In complex work domains and organizations, understanding schedule-ing dynamics can ensure objectives are reached and delays are mitigated. In the current paper, we examine the scheduling dynamics for NASA’s Exploration Flight Test 1 (EFT-1) activities. For this examination, we specifically modeled simultaneous change in percent complete and estimated duration for a given project as they were included in monthly reports over time. In short, we utilized latent change score mixture modeling to extract the attractor dynamics within the scheduling data. We found three primarily patterns: an attractor at low duration, low percent complete; a saddle that was attractive toward full completion and repelled duration away from five months, and an attractor at full completion and high duration. We replicated these three patterns using multilevel modeling. Then, we examined how task dependencies, in terms of the number of predecessors and successors, affected the probability of exhibiting a given pattern over time. Thus, we offer a flexible method for understanding the patterns that can characterize scheduling dynamics as well as other dynamical systems. Several recommendations for future directions are discussed.