While it has been interesting, though not quite so rewarding using the all neutral QP generator in the last few weeks, I have an idea to further process the data in phase one.

This would involve building Markov chains using the output and using the resultant Markov probability to narrow down to the neutral that would be most likely to appear in the 7 draw window for each column.

I will have to consult the writing on the math version of stack overflow and maybe some R documentation.

With the rpy2 library, I can execute R commands directly within Python, while I can use R's reticulate library to do the reverse.

I have cursory knowledge of Markov's theories at best, so this will involve some time to get a picture of what needs to be done. The QP generator will get some more use for the time being.

This would make the selection process a 2 layer approach, with the first layer classifying the numbers by frequency, then the next layer taking this data and determining the best digit to play in each column.

The goal is always to get ONE combo to play, regardless of the game. A classification phase one with a probabilistic phase 2 would result in just that... ONE best guess.

I am also replacing the definition of hot and cold from using the standard deviation to using the observed inter quartile range, as this would allow for a more robust division of classifications that is not as sensitive to the outliers in the group... standard deviation varies wildly between runs.