Nice to see you post here, Kirk!
SportVu data is clearly an important part of the frontier.
A very interesting data source. I note you currently were using the data simply for X,Y locations of events, a relatively modest usage. The ability to spatially track everything on the court will have far more unique uses, I'm sure.
What are your thoughts on the dataset so far? How great will the challenges be when attempting to use the temporal axis as well, working with the full temporal-spacial continuum for non-discrete analysis? Will using the full dataset (not just discrete events) even be useful for many things, in your opinion?
Do you have any idea if the data will be available more widely at any point?
I think this study is interesting because it really makes us think about missed field goals in new ways. As someone pointed out in another thread, the "Kobe-assist" effect is something worth exploring.
The bottom line is that not all missed shots are created equal. Some misses are better than others; part of this can be explained by spatial approaches, part of it can't.
This study largely ignores shot context, which is a limitation.
Or the Derick Rose assist, as you pointed out on Twitter.
Investigating positioning of rebounders would be very interesting--when a shot goes up, how quickly/much can the best rebounders work toward the likely rebound location, and how well can they secure it? Understanding of rebounding currently is certainly limited--who actually "gets" rebounds, contributing additional chances/possessions, and who simply receives them uncontested. Contested rebounds... there is a ton to explore with this dataset simply within this area.
I look forward to what you can do with this, Kirk!