In daily life, sensorimotor integration processes are fundamental for many cognitive operations. The pursuit tracking paradigm is an ecological and valid paradigm to examine sensorimotor integration processes in more complex environments/tasks. However, the analysis of pursuit tracking performance is complicated, and parameters quantified to examine performance are sometimes ambiguous regarding their interpretation. We introduce a new open-source algorithm (TRACK) to calculate a new tracking error metric, the spatial error, based on the identification of the intended target position for the respective cursor position. The identification is based on assigning cursor and target direction changes to each other as key events, based on the assumptions of similarity and proximity. By applying our algorithm to pursuit-tracking data, we show that the spatial error replicates known effects such as learning or practice effects. Beyond replication of established findings, we show that the spatial tracking error fits our behavioral data better than the temporal tracking error and provides new insights and parameters for the investigation of pursuit-tracking behavior. Our work provides an important step towards fully utilizing the potential of pursuit tracking tasks for research on sensorimotor integration processes.