스포츠 데이터 분석에 적용된 통계 및 데이터마이닝 기법에 대한 연구동향

The purpose of this paper is to analyze and compare statistical analysis and data mining techniques used for sports data analysis by investigating previous studies, and to provide the analyzed results to researchers who study sports data analysis. The analysis was performed based on 84 literatures collected from ACM Portal, IEEE xplore, Pubmed, and RISS for Higher Education. Statistics are used to standardize the records for performance evaluation of players and teams and to identify related factors of win and loss based on the statistical methods such as t-test, ANOVA, correlation, regression and so on, while data mining and machine learning algorithms are used to analyze unstructured data such as image and text, and to predict the outcome of games. In conclusion, sports data analysis has become one of the promising research fields as the technical convergence of sports and other studies progresses quickly. We predict that research and development related to sports data analysis will increase continually in the future.