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Balancing sc2

Blogs > darmousseh
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darmousseh
Profile Blog Joined May 2010
United States3437 Posts
October 08 2010 22:29 GMT
#1
You are the creator of a game called black and white. All players have a choice of playing either black or white during the game and its a 1 on 1 game. You are the creator of the game and you have designed it such that black has a tiny disadvantage against white. During a testing period of matchups where players had to switch between black and white between each game over 1000 games you noticed that white wins 55% of the time. This concerns you a little, but you frankly don't care.


Now you release the game to the general public. Each player picks the color they want to be. You notice that more people are playing black 0%, but that the top 200 is 65% black and 35% white. This gets you thinking that black is actually overpowered, except that you don't know that on average, player that play black are about 70 points more skilled than players that play white. Since you don't know this information you try to extrapolate it, except, that you invented a matchmaking algorithm where each player plays games in such a way that their win percentage is always around 50% and at the latest tournament a white player won.



So what information can we gather? The answer? Nothing, it is impossible to know that white is actually overpowered. The information mathematically that you need to determine this is 1. That players who play black are on average 70 points more skilled 2. Two equally skilled players in a black/white game will favor white 55% of the time.

Is there a way to determine 2? Yes, you can test matchups of evenly skilled players who are forced to learn to play as white and as black.

Is there a way to determine 1? No, The rating system being used is a combination of player skill and environment. If the environment is unfavorable this will be reflected as rating. Can we approximate 1? Possibly, if you make a crazy assumption such as that the skill of a player is 95% skill and 5% color. Is there a systematic way of determining this ratio? Yes, if a group of players learned how to play both black and white equally, then you can exprapolate this information.


Why did i post this? Because 1 and 2 are impossible to gather in sc2. It is possible that zerg is actually OP, maybe protoss is OP, unless we have a lot of recent information from similarly skilled players (who have enough games as each race) otherwise we cannot determine either 1 or 2.

When blizzard posts information about win/loss ratio of different matchups what does it mean? It doesn't mean anything. Its possible that player who play terran are simply more skilled on average than zerg, it's possible that zerg adds 50 points to a players skill, but the skill of the players playing it on average lowers it. Maybe cool is an example where he is the same skill as a terran, but that playing zerg gives him an advantage.

The answer is without equal parameters or conditions, all statistics are meaningless.


If you come up with a way for blizzard to get the information about balance without all of the neccesary inforamtion that i just described, please post it and then write a thesis and publish it please.

Also please use probability theory to explain your ideas or at least talk about the minimum information needed.

Developer for http://mtgfiddle.com
Tabbris
Profile Blog Joined June 2010
Bangladesh2839 Posts
Last Edited: 2010-10-08 22:40:47
October 08 2010 22:40 GMT
#2
I think this post is good enough to not be post in blogs . I agree. Blizzard has a very very hard job
ideadude
Profile Joined April 2010
United States7 Posts
Last Edited: 2010-10-08 23:07:42
October 08 2010 23:06 GMT
#3
Great analysis. Thanks.

Couldn't the game company recreate (in a sense) the initial experiment where people were (a) forced to play both sides against (b) random opponents? (When the initial 55% win ratio was discovered.)

If you make 1-in-50 games played match people randomly instead of using the match-making algorithm, you would get (b) above.

If you chose a random subset of these games across all races, wouldn't that be the same as (a)?

You would be able to say a random Black paired up against a random White wins x% of the time. This removes all skill from the equation since you didn't match them based on the calculated skill.

The one thing this experiment would miss is the assumption that equally skilled players are as likely to choose Black vs. White. (Maybe unskilled players are drawn towards White since the game released with the White campaign.)

How would you remove this affect? Maybe you grab randomly from people who have played at least 50 games? Maybe you limit things to people who have played White and Black a certain number of times? Not sure.
Patriot.dlk
Profile Blog Joined October 2004
Sweden5462 Posts
Last Edited: 2010-10-08 23:19:03
October 08 2010 23:17 GMT
#4
They need to focus on getting each race equally played, but how do you overcome a big influence of terrans and protoss due to those races being featured in the single player?

No idea. But I would really try to get them equally played and then collect a lot of different data. How many #1 guys of each race in their leagues, top 500, statistically what race wins most on [insert map], compare with big tournament results etc etc.

Going at this trying to get a smart algorithm in the works is just not how you perfom data mining, you need to look at it in EVERY possible perspective you can think of.

What you suggest here is basic probability and it's miles away from being useful
darmousseh
Profile Blog Joined May 2010
United States3437 Posts
October 08 2010 23:54 GMT
#5
I have had bad luck posting stuff in starcraft 2 general. Usually some moderator comes by and says "This is already being discussed here" or "This should be a blog post" so i usually post here.
Developer for http://mtgfiddle.com
Kaptein[konijn]
Profile Joined August 2005
Netherlands110 Posts
October 09 2010 08:44 GMT
#6
I agree, you can't make meaningful balance-related statements based on statistics in the current match making system. People make a lot of invalid implicit or explicit assumptions when they believe they can.
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