Since the previous 2 systems are focused on a per column approach (because the positional digits are independent), I will stick with a per column approach here as well. That means eliminating the horizontal sum portion, which will simplify the spreadsheet.

The end result will be a cascading sheet, where the groups of columns get shorter as the calculations move across.

The first columns will hold the draw history, the next columns will start ten rows down and sum the 10 rows that make up the first k sample. The next set will make up the N sample size, so they will start 30 rows below the k samples and provide both an average and a mode.

There should be a way to run a basic back test right in the sheet once the data is populated for the average and mode of 30 k samples. That would be to take the average and mode (separately) and subtract the last 9 in the k sample and see if it is equal to the next digit. In this way, if the initial k and N parameters are inconclusive, they can be changed.

It is a great deal of tedium creating such a sheet, but that will serve as the base of coding a function in Python, as the commands to get such data in a pandas data frame are much faster.

So we will see if there is any merit to such a system by counting hits on the back test. That will decide whether it is going to include a mode or an average (or both) when it moves to Python.

This is still a single approach on 3 independent histories, so there will remain that synchronization issue. Hopefully the specific profile information for each gathered from the other 2 functions might be used together at some point to determine the "state" of each position.

I could make the single script prototype open to settings where k and N are different for each position...

At some point I may need to record the output of all of these functions and feed it into a machine learning algo or 2 to find stuff I am currently not seeing.

The sheet is built, with one tab for mid day pick 5 and one sheet for evening pick 5... so far the sums(k) are done as is both the rounded average and mode for the sample size (N)

From here I need to calculate some summary statistics (the range of the sums, the range of the samples and their distribution), and then come up with a formula to test the effectiveness. This part may take some time...

At a quick glance, there is an expected difference in the positions, some run higher (consistently) than others.

Would have been easier with the pick 2, but in this test I wanted more columns to compare.