Now I have the data I was looking for from the start. Learned a bit about getting expected output from the followers... even though there was 1,500 draws, the data of followers was limited to the expectancy, so the formula was not to calculate by the sample size, but rather the sample size divided by 10.

I also learned that while direct standard deviation works for raw frequency, it needs to be constricted a bit to produce similar results... so that was as easy as st_dev x 0.8

Where I went wrong was in the type... rather than using Pandas series for collecting the data to output, I just needed a list! That problem plagued me for days.

To validate the data, I compared the output of the previous version to make sure the counts agreed, and they did.

I may not be any closer to the winner's circle, but I do enjoy being able to take an idea and completely realize it in code from a clean sheet. Even the bug squishing was enjoyable ultimately because it is an exercise in real world problem solving, and there are no guides or documents for lottery analysis software, you are on your own in this domain.

Now for the really hard part, finding correlations in the output. This will take far longer than writing the script, and there may not be a solution, but at least I have some busy work to keep engaged with the hobby.