So, the vertical sums still need a way to be looked at. I have an idea that will use a rudimentary first order Markov chain... but first I need to apply it to the actual draw numbers.

The basic idea is to create a first order Markov model of transition states (such as the observed transition properties of 0 to 9 for each digit in the pick 3 history, accumulating the data to a python dictionary, which will then display the results. Because we know the expectancy, we can spot where it deviates for every digit. Pick N histories will also get a Second order Markov analysis based on vertical pairs.

Once the script is written universally, then collected sum data could be read in the exact same way.

Next, vertical sums would have a probability for each transition, as well as lead in vertical sums... the idea being finding ones that match! Subtract the lead in sum most likely from the vertical sum most likely and that is your pick. I will also have the regular horizontal sums profiled for reference.

The plan seems straight forward, code implementation will be the key. Count the historical transitions and compare them to the expectancy based on a discrete uniform distribution. Instead of sums becoming another layer of abstraction, they might just end up being a potential solution!