As the truism goes, “You can't manage what you don't measure.â€� However, assessing performance in training exercises has classically presented a measurement challenge, made more complex by the paucity of timely, relevant, comparable data on the training audience's performance. Even as the field of learning analytics becomes increasingly sophisticated, military training exercises continue to be assessed in largely subjective and superficial ways. In short, while we may know if the training was completed, it is difficult to objectively answer the basic question: did the exercise do any good? xAPI is an emerging capability to support learning analytics, but until recently has remained largely untested as a solution for delivering comparable results across complex multi-platform asynchronous learning and performance data feeds at scale. Viking 18, a large multinational civil-military exercise, aspires toward full operational integration of Advanced Distributed Learning (ADL) as an integral part of the exercise experience, including the associated learning analytics supported by xAPI. This paper presents a case study and lessons learned from the implementation of xAPI in the Viking 18 exercise. It also delivers a summary of the resulting Viking 18 learning analytics, including data from e-learning courses matched against quantitative observation data from the exercise management tool, with the aim of gaining insight on the relationships between training and performance against exercise objectives. As such, we crack open the door to aggregation of exercise performance data in support of operational and strategic planning. Analysis clearly suggests a pattern of enhanced training outcomes by units with higher rates of Introduction to Viking (pre-training e-learning) course completion.