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Statistical Analysis of StarCraft 2 Balance - Page 6

Forum Index > SC2 General
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Nagisama
Profile Blog Joined April 2010
Canada4481 Posts
Last Edited: 2011-05-07 15:22:41
May 07 2011 15:22 GMT
#101
Sorry this is more helping in editing the grammar/spelling in your paper than the actual content.

In your actual paper, you spelled Terran properly, however in the appendix you used Teran, was that just to save space? or just simple misspelling?

Also, regarding section 5.2, I think the currency is described as Korean Won, instead of Korea Won. Just simple fixes for future versions =)

Regarding content, I was a bit confused at how you calculated B, but you answered it earlier. Not having taken a statistics course in a year has made me forget logistic regressions.
Calendar"Everyone who has accomplished more than you has no life; Everyone who has accomplished less than you is a noob." | Elem: "nagi is actually really smart"
GeorgeForeman
Profile Joined April 2005
United States1746 Posts
Last Edited: 2011-05-07 15:36:21
May 07 2011 15:35 GMT
#102
Nice analysis. I went in anticipating some intro-level stat student doing a regression and calling it science, but you did a good job here!

There are a couple of assumptions about the player pool being representative, etc. that are implicit, and the way tournaments are structured introduces some bias in which players you see more games of, but I think the approach is sound. A better data set for this type of analysis (IMO) might be the NASL. Since there aren't eliminations, you get to see all of the players the same number of times.

Great work!

PS- I used Agresti for my Categorical class, too!
like a school bus through a bunch of kids
mike1290
Profile Joined January 2011
United States88 Posts
May 07 2011 15:36 GMT
#103
I have looked through many balance threads since sc2 came out and people started whining about X or Y. Many of these threads, well some at least, incorporated some kind of statistical analysis like this one to back up various claims about balance.

I do not think this is the correct way to evaluate balance. Statistics are nice, but they do not accurately reflect a game's true balance. Correct me if I'm wrong, but I do not believe anyone has learned how to play sc2 perfectly at this point in time. So I don't see the value of analyzing statistics for players that are not playing optimally. Currently, X race may have a much lower win % than the other races, but this in no way reflects the game balance. This is merely a reflection of where the players are right now.

My approach to balancing the game is to stop patching it and let it develop. I have no idea how long this will take, but I'm sure it's longer than the few months Blizzard puts between patches.

I watched state of the game last week and I found Idra's points very interesting. I agree completely with him that if there is no way for zerg to scout and if zerg has no build that makes it safe against any possible build then the game is imbalanced. However, I do not necessarily agree that this is the case yet in sc2 for zerg. The game is very new and for Idra to claim this definitely seems a little premature. Idra is a top top zerg player and I am obviously not so I also have to take into consideration that there are very few people with Idra's insight into the game as a zerg player.
HateRock
Sueco
Profile Joined September 2009
Sweden283 Posts
Last Edited: 2011-05-07 15:38:27
May 07 2011 15:38 GMT
#104
"There's lies, damn lies and statistics."

- Mark Twain
d_ijk_stra
Profile Joined March 2011
United States36 Posts
May 07 2011 16:02 GMT
#105
On May 07 2011 15:31 Elean wrote:
Show nested quote +
On May 07 2011 05:54 d_ijk_stra wrote:
On May 07 2011 03:02 Elean wrote:
On May 07 2011 02:37 d_ijk_stra wrote:
On May 06 2011 22:25 Elean wrote:
It looks like this model assumes that protoss players are extremely skilled (6 Protoss in the top 10 skilled player), and get to the conclusion that Protoss is underpowered.
Basicaly, it has exactly the same value as Idra saying "I'm the best player, I don't win, thus there is an imbalance".

(actually, this model can converge to different solutions, the particular solution the author got was "protoss players are skilled and protoss are underpowered...", it could very well have converged to "protoss players have no skill and protoss are overpowered")

All the people reading this should understand that this is not a scientific peer reviewed paper.

There is no way, this would be accepted as it is now.
If I were to review this paper I would ask for several modifications, and I would actually reject the paper unless the author answer this question:
How can you tell there is no offset on the "skill parameter" of all the players of 1 race ?

I would also ask a plot of the "skill pararemeter" distribution for each race.


First of all, I think you read it very carefully. Thank you very much for your interest.
I'll talk in technical sense, since it seems like you have good background in statistics.

The problem you're worried of can happen in "unidentifiable" cases,
that is, there are multiple parameters that can represent the same model.
This is not the case for this problem, since I used either

1) Use LASSO as a L_1 regularier,
2) Use non-informative gamers as baseline

Therefore, things like what you described cannot happen.
The existence of regularizer tries to not have the presence of extraordinary gamers
as much as it can, unless he wins too many games.

It is very important to check identifiability of the model before conducting an analysis,
and it is good for you to check out this issue. I understand for you to miss this point
since 1) I agree that the document is poorly written. It should be rejected in every journal/conference 2) you should've not read it as a professional reviewer

And it is also good to point out that THIS IS NOT A SCIENTIFIC PEER-REVIEWED PAPER.
I DID IT FOR FUN, and the fact that I am a Statistics major does not guarantee that the
analysis is correct. I didn't worry much at this point at the time posting it, but people without
proper background could've misled. Thanks.

Your model is:

logit(P)=beta_player1-beta_player2+beta_matchup

You use the LASSO method to fit the value of beta_playerx, and beta_matchupx

You get ONE fit, but there are other degenerated solutions, here is the proof:
Take the values of your solution, then decrease by 10000 all the beta_player of protoss players, then increase by 10000 the beta_PvZ and the beta_PvT.
If you do that, you get another fit that is just as good as the one you first had (i.e. all the logit(P) are unchanged). However, now the beta_PvZ and the beta_PvT are extremely high, and protoss become clearly overpowered.


Your model is probably good to estimate how likely a player is to win a match, but it is 100% blind to balance.

The problem is that all the players only play 1 race, and you will never be able to make the difference between "all the protoss players are way better than the others, but protoss is underpowered" and "all the protoss players are noobs, but it's ok since protoss is way overpowered".
There is absolutely nothing you can do about it. Not with this sample of data.


By LASSO, you mean the existence of (L_1) regularizer.
When you add 10,000 to your parameter, you are being penalized a lot.
I suspect you understand the concept of regularization, sorry.

As far as I can tell, LASSO is a least square method that set up a constraint on the L1 norm. Constraint that has no justification in this case.

You have to understand that if 2 models give the exact same results for any match, there is no way to tell which one is better. I explained to you, that there is an infinite number of models that will give the same resuts with different "balance between 2 races". This means you can not tell if there is an unbalance.

I will explain to you on an example, why the L1 constraint does not have any justification.

For simplicity sake, let's consider only 2 races, T and Z, and let's consider that all the players of 1 race have the same skill.
Suppose TvZ is unbalance, and the actuall value of beta_TvZ is 500.
Since all the players made it in the tournaments, they are likely to have roughly the same strengh (skill + balance). This means the Z players have likely a beta_player that is 500 above the beta_player of the T players.

Now, run your mode, with a sample size extremely large. You get the solution beta_TvZ=0, beta_playerZ=0 and beta_playerT=0. This solution clearly minimize the L1 norm, and is also give the exact result for the probability of each match. However, it is completly wrong and does not manage to see the unbalance.

Your method fails to catch any unbalance for the exact same reason everyone can't tell the balance: we don't know if the zerg players are more or less skilled than the terran players.


Due to penalty on beta_players, it cannot naturally happen that all zergs users having 500 more parameter values than terran users. You know, using L2 norm is equivalent to using normal priors centered at origin? By specifying regularizer, you are implicitly assuming that those parameters should be centered around zero.

Of course then it can be attacked from another angle: can you say that the average skill level of all races are (at least approximately) the same? This is a valid question. If good gamers are all choosing Terran and bad ones are choosing Zerg, it's hard to analyze the balance.
My Life for IU!
d_ijk_stra
Profile Joined March 2011
United States36 Posts
May 07 2011 16:08 GMT
#106
On May 08 2011 00:38 Sueco wrote:
"There's lies, damn lies and statistics."

- Mark Twain


It's funny that you are not the first person quoting this in this thread. :D

Yes, I do not think this analysis is 100% percent. But we do Statistics because we want to give some answer to questions people are interested in. Not just saying that "Who knows? This can be right, and that can be right!" Actually, if you are a scientist/engineer, this is what we do for EVERY scientific problem. No one did prove that Newton's law is correct, or Schroedinger's equation is correct. It is just an attempt to "explain" the phenomena.

But Statistics can be a serious lie, if one claims that his analysis is perfectly correct. Mark Twain should have said that, because too many people just copy last line of conclusion part of the paper, and then say that "This is the damn truth!"

But I am really happy to see these replies in teamliquid: not only they are interested in the problem itself, they question assumptions, they question methods, so that the no damn liar (or, statistician) can deceive them. I am quite impressed!
My Life for IU!
awesomoecalypse
Profile Joined August 2010
United States2235 Posts
May 07 2011 16:14 GMT
#107
I watched state of the game last week and I found Idra's points very interesting. I agree completely with him that if there is no way for zerg to scout and if zerg has no build that makes it safe against any possible build then the game is imbalanced.


Zerg, or any race, being 100% safe against any possible build is not a prerequisite for balance. It is just something IdrA wants because he thinks the game should conform to some platonic ideal which rewards "skill" (by which he means mechanics) above all else, and he hates the idea of losing to anyone who isn't "better" than him (by which he means more mechanically capable). The so-called "safe" builds lose to cheese all the time even in BW (witness Jaedong's legendary success via the 6 pool), unless you're name is Flash and you have jedi gamesense. But IdrA himself was famous for getting cheesed out of tournaments, then bitching about imbalance, Protoss in general, and his opponents lacking "skill". For all that people have this idea that BW evolved into these perfectly safe builds that made you immune to cheese and guaranteed you'd go into the mid and late game on decent footing...that really wasn't the case. All "safe" means is that if you play perfectly, scout like a motherfucker and have ludicrously good gamesense, you could theoretically react in time to anything. But if you had any lapses whatsover--which, you know, basically every pro does, which is why BW pros are far from immune to cheese--you'll still lose to cheese.

Even if Overlords were faster, IdrA would still lose to nonstandard play a lot, and he'd still bitch about it whenever he did, the same way he bitched every time he lost to cheese in BW (which was also a lot). At the end of the day, IdrA's problem isn't with the game, its himself--he's not as good as he thinks he is, and for some reason he has this platonic ideal of how the game "should" be played that has very little to do with how it actually is. That was true in BW and it will always be true in SC2, regardless of what patches may come.
He drone drone drone. Me win. - ogsMC
mike1290
Profile Joined January 2011
United States88 Posts
May 07 2011 16:47 GMT
#108
On May 08 2011 01:14 awesomoecalypse wrote:
Show nested quote +
I watched state of the game last week and I found Idra's points very interesting. I agree completely with him that if there is no way for zerg to scout and if zerg has no build that makes it safe against any possible build then the game is imbalanced.


Zerg, or any race, being 100% safe against any possible build is not a prerequisite for balance. It is just something IdrA wants because he thinks the game should conform to some platonic ideal which rewards "skill" (by which he means mechanics) above all else, and he hates the idea of losing to anyone who isn't "better" than him (by which he means more mechanically capable). The so-called "safe" builds lose to cheese all the time even in BW (witness Jaedong's legendary success via the 6 pool), unless you're name is Flash and you have jedi gamesense. But IdrA himself was famous for getting cheesed out of tournaments, then bitching about imbalance, Protoss in general, and his opponents lacking "skill". For all that people have this idea that BW evolved into these perfectly safe builds that made you immune to cheese and guaranteed you'd go into the mid and late game on decent footing...that really wasn't the case. All "safe" means is that if you play perfectly, scout like a motherfucker and have ludicrously good gamesense, you could theoretically react in time to anything. But if you had any lapses whatsover--which, you know, basically every pro does, which is why BW pros are far from immune to cheese--you'll still lose to cheese.

Even if Overlords were faster, IdrA would still lose to nonstandard play a lot, and he'd still bitch about it whenever he did, the same way he bitched every time he lost to cheese in BW (which was also a lot). At the end of the day, IdrA's problem isn't with the game, its himself--he's not as good as he thinks he is, and for some reason he has this platonic ideal of how the game "should" be played that has very little to do with how it actually is. That was true in BW and it will always be true in SC2, regardless of what patches may come.


I don't think that I said anything about BW being balanced, but correct me if I'm wrong. I am simply saying that to have the game be as fit for competition as possible, the "luck" portion of the game should be as small as possible.

As far as the "safe" build goes, I agree with you that this should not be an option for zerg. I think the problem and the solution is with scouting. Having a build that is 100% safe would be bad for the game and I think Idra was not really suggesting this safe build. He was really trying to argue for better scouting options and presented this safe build as really the only other option is the couting situation stays the same for zerg. Again, I want to state that I'm not sure if what Idra is saying is the case with zerg in sc2 yet, but if it ever gets there, something will need to be done to fix the game.
HateRock
StarDrive
Profile Joined September 2010
90 Posts
May 07 2011 16:59 GMT
#109
It looks like your model does not take into account balance changes via Blizzard's patches over time, since you use 852 games from the entire GSL. Is there a way you could augment your model to handle this?
Elean
Profile Joined October 2010
689 Posts
Last Edited: 2011-05-07 17:16:22
May 07 2011 17:09 GMT
#110
If good gamers are all choosing Terran and bad ones are choosing Zerg, it's hard to analyze the balance.


This is exactly the problem here.
And you have to keep in mind the nature of your DATA. It's from players who managed to qualify for GSL.
It's not just a problem of good players choosing a race rather than another.

The problem is that if Terran is underpowered (I don't like realistic examples...) and zerg is overpowered, then the Terran players who manages to qualify for the GSL are most likely more skilled on average than the Zerg players in GSL.

So if there is an unbalance in the game, you will have differences among the average player skill of each race in your DATA. And it does not make simply "hard" to analyze the balance, it makes it impossible with GSL as DATA.


Now, the fact that you got 6P, 3Z and 1T in the top 10 skilled players suggest that you are overestimating the average skill of the protoss players and underestimating the average skill of the terran players. And this would naturally leads you to the conclusion that P is underpowered and T is overpowered.
RaLakedaimon
Profile Joined August 2010
United States1564 Posts
May 07 2011 17:16 GMT
#111
Wow nice job on this man, love getting solid info like this.
mavyric
Profile Joined November 2010
Taiwan104 Posts
Last Edited: 2011-05-07 17:16:48
May 07 2011 17:16 GMT
#112
A number a people have brought up good points.

My 2 cents on using this statistics model:

You cannot just add up the seasons of the GSL, because there were balance changes(patches) and map changes. Therefore, you cannot infer from the data alone that a certain matchup was favored over another.

Vive Hodie
Cifer
Profile Joined April 2011
United Kingdom69 Posts
May 07 2011 17:33 GMT
#113
There is also factors like the "meta game" and the opposing styles clashing. For example player A wins 70% of his PvZ but he will most likely lose to a player with drop play regardless of his opponents ability. The "ability" of a player is such a dynamic variable, that I don't think it reasonable to use in such fashion.
W2
Profile Blog Joined January 2011
United States1177 Posts
May 07 2011 17:45 GMT
#114
Seems about right. Good work! More people should read this!
Hi
d_ijk_stra
Profile Joined March 2011
United States36 Posts
May 08 2011 01:25 GMT
#115
On May 08 2011 02:16 mavyric wrote:
A number a people have brought up good points.

My 2 cents on using this statistics model:

You cannot just add up the seasons of the GSL, because there were balance changes(patches) and map changes. Therefore, you cannot infer from the data alone that a certain matchup was favored over another.



Totally agree. I hope the patch is being stabilized at some point of time and then I have enough data to analyze a single patch. I promise to do some follow-up analysis with more reasonable model in the near future At this point of time this is just for fun, please don't take it too seriously :D You know, nobody will tell SC2 is IMBA or not based on this!
My Life for IU!
Breaker 1st Class
Profile Joined May 2010
Australia47 Posts
May 15 2011 08:22 GMT
#116
Firstly, this is probably the most academic/scholarly thread I have read in a while on TL.net. Kudos to the OP; his/her receptivity to feedback and criticism warms my heart . I hope you can improve your model soon because I look forward to reading a revised version in the near future

P.S. Your article and the ensuing debate and discussion in this thread have rekindled the fires of my inner scientific self.
Paradice
Profile Joined October 2010
New Zealand431 Posts
Last Edited: 2011-05-17 08:37:22
May 17 2011 08:30 GMT
#117
On May 07 2011 01:49 Mactator wrote:
The imbalance issue is not necessarily related to probability of a player winning. The usual notion of "imbalance" refers to specific issues rather than xvy being imbalanced.

If we consider the notorious example of the protoss death ball then many people complains that zerg players can't win against it. Let's assume that is correct. Then the obvious thing to do if you are a zerg player is to avoid getting into late game against a protoss. This may be a very effective strategy and you may even measure a high probability for zerg players to win. The game would still be imbalanced though!


What? No! I realise this is 1 page ago but I have to call this one out. If there is an effective strategy that lets Zerg win despite the existence of the "unbeatable" Protoss deathball, then that means the opposite of your conclusion.

Consider the game of Connect 4. If player A gets into a position where he has three counters in a line and open spaces on both sides of the line, his position is unbeatable by player B. The existence of that situation does not mean that Connect 4 is imbalanced - the existence of that situation actually means that the game is winnable which is rather a desired property for a competitive game. It would be imbalanced only if player B had (1) no viable means to stop player A from getting into that position, and (2) had no viable means themselves to win the game before that position was reached by player A.

If you want to prove an imbalance you have to argue for both (1) and (2); if you listen to IdrA on SotG with Day9, this is the type of argument he's making. You can also use statistics to try and prove or disprove (1) and (2) - e.g. if you show that Zerg players are winning, it's a fairly logical conclusion that they must have a viable means to win.


And now in regards to the OP - a fantastic effort look forward to any future developments put forward. I would also love it if you expanded the sample size and listed the identified beta values for everyone's favourite pro players - but I'd mainly love it because of the shitstorm it would create
paperwing
Profile Joined February 2011
49 Posts
Last Edited: 2011-05-20 08:06:13
May 20 2011 08:05 GMT
#118
The paper was referenced in the following thread:

http://www.teamliquid.net/forum/viewmessage.php?topic_id=224881

You may be interested in his commentary
Thrombozyt
Profile Blog Joined June 2010
Germany1269 Posts
May 20 2011 08:32 GMT
#119
I would be interesting to see your analysis applied to the NASL stats after the end of group play. With their results being splitted by maps, too, it should be easier to separate map bias from racial matchup bias.
Primadog
Profile Blog Joined April 2010
United States4411 Posts
May 20 2011 09:34 GMT
#120
Featured on Weapon of Choice, congrats!
Thank God and gunrun.
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