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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 OPRred - DPRblue = scorered for red, one OPRblue - DPRred = scoreblue 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
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You would want to have (blueScore = blueOPR + redDPR) and vice versa. Remember, you having a low DPR is better for you, hence you want your opponents to have a high DPR. If you subtract the opponent's DPR then you're lowering your own score just because the opponents are easier to score on ... which doesn't make sense when the opponent's DPR is positive (which is pretty bad).
Some simulation data to come tonight; I have an idea for a quick and dirty Match Prediction method.
<edit> Quick match prediction using the formula above for the DC Quarterfinals:
Code:
blueScore = blueOPR - redDPR:
Teams: (RED) 234, 45, & 620 vs. (BLUE) 1111, 122, 768
Predicted Scores: Red = 43, Blue = 6
Actual Scores: Red = 126, Blue = 100
blueScore = blueOPR + redDPR:
Teams: (RED) 234, 45, & 620 vs. (BLUE) 1111, 122, 768
Predicted Scores: RED=101 BLUE=80
Actual Scores: Red = 126, Blue = 100
Note that the prediction for their second match was identical even when the new match data was added, so predicting the outcome of the second QF match when the scores are that close is nearly impossible. Predictions also don't take into account adjustments for strategy, which is probably why it's best to leave the actual statistics for Qualification Matches only.