Particle advection is one of the foundational algorithms for visualization and analysis and is central to understanding vector fields common to scientific simulations. Achieving efficient performance with large data in a distributed memory setting is notoriously difficult. Because of its simplicity and minimized movement of large vector field data, the Parallelize over Data (POD) algorithm has become a de facto standard. Despite its simplicity and ubiquitous usage, the scaling issues with the POD algorithm are known and have been described throughout the literature. In this paper, we describe a set of in-depth analyses of the POD algorithm that shed new light on the underlying causes for the poor performance of this algorithm. We designed a series of representative workloads to study the performance of the POD algorithm and executed them on a supercomputer while collecting timing and statistical data for analysis. We then performed two different types of analysis. In the first analysis, we introduce two novel metrics for measuring algorithmic efficiency over the course of a workload run. The second analysis was from the perspective of the particles being advected. Using particle centric analysis, we identify that the overheads associated with particle movement between processes (not the communication itself) have a dramatic impact on the overall execution time. In the first analysis, we introduce two novel metrics for measuring algorithmic efficiency over the course of a workload run. The second analysis was from the perspective of the particles being advected. Using particle-centric analysis, we identify that the overheads associated with particle movement between processes have a dramatic impact on the overall execution time. These overheads become particularly costly when flow features span multiple blocks, resulting in repeated particle circulation between blocks.