Although self-regulatory processes are thought to underpin team performance in esports, current research tends to rely on self-reported and retrospective data particularly when examining self-, co-, and socially shared regulation. These methods limit our ability to observe how regulation unfolds during real-time gameplay. The aim of this pilot study was to assess the feasibility of using latent pattern content analysis of League of Legends match transcripts to examine goal systems and their underlying regulatory processes. The objectives were to determine if an event measure approach to regulation, using a latent pattern content analysis, would be suitable in the (a) analysis and mapping of goal systems and (b) for identifying different types of regulatory cycles in esports using match transcriptions. Findings showed that latent pattern content analysis can be used to map goal systems and identify regulatory cycles and phases. This approach offers insight into how teams pursue goals during gameplay. As a pilot study, these findings demonstrate methodological feasibility rather than generalizability across all skill levels or player populations. © 2025 Human Kinetics