This is a good point actually, and also a reason where having some method where changes to the python code, could also update the js frontend code (as opposed to manually translating it 
).
Originally it was based on the python code above here, which has been updated since.
That looks like it’s the opponents’ (whites) phi variable now, which was called deviation.
That probably makes more sense.
I suppose, if we’re not understanding each other, we can try work it out My guess is that it’s the same E used in Elo, the expected win rate (which is the probability for one player given that we’re not looking at draws) and the random variable is just win=1, loss=0.
It’s very much used in the same way, in the rating adjustments, the g factor weights the contribution of the score from the expected score, and then overall that seems to be scaled by some variance factor.
I suppose if we dig deeper than the glicko and glicko 2 pdfs, which seem more like example calculations, and look at the derivation in the other paper
http://www.glicko.net/research/glicko.pdf
with
from earlier.
I think functionally it looks the same, but is it being evaluated at different parameters?
In the glicko pdf you mentioned it does mention something “expected” on page 5
