Unsupervised learning to identify most common basketball plays from spatial data
This is a really interesting problem! Like most really interesting problems, you're unlikely to find an out-of-box solution for this, but I think the field of graph/subgraph similarity has some promise here. I'll go into more detail, but, at a high level, I think you can view players' paths during a play as a collection of five traversals through x,y space, with a vertex being any x,y point you have available (presumably there's some level of time granularity here), and an edge describing a player's movement from one point in x,y space to the next over time. It should be possible to cluster your data using a similarity metric (e.g., see Koutra et al., 2011 for a nice overview). Then, using your own domain expertise, you should be able to identify whether the clusters you've derived have some real-world meaning in basketball.