Quote:
Originally Posted by JesseK
I found that it doesn't matter if you solve the matrix based upon win margin or if you just subtract DPR from OPR, the number is still the same. The CCWM is based upon the white paper that I saw Ed Law put out at the end of last year, which is what I originially tried to implement in the matrices. When I saw that the numbers were the same, I simplified all of the code and didn't rename the stuff -- oversight on my part.
CCWM = Caclulated Contribution to Winning Margin
aka PlusMinus Rating, i.e. different calculations to arrive at the same solution.
I think you want a high OPR for a first round pick, then a high CCWM for the second round... that is, if you want a high-octane, excitingly offensive alliance (which is my preference).
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I just had an idea, which might resolve to CCWM/PMR, but I don't think it will.
It ends up with a 2N x 2N matrix (for a regional with N teams)
sum[team's scores] = weighted sum of OPRs of alliance members minus weighted sum of DPRs of opponents.
At first I thought that it would be unsolvable (each equation has 6 unknowns, and you'd end up with 2N unknowns and only N equations), until today I had the fairly-obvious brainwave that each match would give you two equations (one OPR
red - DPR
blue = score
red for red, one OPR
blue - DPR
red = score
blue for blue). This approach seems like it would be more predictive of robot performance than simply doing OPR and DPR separately*. I'm going to try to implement it tonight, but if someone wants to try it themselves and report back, that'd be great.
*The problem with the current approach to OPR and DPR, especially in a game like lunacy, is that they assume that a alliance's score comes ENTIRELY from its teams' offensive powers, or ENTIRELY from its opponent's lack of mobility. This proposed new equation seems like it would balance the two, and hopefully give more accurate results.