Once again, another question that came in via Quora. The issue at hand is, what do you do to balance experts and novices in a game? Especially if there are persistent elements like leaderboards in the design, which tend to cement experts towards the top?
This is a big issue as games become more persistent and emphasize multiplayer aspects more heavily. Single-player games now swim in a soup of constantly connected profiles with all sorts of achievement and expertise data, effectively rendering them all multiplayer via the addition of a metagame. And we should not forget: the average player is below average; or to be more precise, the median player will have a win-loss record that is lower than the mean or average win-loss record, because the high-skill players win a disproportionate percentage of the match-ups. This results in the mode for the win-loss record curve being “loss.” (For more on how Pareto curves manifest in this sort of persistent environment, I refer you to my 2003 talk on “Small Worlds” [PDF]).
This sort of accumulated record of expertise can serve as a huge disincentive to participate. Novices will look at high ratings and consider the game hopeless. Nobody likes feeling inadequate. And of course, once in an actual game session in any sort of competitive scenario, it is rare for the match to actually be between perfectly matched opponents. It doesn’t even take a significant skill gap for an accumulated win-loss record between a novice and a ninja squirrel to begin to look pretty dismal. And of course, in skill-based systems that lack infrastructure, people can try to hide their ratings — that’s the basis behind being a pool shark.
There is no way known to solve this issue. In fact, balancing arbitrary teams, for example, is an NP-Hard problem. Fortunately, there’s a pretty standard grab-bag of tricks to ameliorate the issue: