An animal's maneuverability will determine the outcome of many of its most important interactions. A common approach to studying maneuverability is to force the animal to perform a specific maneuver or to try to elicit maximal performance. Recently, the availability of wider-field tracking technology has allowed for high-throughput measurements of voluntary behavior, an approach that produces large volumes of data. Here, we show how these data allow for measures of inter-individual variation that are necessary to evaluate how performance depends on other traits, both within and among species. We use simulated data to illustrate best practices when sampling a large number of voluntary maneuvers. Our results show how the sample average can be the best measure of inter-individual variation, whereas the sample maximum is neither repeatable nor a useful metric of the true variation among individuals. Our studies with flying hummingbirds reveal that their maneuvers fall into three major categories:​ simple translations, simple rotations and complex turns. Simple maneuvers are largely governed by distinct morphological and/or physiological traits. Complex turns involve both translations and rotations, and are more subject to inter-individual differences that are not explained by morphology. This three-part framework suggests that different wingbeat kinematics can be used to maximize specific aspects of maneuverability. Thus, a broad explanatory framework has emerged for interpreting hummingbird maneuverability. This framework is general enough to be applied to other types of locomotion, and informative enough to explain mechanisms of maneuverability that could be applied to both animals and bio-inspired robots.