021 women's basketball Asian Cup tournament. The subject of this study was a total of 200 game records among the records obtained through the official records of the 2015, 2017, 2019, and 2021 Women's Basketball Asian Cup tournaments, and a total of 22 variables were used to predict win/loss results and analyze win/loss factors. In order to predict the win/loss result of the Women's Basketball Asian Cup competition, five machine learning classification models are used, KNN, Decision Tree, SVM, Logistic Regression, and Random Forest, and predictive performance by model by predicting win/loss results. were comparatively analyzed. In addition, in order to analyze the factors affecting win/loss, the importance of each factor was analyzed using a random forest classification model.
First, when analyzing factors affecting win/loss using box score data, it was considered that total score and efficiency factors should be removed before analysis in order to obtain more accurate factor importance. Second, in the analysis of factors affecting victory and defeat after cleaning dirty data, the number of successful shots (FGM) was found to be the most important factor, followed by the shot success rate (FG%), the two-point success rate (2PTS%), and personal fouls (PF)，interception (STL), and so on. Third, in predicting win-loss results, the logistic regression model showed optimal prediction performance than the KNN, decision tree, SVM, and random forest models, and showed 95% prediction accuracy and 0.95 F1 score.

참고자료

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