Original Blog Entry: How the hot/cold data is verified correct...

hypersoniq — Fri, 21 Feb 2025 16:35:55 GMT

It is actually quite simple with a spreadsheet.

1. Were the last Y draws correct and in the right order? They should match exactly the last Y draws on the spreadsheet history.

2. Were the distribution counts accurate? Select X draws above the Y value and manually check the counts, they were equal.

3. Were the hot/cold thresholds applied correctly? The equation is

(D/X)*100

Where D is the distribution count of the digit in question and X is the value of X, which is the total number of draws involved in the count.

For example, in the previous post the first column picked a number that was drawn 8 times as a HOT (criteria being the number of times appearing is = 12%) so that looks like

(8/60)*100 = 13.33333 %

Which is greater than 12% so a valid HOT by the criteria.

That is one thing that makes coding difficult, is validating that what you expected the program to do to what it is actually doing. If the program ran that means it was free of Syntax errors, but only through testing and validation can you be sure the program is free of Semantic errors... those are a prime source of bugs that produce unexpected and erroneous output.

I am definitely a fan of the Pandas library for Python, it is a huge time saver to use a data frame to hold data. It is definitely suited to help in the work those of us do trying to solve impossible problems by manipulating data trying to win a game that has a massive house advantage... maybe one day...... [ More ]