Bill Spight wrote:Shinkenjoe wrote:You also need to play thousand games to lose one in a thousand.
Actually, if you play 69 games at those odds, the probability is 0.500 that you will lose one.
Do you mean 690? (Actually, 692 or 693):
(999/1000)^692 ~= 0.50040
(999/1000)^693 ~= 0.49990
Claint wrote:My rating is 5-6kyu ish KGS. Let's say, I am the guy and I am 6kyu.
According to ELO formula the expected win percentage is magnified 10 times with each rating difference of 400. So for 0.001 chances, you need a rating difference of 1200.
So with EGF ranks: 6kyu = 1500 ELO rating.
That means we need a guy with 2700 rating. Which again with EGF ratings is equal to 7 dan amateur or 1d professional.
Note: This is a quick calculation and is probably wrong, since according to reference, EGF modifies the normal ELO formula somewhat. But 0.001 is a mighty difference.
An interesting source of statistics about winning chances between mismatched players in even games can be found here:
http://gemma.ujf.cas.cz/~cieply/GO/statev.htmlThe most basic versions of the Elo model assume that if we have players A, B, C and the odds of player A beating B are 1:x (that is, a probability of 1/(x+1)), and the odds of player B beating C are 1:y, then the odds of player A beating C are 1:xy.
If we assume that this is true, then roughly eyeballing the stats on the G+4 column and multiplying up the odds for a 6k beating a 2k, and a 2k beating a 3d, and a 3d beating someone around 6d or 7d, it does actually look like you need to go up to about 6d or 7d before you reach an odds ratio of 1:999. Indeed, 0.001 is a mighty difference.
However, it's common wisdom that the chance of a weaker player beating someone much stronger falls off much faster than this sort of model would suggest. The stats on that site seem to bear this out. In almost every case, if you multiply up the appropriate odds ratios for the stats in the G+1 column and/or G+2 columns to get a prediction for the G+4 column, you obtain an odds ratio that's smaller than the actual one. For example, multiplying up the ratios for 6k beating 5k, 5k beating 4k, 4k beating 3k, 3k beating 2k, you get a odds ratio of 1:2.69, but the empirical ratio for 6k beating 2k is 1:3.67.
Whereas this effect is clear in the G+4 column, if you do the same thing for the G+3 column, you only barely see this effect. If you do it for the G+2 column, you basically don't see it at all and the simplistic model matches up pretty well relative to the noise in the stats.
Based on this trend and some intuition, it seems likely that the effect will continue to increase as the strength difference between the players increases, although there's no data to pin down how fast it will increase. But playing with these stats in a spreadsheet with various extrapolations for how fast, it looks like anywhere from about 2d to 5d would be a plausible guess for how strong you need to be to have only a 0.1% chance of losing against a 6k.