In the raw frequency function, quartiles were literally 2 lines of code! Thanks to the statistics library, you call it as a quantile on your list, and use N=4, this gives you 25%(Q1), 50%(Q2, Median) and 75%(Q3) all on the same line. The second line was added so I could round the output to 2 decimal places for readability.

That was the easy part... changing the follower function to allow classification of the output to match the functionality of the raw frequency data is proving much harder... because  counting followers is a 2 step process where you first get a list to show every number that followed the last draw, THEN you have to count that list. If I try to make it a one shot function, it fails when validating the data. So basically the current project is adding the classification part after the second part completes but before it fetches the next list.... getting there slowly, but surely.

Looking at the two sets of data together has no immediate correlation presenting itself, though adding quartiles is showing that the neutral numbers drawn seem to sit close to one of the 3 quartiles, more so than the standard deviation. SD is the classifier, but the quartiles are where I feel some consistency will be found.

One funny error was that I accidentally hooked the quartile calculation to the list of digits rather than the frequency... the output there is ALWAYS the same... 1.5, 4.5 and 7.5 when your list is 0 to 9. Fortunately I recognized exactly where I messed up after I saw the output