군집분석을 통한 K리그 축구팀 플레이스타일 분류
Identification of playing styles for K-League football clubs through cluster analysis
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초록
본 연구는 2020 K리그 경기에서 발생한 패스 관련 분석인자들을 이용하여 군집분석을 통해 K리그 팀들의 플레이스타일을 알아보고자 하였다. 2020 K리그 모든 팀들의 전 경기(스플릿 후 경기 제외)를 대상으로 하였으며, 연구의 대상이 된 경기 수는 총 132경기였으며, 양 팀의 자료를 각각 고려하였다(n=264). K리그 프로축구연맹 ‘데이터포탈’에서 제공받은 18개의 패스 관련 분석인자들을 Microsoft Office Excel 2007을 이용하여 정리하였고, 그 후 R 3.6.2를 이용하여 자료 처리하였다. 통계적 검증을 위하여 기술통계 분석(descriptive statisticsanalysis)을 실시한 후, 데이터 마이닝 기법 중 하나인 k-평균 군집분석(k-means cluster analysis)과 교차분석(cross-tabulation analysis)을 실시하였다. 본 연구의 군집분석을 통하여 얻어진 군집의 수는 3개였다. 절반 이상의 팀들이 군집 1에 속하였고, 군집2(전북, 울산, 강원)와 군집3(대구, 광주, 인천)에는 각각 3팀이 속하였다. 최상위 팀인 1위 팀 전북과 2위 팀 울산이 속한 군집2는 다른 군집들과 비교해 공격 1/3지역 패스 비율, 숏 패스 비율, 전진 패스 비율을 제외한 나머지 15개의 분석인자들에서 가장 높은 평균값을 나타냈고, 군집3의 경우 가장 낮은 평균값을 보였다. 분석인자들 간의 유사성을 이용하여 군집을 나누는 방법으로 직접적인 팀의 플레이스타일을 표현하는데 한계가 있지만, 본 연구에서 사용된 분석인자들을 통해 비슷한 유형의 팀들을 군집하는데 의미가 있다.
Abstract
The purpose of this study was to identify the playing styles of football clubs in K-League through cluster analysis using performance indicators related to pass. All matches excepted to split matches were used for analysis and all data were provided from Korea Football League(n=264). All data were preprocessed on Microsoft Office Excel 2007 and statistical analysis was conducted on R 3.6.2. Descriptive statistical analysis was firstly used to calculate means and standard deviations for each performance indicators and then k-means cluster analysis, one of the data mining method, was conducted to identify clusters. Finally, cross-tabulation analysis was used to identify K-League teams into each cluster. Three clusters were identified and Jeonbuk, Ulsan and Gangwon was included in cluster 2 whilst Daegu, Gwangju and Incheon was included in cluster 3. The other teams were included in cluster 1. Cluster 2 had greater performance indicators related to pass rather than other clusters. Although cluster analysis, grouping performance indicators in such a way that performance indicators in the same cluster are more similar each other compared to other clusters, could not determine accurate playing styles in football, it is literally meaningful to group the similar type of teams. There needs to be a great interpretation of the characteristics of the formed clusters.
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