Atmospheric turbulence effects greatly reduce the resolution that can be obtained by systems that must form images through the atmosphere. Postdetection image reconstruction techniques, such as speckle imaging, and deconvolution from wavefront sensing provide a means of overcoming some of these effects by postprocessing sets of short-exposure image measurements. Previous work has shown that using image quality metrics to select the best subset of an ensemble of measured images to process can yield better results than processing all the measured data. In this paper we extend this idea to select a subset of frames using metrics derived from wavefront sensor (WFS) measurements made simultaneously with the image measurements. This approach to using WFS data may allow the amount of data that is saved to be reduced, or automate the process of sifting the data for the best subsets to process. Our results indicate that the WFS-based metrics are consistent with the image-quality-based metrics.