There are some overpopulated areas (most notably the low battle, high winrate corner where sealclubbers resided), but generally the grid is neutral. Battle count and frag count does not create large, correlating areas, they are pretty much unrelated. But to make sure, here is a linear fit between them, with absolutely horrible correlation:
Now, with two independent variables, we can look for the result, how they contribute to rank, calculating the average rank of the groups of players defined by the above grid:
One rarely sees so perfect correlation. You need both enough battles and high frag rate to reach good rank. No matter how good you are in one field if you lack in the other. So the definitive answer is that casuals likely won’t rank out, but no-lifers with bad skills neither.
Finally, a personal note. My results were 42 battles, 0.93 frags. This places me to the last column, 4th row. The average rank of that group is 9.8, so my rank (10) is exactly where it supposed to be. Since my frag rate is already top 10%, all I have to do is play more battles next season.
Update: the standard deviations of the groups:
It’s between 1 and 3 everywhere, meaning that approximately 2/3 of the players are 1-3 rank away from the value defined by their frag and battle count. Since the matching brackets are 5 rank wide, this SD is pretty good. The high battle count rows are on the wider SD zone, so the population is more diverse there, but still within a bracket range.
Average values are nice but a table with standard deviations of obtained ranks would allow to see the accuracy of predicted rank.
“The average rank of that group is 9.8, so my rank is exactly where it supposed to be. ” I presume you got the 10th rank but explicit statement might be needed.
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@retsep: update added.
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@Gevlon: thanks for additions. As expected, standard deviations were low.
Is expression “approximately 2/3 of the players are 1-3 rank away from the value defined by their frag and battle count.” based on https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule ? Maybe more appropriate is to use https://en.wikipedia.org/wiki/Chebyshev%27s_inequality , since rank can’t be less than one.
High amount of battles seemingly predicts approximately 0.6 frags.
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@retsep: the amount of elements out of 1 sigma, just for you: https://greedygoblinblog.files.wordpress.com/2018/07/sigma.png
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@Gevlon: thank you for calculations.
About 15% of groups have the amount of elements out of 1 sigma greater than 0.39 and about 10% of groups have the amount of elements out of 1 sigma less than 0.20, so it balances out.
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I don’t think this correlation says anything.
If frag count is a good proxy for performance then it predicts win rate and rate of saving stars.
The number of ranks gained is a direct product of the number of battles and win/star saving rates.
Add a bit of variance for starting position and you get exactly this.
It’s nor correlation, it’s equation. Equivalent to stating “better win rate produces more stars”. Duh.
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@Stawek: obvious for me and you, but not to those who claim that you should sacrifice for your team and push like crazy. The thing is that nowadays saying the obvious is important.
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@Gevlon
Are there any RNG elements that would tend to push or skew the standard deviations on an individual, or team basis?
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