I like the rankings and the effort. I had done my own rankings several years ago (looks like ~5 based on the files I have), so I know how much effort is involved.
If you are looking for measures to consider for future iterations, here is how I did mine:
I was most interested in parity myself, and I didn't want to have to scrape too much data. So pulled all my data from the team pitching stats page.
I measured standard deviation of team wins, team runs scored, and team unearned runs (an attempt to measure bad fielding based tanking from pitching stats)
For each of those categories, I compared the world's standard deviation to the average of all worlds, giving either a positive score (lower than average) or negative score (worse than average). And zero basically meant average.
I had do so some monkeying around with weightings for the 3 categories to balance things out a bit and make the score on the wins measure the most heavily impactful. I also only looked at the most recent two years (weighted 2/3 for the most recent year) because I didn't want to penalize a world too much that was trying to correct prior imbalance.
If I did the rankings again (it would probably take 8 hours or so of boring cut and paste work), my guess it is would have an order that looks a lot like your list. By which I'm not saying my way was any better or worse than what you are doing, just a different idea for how to do it.