Okay here we go. So first let us introduce some notation.
Let P(A) be the Probability P that some event A happens. Then P(A∩B) is the probability that both A AND B happen. P(A/B) is the probability that A happens, given that B has already happened.
Now the general formula relating all these is: P(A∩B) = P(A/B) * P(B);
This makes sense if you think of it logically. For both A and B to happen, first one event must happen, and then the other must happen given that the first one did.
In this case, let event B be: A player is a werewolf in the first role distribution. And event A: A player is a werewolf in the second role distribution.
There are two ways of looking at it. A priori or before the fact: P(A∩B) is indeed very low as you said(1/16), but that is not what we are looking for. We want P(A/B), or the probability of a player being a werewolf now given what we know: That they were a werewolf before. Using the forumla above we get:
P(A/B) = P(A∩B) / P(B) = (1/16) / (4/16) = 1 / 4 = 4 / 16, same as everyone else.
If this doesn't seem intuitively obvious to you, you can apply the theorem after-the-fact and try to find P(A∩B) with all the information that we know.
P(A∩B) = P(A/B) * P(B);
P(A/B) is, of course, still 4/16 or 1/4, that is a constant. But P(B) = 1, obviously, since we know for sure that the player was a werewolf the first time.
Therefore: P(A∩B) = 1/4 * 1 = 1/4 or 4/16, same as everyone else.
In other words, a player is unlikely to be a werewolf twice in a row for the simple reason that they must pass the 1/4 test twice. But anyone who was a werewolf the first time has already passed that once, so it is irrelevant now. This confirms what we intuitively know to be obvious: The chance of someone being a werewolf the second time is unrelated to whether or not they were a werewolf the first time.