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The time is now 4:56 pm
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June 4, 2026, 2:40 pm
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Lessons learned so far
Published:
So, I have learned that every single game history comprises a discrete uniform distribution. Testing with a Chi Square Goodness of fit test verifies that whole histories to single columns of a smaller sample pass this test. Bottom line, whatever the draw method (ball machines or RNG), the data is random enough.
Not only are past draws independent, but in the case of pick N games, the positions are also independent. Think about that... the pick 3 history is completely different for each position. I can see this easier with the new classification divisions that highlight where each neutral exists in relation to the median. Looking at pick 3 data shows 3 different histories, with different statistics for each. If there are cycles in the data, they most certainly do not happen at the same time. When follower frequency is measured, the 10 most recent followers actually prove to be better at zeroing in on a specific next digit than the whole Markov Chain display... but not always and not predictably. The shapes described by the classification are different with each position .. some have true median plateaus, while others have zero numbers at the expected value, and can tilt in favor of an above median or below median neutral band. This is the entire reason that older systems (and current ones as well) seem to have that "synchronization issue", where a system might pick 2 of three correctly, but be way off on the third.
Based on the above, it would be easy to pack it all in, I have certainly tried. I have taken many lessons in spreadsheet use and programming enough to say it was not a wasted journey... but it could just be that the journey is the thing... ever chasing that elusive solution to the impossible problem that keeps driving development and refinement.
The goal has never changed... make one pick based on the past draws. The methods have evolved, the data collected has certainly changed but the goal is still the same.
I think that learning to better interpret the data is that elusive next target. I get that feeling that statistics holds the answer, but I am still falling short on asking the right questions. I remember thinking the same thing over 20 years ago when I created my first draw history spreadsheet...

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