The probability models in question don’t require identical probabilities, just conditional ones.
In terms of the dating analogy, suppose there were 10 women looking for a date, and 100 men looking for a date from one of those 10 women. The men can only accept one date.
We know 90 of the men will get no dates no matter what they do, since there are only 10 dates total. Like even if all 100 men ask out all 10 women, necessarily 90 of the men must get no date offers.
OK, but then if the asking out process happens simultaneously, particularly desirable men may get more than one date offer. As an aside, this will mean the women have to figure out how to make more than one offer. Perhaps they will invent yield models, waitlists, binding early decision, and so on.
OK, so men, understanding the odds, then each ask 4 women. Some immediately get multiple offers, lots of women like them. Some only get one offer, but that works. But how many women will get the best date they can out of the 400 total offers?
Well, potentially all of them, particularly if they have good enough yield models and waitlists and such.
And this is where women having different preferences actually helps speed this along, if the men can figure out who they might match up better with. So maybe the women try to describe what they prefer, and the savvier men do a good job paying attention. Again, potentially all 20 dates available get filled this way after as few as 4 offers each by the 100 men.
OK, but if 4 doesn’t quite do it, what about 5? 6? Each increment represents 100 more offers total, many more opportunities for women to find their matches by the time yield and waitlists and such unfold.
Again, plausible models are going to make it extremely unlikely the women will need each man to make 10 offers in order to find the 10 they collectively prefer. Of course 1 a man won’t likely work either. But the matching process will likely be complete with something substantially more than 1 and substantially less than 10. Call that N.
OK, so now you are some individual man, and I gain the knowledge that you did your best to identify the best matches, asked out N, and got zero offers. I now know you are in the 90 and not the 10. That’s going to happen to most of you.
OK, last step, but then you ask out the remaining worse matches too, so ask out all 10. Does that change my assessment? No. We already know from the fact your best N matches failed that you are in the 90.
That analogy may be more trouble than it is worth, but that’s getting closer to something realistic. The really critical underlying facts to make such models plausible are that there are far more applicants hoping for a match than matches available, and that the entities deciding who will get offers can communicate individualized preferences, and then use yield models and waitlists and such to get the offers they actually want to take.