Multi-agent Path Finding (MAPF) is the problem of finding paths for a set of agents to their goals without collisions between agents. It is an important problem for many multi-robot applications, especially in  automated warehouses. MAPF has been well studied, with many algorithms developed to solve MAPF instances optimally. However, the characteristics of these algorithms are poorly understood. No single algorithm dominates the other algorithms and it is hard to determine which algorithm should be used for which instance and for which instances algorithms will struggle to find a solution. In this talk, I will present results from two papers that seek to better understand the performance of MAPF algorithms. The first part of the talk will cover our MAPF Algorithm SelecTor (MAPFAST), a deep learning approach to predicting which algorithm will perform the best on a given instance. The second part of the talk will cover the role the betweenness centrality of the environment plays on the empirical difficulty of MAPF instances.