This would undoubtedly be that I can store data in the same file and only target the columns needed.

This means I can store draw data alongside it's corresponding angle data and just choose to isolate one or the other. I can also expand on what is there.

For each angle (signed, forming a bell curve type distribution) there is a corresponding line length that comes from the vector, this will also be added. The expected distribution would be more logarithmic, with shorter distances expected to make up the majority of the draws.

Why is this exciting? Because I would not have to go through abstraction processes like "one hot encoding" to turn features into binary data, I can just use the actual data instead.

This process would create data points (draw history) with features (lead in angle and line length) which would allow the use of machine learning algorithms to help figure out what in the data may be of importance.

The engineering part of machine learning goes through two steps when you are unsure what is significant...

1. An unsupervised algorithm where it can spot and report patterns and help estimate weights

2. A supervised algorithm where it will actually use the info from the previous step to help build and train a model to obtain predictions.

This was always an idea, but now it gets a step closer to realization.

Now the history file can be packed with information. Basic aggregations like odd/even and high/low, alongside the vector components. Using step 1 to determine what is important and what is irrelevant, then using this information to craft step 2.

Yet I do not need to remake versions of the history files because individual columns can be targeted in the scripts. Multiple scripts, one history mega file for each game...

That will not be an easy slam dunk like these scripts that spit out follower distributions, but it represents a goal, and when you have a goal, you have motivation...

Much to do...