Lottery results are what statisticians call a "Discrete Uniform Distribution". The premise that each value in a discrete ( limited membership, like 0 through 9) set has an equal chance of appearing makes the graph of results different from, say, a Bell Curve.

In this distribution, one standard deviation is considered statistically significant. The standard deviation is simply the square root of the variance, which is a measure of how much each data point sits away from the central tendency (the mean).

For the purpose of the difference between Hot, Cold and Neutral classification, the expectancy is each digit should appear 10% of the time in a pick N game where the set is (0,1,2,3,4,5,6,7 8,9).

In the development of the Python script, I went with a gut feeling of >= 12% to be Hot and 8% or less to be Cold, with anything in between to be classified as neutral.

After a few runs of the script, I took the distribution counts an placed them into R studio to run some simple tests on the standard deviation and found that it was usually between 2.5 and 3. I was not far off! So, it is relatively simple to calculate standard deviation at run time using the stats library or Pandas. The next update will be incorporating this functionality into the script. By doing this, I would no longer be unsure of the statistical significance of the Hots and Colds, as it would be correct for each column... 1 standard deviation above or greater for HOT and one standard deviation or below for COLD.

Here is the interesting part... when taking a sample of draws, the larger X becomes (X being the number of past draws), the more the results occurred nearer their expectancy. This would result in fewer overall Hots or Colds and more Neutrals. The bottom line is that too few draws produces volatile variance and too many produce too steady of a variance.

What does this mean? I need to find the "sweet spot" of the number of draws where Hots and Colds are produced. That yet unknown range where there is just enough volatility in the variance to have a shot at gaining actionable output.

So, no finish line yet, but progress is being made!