In this work, we relate the extent and quality of inter-agent communication and the overall performance in teams of multiple agents. Specifically, we examine the RoboCup Soccer Simulation 2D League, and carry out multiple simulation experiments against two evenly matched teams. For each simulated run (a 2D soccer simulation game), we generate the communication efficiencies (i.e., communications sent/communications received) for each agent pair. Applying linear regression and principal component analyses, we then correlate these efficiencies with measures of performance (i.e., goals scored and goals conceded), enabling the construction of inter-agent communication networks. Analysis of these networks highlights the microscopic player-to-player and macroscopic role-to-role communications correlated with performance. The approach determines the salient pathways within inter-agent communications which globally affect the coordination and the overall performance in multi-agent teams.