Viewing from different "vantage points" yielded no particular benefit.

Creating a recent list for weighting was not statistically significant because the data does NOT exhibit seasonality.

Enter recursion... I never tried to reprocess the resultant list for a follower among followers...

Recursively reducing this follower list by running it through the same function might just be what I was missing... a better indicator of what is next than just the most frequent number in the follower list.

It is sometimes difficult to get a proper base case (when recursion should stop) without running experiments, so that is the focus for now, with parallel development on the time series classification angle.

Pick 3 and pick 5 are still the targets. And this time I am writing the functions to auto detect the number of columns so I do not need a different function for each game type... a one size fits all approach!

I walked away from the laptop for a few days to try and re-imagine the next steps and this was my " a ha" moment...

Ready to get back in the ring!