SurfSupDude
SILVER
I manually pulled the top 9 G09 GA teams in SoCal (that is, all GA teams in SoCal) and the top 9 G09 NPL teams in SoCal. The average GA rank was slightly lower (better) than the average NPL rank. Which would seemingly contradict the graphic, even though they were quite close.You're right, there are any number of ways to present the differences in league. One of the common ways that was done before, is taking the average rating, of all teams that were assigned per league. This helped leagues that had a limited top to bottom spread, and hurt leagues that had a large spread. For example, an average NPL team is somewhere near the midpack of NPL 2 (assuming ECNL-RL, NPL1, NPL2, NPL3), while the average MLS N team is somewhere in the middle of MLS N. The top RL team vs. the average NPL 2 team is going to be expected to be a blowout, while the top MLS N team vs. the midpack MLS N team is expected to be a win, but not nearly the difference implied.
Another way to compare, is to ask the question - how do the top teams in league A vs the top teams in league B stack up, and the way this could be done is choosing the top 10 teams by league. Of course - if a league only has 9 teams total, that means all of them and an average of them would show pretty weak, as the top 9 teams in league would include both the top teams as well as all of the bottom teams. Some would say that league that has less than 10 teams in it state-wide is already weak - but that's a different discussion. A potential fix might be to only take the top 10% of teams rather than the top 10, but for a league that has 300 teams, that's comparing 30 teams against another league where the top 10% means less than 1 team. Top 10 seems like the better choice of the two, given only those 2 options - but in either option, the disfavored league would complain about the results.
One of the things that this type of view does show pretty clearly, is that the top teams in several leagues, are much closer together, than the earlier comparison of average teams in league. It is answering a different question, "How would one top team do against another", rather than "How would an average team of League A stack up against an average team of League B". Both questions have value, but maybe the first is a better way to compare leagues, maybe some disagree.
Drilling into the league descriptions to see which teams went where would end at least some of the questions about what each league represents, and confirming that the team association / league association jives with local understanding. It's quite possible now that the underlying data has issues, especially if the particular league is harder to identify - and while it would be good to know how it was done - it would also be good to just know the top 10 teams, so there would be inherent validation over the data, and therefore much of the results.
All of this becomes irrelevant when comparing one specific team against another specific team, where their ratings can be compared directly, and a probability between the two can be compared and displayed. It will always be more accurate in predicting which team might best the other on the day, rather than comparing the leagues they play in to make the same type of inference.
But then I realized that I accidentally pulled the G10 data.
I might pull the #s for G09 tomorrow and try again.