So the premise is simple... we will stick with the pick 3 to keep it simplified.

A follower distribution is simply counting how many times each possible number has followed the last number drawn in each position. In a discrete (only digits 0 through 9) uniform (random, every number has an equal chance to appear next) distribution, if a 6 was drawn, the expected value of each number from 0 to 9 to follow it is 10%

When we count each of the digits that has followed a 6 in the draw history, we are looking for deviation from that 10% across all 10 possible values. This is how we train that first order Markov chain model to give the result, it is simply a count of the number of times each of the ten digits followed a 6 (or whatever the last number drawn was). This is done against the entire draw history (so for pick 3 evening in PA, that is over 17,000 draws).

They say that the data will converge once there are enough draws to that expected 10% per value.

In addition to the distribution values, I also include the last 10 followers of that number to see if maybe a recent trend is in place that would indicate a better pick than the one with the highest frequency count per column.

So it fits the Markov chain "memoryless" property because it is only presented with the most recent draw. If I were to use all 100 values possible when you look at the last 2 draws, that would become a second order Markov chain... this requires 10x the data, and we know that past draws are not dependent on each other, so there is really no need to go into that extra level of complexity.

Is it a valid predictor? Of course not, as one thing I have learned from these years of study is that contrary to what Gail Howard says, the most of something does not always happen... the most frequent follower in each column is not always the next draw. The hot numbers rarely come out together. What it ends up being is a way to see micro bias in distribution reality vs distribution expectancy.

Has it worked? In the brief time I had actually used it to play, it did bring in one straight pick 3 mid day hit within the first few months.

One thing I have noticed in all of this development time is that I still need to learn how to interpret the volumes of data I can generate.

Why am I including this in the GUI app that is for classification? Because I feel this is an important part of the puzzle. While I have not seen more than 1 straight win on either system, it might be a critical read of both system outputs that reveals a better guess. Plus, the GUI was always intended to be a framework, good for including any number of systems.

Hooking up the update scripts to the kivyMD framework was a walk in the park compared to integrating the classifier and follower scrips... but that is the path I have chosen.

As a dev note, using git to implement proper version control is so much better than my ad hock file naming convention of adding copies appended with _Vx.