Jabba, I used analogy of a found-penny-on-the-street because it easier to reason about evidence when the evidence isn't ones own existence. But the problems are otherwise analogous.
If you want to understand why the likelihoods of your hypothesis are each 1, set aside those hypotheses for now, and concentrate on understanding the penny problem. First, make sure you understand the problem; then see if you can understand why the likelihoods are each 1. Then, and only then, try and see the similarity between the penny problem and your problem.
Hypothesizing after the results are known is a fallacy because, after observing the evidence, you change they hypothesis from the original, general hypothesis to a specific hypothesis that "just happens to" predict precisely the observed evidence.
One more analogy, which I've used before. Joe is told that a deck of cards sitting face-down on a table has a 50-50 chance of having either been shuffled or intentionally set in some order. Joe turns over the cards and observes the sequence: 6S, AH, 2D, 9D, ...., and he reasons as follows. If the cards had been shuffled, the probability of their winding up in this sequence would be about 1 in 10^68. However, if the cards had been stacked in this order, then the chance that they would be in this order would be 1. Therefore, the cards were almost certainly stacked.
A person who reasoned this way would always guess that the cards were stacked, but, based on the 50-50 prior odds, he would be wrong half the time. Therefore, there must be some error in his reasoning. Do you see it? Joe changed the hypothesis after he saw the sequence of the cards. Before seeing the sequence, the "stacked" hypothesis was H1 that the "cards care stacked in some sequence." After he saw the sequence the hypothesis became H2 the "cards are in this sequence." The original hypothesis did not predict the observed evidence; rather, hypothesis was altered so that it perfectly fit the observation.