Ha ha yeah, sorry, the talk is a bit long – I talk too much 
It’s to make sure the GRW “walks” towards its priors instead of away from it when one doesn’t have much data. And the nice thing is that the prior is actually the result of another model, called “the fundamentals” model.
Linzer uses the “Time for change” model, but basically it’s a regression of past election results on a domain-expert-informed selection of socio-economic variables – so, yeah, there are actually two models 
Indeed, nothing in the data trends that way, but it’s because we don’t have any data (i.e polls) from 2 months in the future. When that happens, the beauty of this model is that it reverts to the fundamentals forecast (which doesn’t contain polls, only socio-econ variables available well in advance of the election) to still be able to make a forecast. Otherwise, you would just get a huge, uninformative uncertainty.
If you’ve already used Gaussian Processes, you can draw a parallel here, as GPs revert back to their mean when data become sparse.
Restating what I wrote above, in case it wasn’t clear: the model is reverting to the state-level fundamentals forecast when data become sparse.
Definitely a good strategy 
Good luck and happy holidays 
