In this paper, we explore multiple metrics for the evaluation of time-to-saccade problems. We define a new sampling strategy that takes the sequential nature of gaze data and time-to-saccade problems into account to avoid samples of the same event into different datasets. This also allows us to define novel error metrics, evaluating predicted durations utilizing classical eye-movement classifiers. Furthermore, we define metrics to evaluate the consistency of a predictor and the (modification) of the error over time. We evaluate our method using state-of-the-art methods along with an average baseline on three different datasets.