Fitts’ law is a behavioral model, used to design protocols and analyze data from pointing experiments. These are usually conducted in HCI to evaluate input performance. We recently proposed an alternative method to characterize input performance, called the method of PVPs in 1D, based on 1) a dual-minimization protocol, and 2) an analysis of the variability of entire trajectories. We extend the method in 2D; our contributions include new metrics, a new protocol, and a Python library. We also present the results of a controlled experiment where the new method is validated using three devices (mouse, touchpad, controller): effect sizes in the 2D case replicate those previously found. We also propose a comparison between Fitts’ law and our novel evaluation: the method of PVPs provides more information than Fitts’ law, and can predict its parameters. We discuss how this new method may relieve open problems of Fitts’ law.