Motion data are curves over time in a 1D, 2D, or 3D space. To analyze sets of curves, machine learning methods can be applied to cluster them and detect outliers. However, often metadata or prior knowledge of the analyst drives the analysis by defining cohorts. Our goal is to provide a flexible system for comparative visual analytics of cohorts in motion data. The analyst interactively defines cohorts by filtering on metadata properties. We, then, apply machine learning and statistical methods to extract the main features of each cohort. Summarizations of these features are visually encoded using, in particular, boxplots and their extensions to functional and curve boxplots, depending on the number of selected dimensions of the space. These summarizations allow for an intuitive comparative visual analysis of cohorts in a juxtaposed or superimposed representation. Our system provides full flexibility in defining cohorts, selecting time intervals and spatial dimensions, and adjusting the aggregation level of summarizations. Comparison of an individual sample against a cohort is also supported. We demonstrate the functionality, effectiveness, and flexibility of our system by applying it to a range of diverse motion data sets.