The spearman rank correlation is just a measure of whether a higher rank tends to have a higher rank of annualized return. In other words, does each subsequent bucket show a monotonic increase in returns than the prior bucket? If so, you should see a spearman correlation of 1, which is generally a good thing.
It’s just one metric that I use to evaluate of a factor or ranking system, but it doesn’t capture anything about the magnitude of changes from one bucket to the next. The pearson correlation is one way to measure that, as is fitting a regression to the x=rank and y=return line and measuring the slope of that which is also now shown in the new rank performance implementation.
As I mentioned in this comment in the “Ranking your ranking systems” thread, I tend to find the spearman correlation most useful when I’m running a rank performance on single factors, and I tend to focus more on measures of the return of the top bucket or top couple buckets when evaluating ranking systems.
My short ranking systems have a spearman correlation of 0.93-0.97 when evaluating at the granularity of 20 buckets.