I spent the day up at Columbia, with the Stream Team, which is Marla Geha (Yale), Kathryn Johnston (Columbia), me, and parts of our research groups. We discussed just-finished papers, next papers, and things that have come up in the literature. On that last point, we spent some time discussing this paper by Gibbons et al on the Sagittarius stream. The paper makes potential inferences based on data, which is Good, but takes as its "data" a very limited set of measurements—a precession angle (between apocenters), two apocenter distances, and a progenitor 6-d position—and nothing else, which is Bad. We discussed the point that the limited set of measurements they used is not even close to a set of sufficient statistics; in particular, Price-Whelan has shown that you can get multiple potentials for any precession angle, and that the overall shape of the stream and the radial velocities of the stars in the stream will distinguish these options. When your data set contains many measurements (as theirs does) and when your model can predict those measurements (as theirs can), you only hurt yourself by using subsets of the data or limited, derived quantities! (I said all of this to Evans and Belokurov a couple months ago.) I don't want to harsh them out too much, though, because the stream literature has been rife with theory papers that don't confront data at all; this paper is a step in the right direction.