Ladder-Balance-Data

- The data is biased towards EU/US and towards higher skill-rate.

Gamescount: 125976
Sc2-Accounts: 45203

-worst to best player: 3200 MMR
-one average win/loose on Ladder: +16 / -16 MMR

TIME Filter: 13 Jun 2012 02:34:54 GMT - 15 Jul 2012 16:05:41 GMT
Games Left: 109028
MMR Filter: Above Master
Games Left: 19688

TIME Filter: 13 Jun 2012 02:34:54 GMT - 15 Jul 2012 16:05:41 GMT
Race account count: 15814
Data average MMR: 1539.46
Difference to average MMR per Race:
T-P: -62.14
T-Z: -117.03
P-Z: -54.89


TIME Filter: 13 Jun 2012 02:34:54 GMT - 15 Jul 2012 16:05:41 GMT
MMR Filter: Above Master
Race account count: 2840
Data average MMR: 2265.5
Difference to average MMR per Race:
T-P: -35.0
T-Z: -21.0
P-Z: 14.0

I thought long about if i should publish this data or not because i know the balance mentality on tl / reddit / sc2-forums.
I fight against balance whine on this forum since i joined it and even created an script to ignore balance whiners on tl.

However i had the data to calculate objective balance data and the balance threads will pop up so or so.

Some month ago, i created an program to calculate the Hidden match making rating.

To find out more about mmr and functions behind it, i included an upload function, that uploads gamedata on our server.
"Not that" and me used this data to find out more about the MMR. We were able to solve the most secrets and calculate it very accurate.
One day decided to uploaded the race value too, without thinking much about it.
Than i realised that with race value and MMR and a lot of data, i can calculate the average MMR for an race.
This post is about such an calculation.

-I took all users and opponents and calculated their MMR.
-After that, i created a list of bnet accounts with their last MMR and race.
-Over this list i took the average of each race!


This steps are very easy to understand but everything else than easy to calculate.
It took us over 3 month and hundreds work hours to calculate accurate MMR.

1) Why average race mmr = data balance

We saw many ways to calculate balance in the past. Some can be indicators some are total useless.
Win/loose statistic of pro players was often used because they are easy accessible and can indicate balance problems.
However, this data dont take into account how strong the players are depending to each other.
The Skillfunction behind MMR is invented to do exactly this.

Arguments like "Race x players are stronger than race y players" are invalid because if this is the case we can call this already imbalanced.

2) Why mistakes in the MMR calculation don't affect the result or affect it

First: the accuracy of my mmr calculation is very good. But i can be wrong in some points or for some users.
However nothing in the calculation takes the race into account.

Theoretical it can be that my mmr calculation is race biased without even knowing the race.
However at the moment i dont see any indicator for that. But i will watch it closely

3) Why race populations dont change the result.
Because i take the average. This point is obvious but i better point it out.

4) Statistic independence
if you take the average of the race you must make sure that you don't have and depending factors in the data.
So what can be such a factor:

1) race/skill distribution of the user-group of my program is not representing battlenet user group

There is no reason why 1. should be true.
Also the data includes to 96% the opponents of my users and only to 4% of the users himself.
So the data have a random user base.
so we can exclude point 1.

2) Skill-range of my users is not skill-range of the battlenet

The users of my program have a way higher average skill than the bnet usergroup.
Also the opponent is allways in the range of the user.
So point 2 is true!
We have to remember that our result is not valid for the hole ladder, its only valid for our skillgroup.
Diamand, Master and Grandmaster are overrepresented in my usergroup.
This means this data show the balance on higher skillevels!

5) prove of small deviation and significance

lolcanoe make a nice analyses of the data. thanks for that!




6) Because some people have a problem understanding this:

-I calculate the unbalance of skill not the reasons for this unbalance!
-I calculate the average skill of an race not the general popularity of an race

7) Data:
Datafile

Please keep in mind that the imbalance result is very small and there will be never perfect balance.
You only improve, in the long term, by ignoring the balance.
Your race can be underpowered today and overpowered tomorrow.

Also, the users make most of the balance, not the game designer.
But this is a different topic....

This is not an university paper about sc2 balance
I dont get money for this.
I dont personal care which race is op or not

I publish the data i collected with my own program that i wrote to back calculate mmr.
I found a very interesting anomalies in the race data.
So i published the result here.

If you want to do a more complex test or analyse with the data.
Feel free to do so!
datafile

If you read the text careful, i think will agree that this is not perfect but a way better method
than tldp win-ratios or random tournament results.