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2022 FIBA 남자농구 아시안컵 경기결과를 활용한 머신러닝 분류 모형의 예측 성능 비교

Comparison of Prediction Performance of Machine Learning Classification Model Using 2022 FIBA Men's Basketball Asian Cup Match Results

이성노|예원진
한국체육측정평가학회 (Korean Society For Measurement And Evaluation In Physical Education And Sports Science)|2022.09|한국체육측정평가학회지 (The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science)|제24권 제3호|pp. 53-69|DOI 10.21797/ksme.2022.24.3.005

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키워드

머신러닝Machine Learning예측PredictionClassification Model분류 모형남자농구 아시아컵Men's Basketball Asian Cup

초록

이 연구의 목적은 2022 FIBA 남자농구 아시아컵 경기대회의 공식기록(box score)을 사용하여 전통적 측면 에서의 통계적 방법, 데이터마이닝 기법, 머신러닝의 기법을 활용하여 예측 성능을 비교한 것이다. 본 연구의 대 상은 2022 FIBA 남자농구 아시아컵 경기대회의 공식기록을 통해 얻어지는 기록 중에서 총 72개 경기기록이었 으며, 총 20개의 변수를 통해 경기 승패 결과 예측을 하였다. 남자농구 아시아컵 경기대회의 승패 결과를 예측 하기 위해 KNN(K Nearest Neighbor), Decision Tree, Support Vector Machine(SVM), Logistic Regression, Random Forest 5가지 분류 모델을 사용하였다. 이 연구의 자료수집과 처리를 위하여 통계프로그 램 Python 3.10.1 버전을 라이브러리와 함께 사용하였고, 얻은 결과는 다음과 같다. 첫째, 모델별 예측 결과에 서는 SVM 모델이 KNN, Decision Tree, Random Forest, Logistic Regression 모델보다 최적의 예측 성능 을 나타냈고 86.67%의 예측 정확도 및 0.868의 F1 점수를 보였다. 둘째, Random Forest 분류 모델을 이용 하여 2022 남자농구 아시아컵 경기대회의 승패 결과를 예측했을 때, 데이터 세트의 샘플 개수가 충분하지 않기 때문에 과적화(Overfitting) 현상이 발생했다. 이 연구의 연구 결과를 토대로 향후 사례 수를 증가하여 더 많은 데이터가 모델의 정확도를 높이는 동시에 과적합 가능성을 줄일 수 있다고 사료되었다. 그리고 더 정확한 예측 결과를 얻기 위하여서 머신러닝뿐만 아니라 딥러닝과 관련한 연구도 필요할 것으로 판단되는 바이다.

Abstract

The purpose of this study is to compare predictive performance using traditional statistical methods, data mining techniques, and machine learning techniques using the box scores of the 2022 FIBA Men's Basketball Asian Cup Games. The subject of this study was a total of 72 match records among the records obtained through the official records of the 2022 FIBA Men's Basketball Asian Cup, and the outcome of the match was predicted through a total of 20 variables. Five classification models were used to predict the outcome of the men's basketball Asian Cup competition: K Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM), Logistic Regression, and Random Forest. For the data collection and processing of this study, the statistical program Python 3.10.1 version was used together with the library, and the results obtained are as follows. First, in the prediction results for each model, the SVM model showed the optimal prediction performance than the KNN, Decision Tree, Random Forest, and Logistic Regression models, and showed 86.67% prediction accuracy and 0.868 F1 score. Second, when the Random Forest classification model was used to predict the win/loss result of the 2022 Men's Basketball Asian Cup, overfitting occurred because the number of samples in the data set was not sufficient. Based on the results of this study, it was considered that more data by increasing the number of cases in the future can increase the accuracy of the model and reduce the possibility of overfitting. And in order to obtain more accurate prediction results, it is judged that research related to deep learning as well as machine learning is necessary.

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