Interesting numbers! I think graphs always help data make a lot more sense. I accidentally left my laptop charger at a friends, so I don't have access to my best friend Stata right now. However, Excel works in a pinch.
The first graph essentially plots the distribution of OPRs since they are ranked in the data file. You can see that there is a very sharp drop off among the truly excellent teams, a wide flat region, and then a drop off on the tail end. If I had to bet, I would say the teams that are ranked extremely poorly probably played mostly with excellent teams in bad matches, and to make the math work out ended up with negative numbers. Since penalties do not subtract from scores this year, you'd have to be intentionally awful to interfere with your partners enough to really hurt their OPR. Also, there were 1009 points in this data set, so going by 100s you can basically break OPR up into percentiles.
The next graph shows OPR versus team number (which is a fairly decent approximation for age). With an R-square of .076, it obviously doesn't explain all of the variation, but I think it is interesting to see the general trends (and watch 2056 absolutely decimate one of them).
Finally, the last one to me is the most interesting. It plots the distribution of Week 1 and Week 2 OPRs, and is normalized by the number of teams since significantly more teams played Week 1 than Week 2. I apologize for the smallness of the graph, but this computer is ancient and apparently asking Powerpoint to resize this was asking way too much as the computer crashed a couple of times just getting to this point. As you can see, the top tier teams didn't change much, and the teams that didn't score much stayed essentially the same. However, the middle of the road teams to upper middle of the road got a couple of points better. I would think this is exactly what we'd hope to see (although more lifting on the extreme low end would be nice too), it should provide more exciting balanced matches.
(one you can blow up more)