Abstract
A player’s role for a team can be distinct from their playing position. Positions are generally attributed based on where the players line-up relative to their formation, whereas roles can be defined by frequency of their actions. Hence, the method presented in this research, attributed player roles based on event data. Player role feature selection involved a semi-supervised machine learning approach, that extracted feature importance in the form of Shapley values. These values helped define the KPIs for pressing attacking players. By using the proposed role similarity approach, it is possible for recruitment departments to identify players that occupy similar roles as current players. Furthermore, the evolution of player roles across time can be evaluated, which has applications with performance analysts, as they can interrogate the constituent roles of each player and its influence on overall team performance. Hence, the proposed method can help uncover the optimal KPIs for a given set of roles, while having practitioner applications within elite-level performance analysis and recruitment departments. Future methods should combine physical data sources, such as from tracking data, to enable greater specificity in player role classification.