Now that I have some numbers on the frequency of the observation of interest using entire draw histories rather than samples, it is time to start figuring out how to use the collected data.

First thing is that in the production script, I will run the function twice, once with an offset of 7, and again with an offset of 0. This will give classification results for the last 7 draws, but the training set will contain 143 of the same draws. Tracking the change from the playable selection data from the last classification might be one avenue of selection.

Next, looking at the classification data held in the csv files will hopefully reveal something.

One strategy will be to isolate all of the NNN or NNNNN draws on their own sheet to look at the most frequent percentage rankings. Both long term and perhaps over the last 150 occurrences. Looking at both frequency AND range, to maybe build a profile of what area of neutral the numbers come from most.

I still want to see if the quartiles offer any clues, so I may include them in the next back test data output, with the idea that the median (Q2) and it's variance from the expectancy may point to the best pick from the neutral set.

The goal is to develop some sort of guidelines to selection NOW, while there are only 10 digits in 3 or 5 columns BEFORE trying to solve the multitude of issues that will undoubtedly arise when moving to jackpot data.

What would really be helpful would be a local LLM trained specifically in statistics to help see things I will miss.