Recently, interaction techniques in which the user selects screen targets by matching their movement with the input device have been gaining popularity, particularly in the context of gaze interaction (e.g. Pursuits, Orbits, AmbiGaze, etc.). However, though many algorithms for enabling such interaction techniques have been proposed, we still lack an understanding of how they compare to each other. In this paper, we introduce two new algorithms for matching eye movements: Profile Matching and 2D Correlation, and present a systematic comparison of these algorithms with two other state-of-the-art algorithms: the Basic Correlation algorithm used in Pursuits and the Rotated Correlation algorithm used in PathSync. We also examine the effects of two thresholding techniques and post-hoc filtering. We evaluated the algorithms on a user dataset and found the 2D Correlation with one-level thresholding and post-hoc filtering to be the best performing algorithm.