## FANDOM

42 Pages

Hello, folks! This is Kaighn Kevlin here to introduce the new Elo rating system. This is a universal rating system for head to head competitive games where only the outcome of the games is recorded. It’s THE standard for chess ratings, and it’s used in many other games like in online games, other board games, and even team sports. If you’d like to read more about it, click here.

Here’s a nice little explanation: Everyone’s rating starts at 1000. The rating system models each player’s chance of winning against any other player. The way it will do this is by using something called the logistic distribution, which is extremely similar to the normal distribution.

Let’s look at a game between player A and player B. Let’s say player A’s rating is Ra, and player B’s rating is Rb. For now, assume Player A is more highly rated.

Now, player A will probably win, that’s obvious because he/she has a higher rating. But what is that chance? According to our Elo model, the chance of player A winning against player B is:

Ea = 1/(1+10^((Rb-Ra)/400). (expected value for player A)

Note that it Ra = Rb, then this is 1/(1+10^0), which is ½, exactly what we want. Two equally rated players have 50% chance of winning.

If Ra>Rb, then 1/(1+10^(negative #)), and 10^(negative #) is less than 1, so 1/(1+something less than 1) would be something in the range (.5,1).

If Ra 10 points. If you beat a lesser rated opponent, you get K*(1-chance of winning) = K(1-something bigger than .5) < 10 points. The exact amount is determined by your exact chance of winning. The above chart is a good rule of thumb. In the table, the chance of beating someone x points below you is Ea, but the chance of that player beating you is 1-Ea. For instance, beating a player 50 points below you has chance 57%, but the chance of someone beating someone that is 50 points higher is 1-.57 = 43%.

Details about K: K is not 32 for everyone. If K was constant, then this would be a zero sum game, where if someone goes up by a certain amount of points, someone else went down. Instead, we want a system where the elite bumper pool players cannot just keep beating people and getting more and more points (even though the amount of points he/she gets each game goes down as he/she gets a higher rating). We want a system where K values for elite players are less than worse players’ K value. So if K=16 for elite players, they will have a harder time getting points. Notice as well that a lower K value makes it harder to lose points too (this is okay because we assume that once a player becomes good, he/she doesn’t regress. Even if he/she did, the system would eventually correct it). To this end, we currently have a 3 – Tiered K values:

K = 32 for player’s with ratings <1010

K = 24 for player’s with ratings >1010 and <1100

K = 16 for player’s with ratings 1100+

Note that, when determining your new rating after a game, only worry about your K value. If you, a 950 player, play Tyler, a 1250 player, and Tyler wins, then:

Your K-value is K = 32 (for rating <1010)

Tyler's K-value is K = 16 (for rating 1100+)

Your expected win chance was : 15%

Your rating goes down by 32*(0-.15) = 32*-.15 = -4.8

His rating goes up by 16*(1-.85) = 16*.15 = +2.4

Alternatively, if you win:

Your expected win chance was : 15%

Your rating goes up by 32*(1-.15) = 20*.85 = +27.2

His rating goes down by 5*(0-.85) = 5*-.85 = -13.6