As some of you might know I've been working on a distribution to address this problem for a while now. Unfortunately there's been a family trauma recently and I haven't quite got it ready for public showing. While the
how is very complicated and the tools to get the point across are still being developed, I can at least mention
what it does.
Basically the rough idea is a simple scaling increase in drop rate that kicks in after you've reached a point where you "should have" gotten a card by now
The problem with this design, depending on how much you increase the chance after you pass the "intended number of kills for a card drop", is that you end up shifting the new average drop point such that the card actually becomes overall easier to obtain.
The distribution I have designed (and made fully functional, devs please PM me if you would straight up like the function and code without the article that comes with it) makes sure that not only the point where you "should have" gotten the card is exactly that of the previous system (give or take same random errors), but you can weight said distribution to a point where you can guarantee a number of kills by which
everyone will have the card.
If that point is set to be the intended average, everyone gets the card at that number of kills. Simples.
If that point is "large" (an approximation used in physics where a value is big enough to act similarly to infinity), the drop distribution approximates that of the geometric -
the same distribution that is currently used.
Ideally that point will be somewhere in the middle, and you can play around with values to suit your needs.