Hey players,
this is more of a chat and sharing session than anything else, so please treat it as such.
To recap some of the things I have shared in the past.
We actively calculate each players rank based on the result of every match, and we lovingly call that rank "Mu". Before the first battle, the player is given a rank of 25. That rank is updated after the battle, based on the weighted average of the persons duration in the battle and whether he won or lost. Simplified (the calculation is considerably more complex)
NewMu=OldMu+"Player seconds in Battle/Battle Duration" * Win/Loss
So if you lose, your Mu goes down, and if you win, your Mu goes up.
This is a modified version of elo ranking, used in many competitive sports such as chess, major league team sports and esports. Eventually your Mu will converge and stabilize around your "true skill", which is where you will win and lose equally against either players with the same Mu. However, since this is a team game, convergence will happen slower and you may find that even having the best game of your life, will not influence the match enough to secure a win. I will, however, demonstrate that it works very well to predict player skill.
The underlying problem is that after one match, everyone is very close to 25, and 2 battles, even 10 battles in, only the very best (and worst) have begun to be different from the pack.
That's why it's imperative to find a proxy for Mu for the first battles, which is what comes next in our findings.
Now to the data. I have been working with a big sample of player data, testing multiple theories, some from the forums (WP/Death), the ever classic K/D of course and a time based WP/second and the results are very promising.
DATABy creating buckets of Mu, I can calculate the three ratios of the players within each those Mu buckets and create a correlation table against Mu. That Correlation is then shown graphically on the top Chart.
Seeing that two of them are obviously logarithmic in nature, I normalize with the log function and get the bottom chart, Normalized Correlation.
Calculating and also just analyzing the graphs, we find that the correlation between WP/s and Mu is a towering 99%, and a bit lower for the other two, but still statistically very relevant. It basically means that all of them could be used in place of Mu in the beginning while Mu converges, and even in place of Mu overall.
Now, our next step is to implement a better use of this data. One simple way would be to say, instead of exiting the Academy at an earned WP basis, it's not until you actually reach a minimum threshold of WP/s. It is also imperative to utilize this information more during the teambuilding part of the matchmaker. In any sport, if the two best players are on the opposite side, everyone, even the bad players, can have fun and be inspired by the good players. If both of them are on the same side, nobody has fun.
I hope you enjoyed this little insight piece
P.S.
Those with eagle eyes will notice a weird anomaly in the two lowest Mu brackets for the both WP/Death and K/D ratios, but not for WP/s. My theory, is, and not based on prejudice at all, is redline snipers. My reasoning is that they are able to avoid death rather easily, they will be able to pick off stragglers and low hitpoint suits on a regular basis but sadly, have little to no relevance to the battle result, as they do not hack nor defend objectives effectively. Why their WP/s does not show that, I theorize, is because they spend quite some time getting to a mountain top, and or with a dropship to a tower, and if they die, they are forced to do so again. Feel free to burn me at the stake, and/or voice your alternative theories.
