The difference is not prior probability, but salience. If S is the winning sequence and pred(S) is the event that the winning sequence was predicted, then P(S | pred(S)) = P(S); whether or not you predict it, the probability of that particular sequence doesn't change.
However, a winning sequence that you predicted is far more salient to you than a typical winning sequence. Salience is about how relevant an event is to your personal situation and goals. You put time and effort into your prediction, and the "payoff" of your action was wildly out of expectation with the time and effort put in. Even if you didn't bet, but merely observed the result, you were still trying to get the right prediction, so it's still a psychological "payoff" of sorts if you do happen to get it.
A similar, though slightly different, situation often comes up in science; if you predict a pattern and then test it and indeed the pattern is there, this is a far more significant finding and much better science than if you had simply observed the same pattern in your data, after the fact.
Let me explain. Suppose you are a scientist and you observe some data, and you notice that the data fits a certain pattern that is very unlikely based on your background assumptions. Wow, you say, the chances of that happening under our background assumptions are incredibly low! I'm really onto something, and we'll have to revise our background assumptions!
But that's fallacious reasoning. The problem is that there are a very large number of individually unlikely patterns the data might fit, and so the chance that the data fits any particular unlikely pattern (under the background assumptions) could actually be fairly high. So you have not demonstrated a need to revise the background assumptions.
It would be far more impressive if you had predicted beforehand that the (unlikely-under-background-assumptions) pattern would occur, and it did. Because then, there's only one pattern you're testing for, and it is indeed very unlikely it would occur. So if your prediction comes true then you have indeed made a good case for revising the background assumptions.