How to measure how well a list of numbers is uniformly decreasing? I have a list of numbers that I want to measure how well are sorted/decrease. I want something more detailed than simply inversion, I want to know how uniformly they decrease.
For examples, the list (10,8,6,4,2,0) should have a higher score than (10,9,8,,3,2,1).
(10,8,6,4,2,0) and (10, 8, 6, 7, 4, 2) should both have a higher score than (10.5,10.4,10.3, 10.1, 8, 6).
In other words, I want to measure how well the numbers descend, and also I want to favour the magnitude of descending.
My idea is to assign each number an index, and then to measure the correlation. However, what kind of correlation would I want? Should it be Pearson since I am looking for a linear correlation. Or is there a better method?
More info:
In some tests I have done Pearson gave the list [10.91, 4.84, 4.75, 4.75, 4.397, 4.37, 3.85, 3.05, 3.05] a lower correlation than the list [14.71, 14.71, 14.71, 10.15, 10.15, 10.17, 10.22, 10.22, 10.22].
The first list is more desirable to me since the range is bigger - we can see the list is descending more strongly. Should I use spearmans rank instead? If so, why?

Convert to differences, so (10, 8, 6, 4, 2, 0) = (-2, -2, -2, -2, -2) and (10, 8, 6, 7, 4, 2) = (-2, -2, 1, -3, -2). Find mean, variance, and standard deviation, and relative standard deviation (aka coefficient of variation) for each list. Lower relative standard deviation means more uniform descent, higher relative standard deviation means less uniform descent.
If you want to also penalize lists having individually smaller decreases, construct a composite score of these two important aspects. Use mean difference of each list as a weight in the final score, scaling it however you need, e.g., final score = mean difference - 2 * relative standard deviation, in which case score of (10,8,6,4,2,0) = 2, (10,8,6,7,4,2) = -1.11, (10.5,10.4,10.3,10.1,8,6)= -0.98, and as desired (10,8,6,4,2,0) is better than (10,8,6,7,4,2), both of which are better than (10.5,10.4,10.3,10.1,8,6).
