머신러닝 기반 골프선수의 경기 스타일 분류 및 예측 시스템 개발

초록

PURPOSE This study sought to classify the playing styles of KPGA players based on performance-related technical factors and develop a supervised learning model that automatically predicts and classifies these styles. METHODS Performance data were gathered from KPGA Korean Tour players between 2015 and 2024, focusing on six key technical indicators. Distinct playing styles were identified by standardizing the variables using z-scores and then clustering them using the K-means algorithm. Based on the clustering results, predictive classification models were built by applying five supervised learning algorithms—decision tree, random forest, K-nearest neighbors (KNN), support vector machine (SVM), and multinomial logistic regression. Model performance was then evaluated using accuracy, precision, recall, and F1-score, with generalizability assessed via five-fold cross-validation. RESULTS Four playing style clusters were obtained, each labeled according to players’ technical characteristics: “overall weakness type,” “distance-deficient but technically proficient type,” “accuracyoriented type,” and “power and risk-management type.” The multinomial logistic regression model showed the highest predictive performance, followed by SVM, KNN, random forest, and decision tree. CONCLUSIONS This study confirmed that KPGA players can be characterized into four distinct playing styles based on their technical performance data and that these styles can be effectively classified and predicted by supervised learning models. These findings highlight the models’ practical applicability in personalizing training strategies, developing course-specific game plans, and contributing to the advancement of AI-based sports analytics systems.

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