벌점화 회귀분석을 이용한 English Premier League의 승점 예측모형 개발 및 경기력 요인 탐색

Abstract

Advancements in Information Communication Technology during the Fourth Industrial Revolution have led to rapid progress in soccer-related industries and research. Various countries have professional soccer leagues devoted to securing victory. These activities include studies that identify the performance factors that significantly influence the outcome of matches. Researchers often use linear regression analysis based on least-squares estimation for this process, but this method has several issues. As an alternative to this, penalized regression analysis that minimizes the penalty-pointed residual sum of squares has been developed as an alternative. However, there are few studies using the penalty point regression analysis to predict points in soccer. Therefore, in this study, theoretical contents were introduced to activate the penalty point regression analysis, and further examples of application were presented by empirically analyzing 2020-2021 EPL(EPL: The Football Association Premier League Limited) data. It was found to be a statistical predictive model. According to the results, the LASSO model and elastic net models were more suitable statistical prediction models from the perspective of the root mean square error (RMSE). Additionally, among the 19 variables used in the analysis, defensive factors such as tackles, clean sheets, and saves were identified as critical performance factors that impacted the score. Based on the results of this study, in order to enable more in-depth research in the future, the researchers hope that the EPL website will provide activity data, such as the total distance traveled, number of sprints, instantaneous speed, and distance between attackers and defenders.