![]() And table tennis is also, because of this, one of what I believe is few sports where men and women often play head-to-head (even though men generally have much higher ratings, on account of the sport requiring far more strength than you might suspect).Īll of this, plus an additional observation that i've had about games w/ tiers/divisions: player skill is assumed to be normally distributed when that is just so demonstrably not the case- there is a fairly high skill floor to be able to play the game at all, and the right tail (high skill) of the distribution is WAY fatter than the left.Įspecially with well-established, popular games- Chess, League of Legends, Overwatch, etc. It seems that the ability to match players who have never seen each other before, ensuring interesting matches, is part of keeping the game competitive for those in it. Today, someone with that rating isn't even going to be in the top brackets of serious tournament.ĭespite all that, the usefulness of the rating system keeps it in use as a valuable tool. So back in 1991, my wife was in the top 30 women in the USA with a rating in the mid-1700s. Many are saying that for someone of the upper echelons, their rating is maybe 200 points higher than it would have been 30 years ago. It's hard to quantify because the Elo system is the only objective comparison we have, but over the course of the almost 30 years I've been watching my wife play, the Elo rating enjoyed by a player of a given hypothetical skill level has increased dramatically. But those points are still in the system, having been added to their winning opponents. When they quit the sport, they're never going to reclaim any of the rating points that they lost initially. ![]() But for those that got discouraged and quit, in the course of their loss they caused a few points (not many, because they're likely way overmatched, but definitely more than 0) to be credited to their opponents. For the ones that stay in the game, things probably work out in the long run. Most likely that newbie is going to lose his first matches, and some proportion of those newbies will get frustrated and quit. It seems that much of the problem comes from rating points brought in by newbie players (and note that, contra TFA, the problem isn't with experienced players losing to newbies, but the opposite).Ī newbie is started off with some nominal rating I forget the number, but let's say it's 800. ![]() It doesn't suffer from the weaknesses that you cite, but even so, the problem of "rating inflation" is widely discussed. This sport uses Elo as well, and I know from watching the sport over time that the rating system has real problems. My wife was a champion table tennis player. In short, why so many game devs are enamored with Elo when it comes to ranking is a bit bizarre. Of course, mixing and matching pre-made groups with non pre-made groups creates as many issues as you might imagine. Using Elo (or variants thereof) for team-based games where the team isn't really a team (more like 3-5 random people plopped together for one match) is incredibly misguided, but continues to be implemented in just about every modern multiplayer game (to the players' frustration). TruSkill attempts to fix (3) by using clever Bayesian updating on a player-by-player basis but in reality, it's a shit-show. Obviously, no game is going to be a coin flip, but there's a world of difference between chess and DOTA. Trying to model win-loss-ratios using a sigmoid curve is silly. Suppose your "game" is simply the flip of a coin (everyone wins 50% of the time). Chess has such a high skill ceiling for a number of reasons - it's one of the oldest games still being actively played, for one. Most games also don't have chess' high skill ceiling. If you're using Elo, this will pollute your model. For instance, given a slow combo Magic deck, you will most likely auto-concede to mono red aggro (regardless of skill level). There's also the problem of RPS (rock-paper-scissors) mechanics or pick-counter-pick mechanics which will also heavily skew win rates. These mechanics (while fun and well-designed) might pollute your "idealized" model. Most games aren't chess - where the only variance is picking who's black and who's white - in fact, they might include dozens of RNG mechanics (from critical strikes to ability rolls, to spawn points). ![]() Let's briefly go over these three points. Elo works great for chess, but it would never work for something like Poker. Chess is (1) extremely low-variance, (2) has an extremely high skill ceiling, and (3) is 1-on-1. Elo is great for what it was built for: ranking chess players.
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