OPR is a least squares solution to an over constrained matrix.
If you've ever done statistics at school, you can think of it sort of like a linear regression, but with more than two variables. If you've got 3 points that form a triangle on a scatter plot, you can't make a single line go through them all. So, you do a "best fit line" knowing there will be some error in your regression.
When there is a strong correlation between OPR and actual contribution like in this example:
http://www.mrholloman.net/SCP/Notes/...9/image006.png
OPR is very well suited to assess a team's point contribution in a match. We are most likely to see a strong correlation between OPR and actual point contribution in years when scoring is linear and non-excludable. For example, in 2013 if you scored a Frisbee in the high goal it was 3 points...no matter what. 2 Frisbees? 6 points. 10 Frisbees? 30 points. Additionally, one team scoring Frisbees usually did not prevent their partner from scoring Frisbees (except for some cases with FCS draining all discs from the Human Player Stations).
However, sometimes it is a weaker correlation, more like this:
http://surveyanalysis.org/images/thu...orrelation.png
This is usually observed when there is some non-linearity in scoring or excludability between partners. In this years game, defenses are non-linear (only count the first 2 times they are crossed) and excludable among partners (i.e. one team crossing the low bar twice excludes their partner from scoring points for doing so). Excludability, diminishing marginal returns, and plateaus for scoring are generally bad news for using OPR to predict scoring contribution. It gets more muddled when things like the incentives from the ranking system, the random pairing of alliances, etc. come into play. We have a lot of that this year.
In 2015, OPR was more useful because the limit of 3-7 Recycling Containers (depending on canburglarring) was less commonly hit than a breach is this year, especially in qualifying matches. Additionally, your sole ranking incentive was scoring as many points as possible. Thus there weren't really reasons to deviate from scoring as many points as you could all the time.
Bottom line is understand what OPR generally is before you use it. It
IS a useful tool for somewhat understanding a team's relative contribution at an event (within some margin of error). It
IS NOT a reasonable justification for picking a team with an OPR of 30 instead of another team with an OPR of 29. If you're comparing a team with an OPR of 40 to one with an OPR of 5 and there's a reasonable sample size? Sure, there's probably a good reason for the discrepancy.