The main goal of this paper is to provide an effective approach to quantify patterns of team managers so that they can be learned and compared by machines. Traditionally, team managers are analyzed and compared based on the data collected from surveys and questionnaires. Then, managers are usually represented by managerial roles/skills and analyzed semimanually by human perception. However, it causes two methodological problems: 1) managerial roles and skills are usually isolated and only indirectly related to managerial actions and 2) the perception-based methods cannot provide detailed analysis results since human perception is abstract and lacks stability. In order to solve these problems, we propose a simple but powerful manager representation method which is general enough to cover most manager types. With this, team managers can be learned and compared my machines. Particularly, we represent a manager by managerial actions with flexible feature groups to improve the expandability and description power of the proposed representation model. For manager analysis, we introduce the first matching algorithm which not only returns robust and stable manager similarities but also details the matched parts among managerial action sequences. The efficiency of the proposed methods is substantiated by comparison experiments between machines and human perception.