Comment #2

hypersoniq — Mon, 22 Dec 2025 22:43:37 GMT

It is precisely what will be needed to make sense of the vertical sum data!

Comment #1

hypersoniq — Mon, 22 Dec 2025 01:15:20 GMT

1. Generate the dictionaries<br />2. Create an agent to parse the dictionaries to extract the highest transition percent, and incorporate tie breaker logic... using the most recent draw.<br />3 output the pick.<br /><br />Sounds much like the follower script from a while back... except one such tie breaker would be to scan how many draws out each transition is...

Original Blog Entry: What a Markov Chain Python Dictionary would look like.

hypersoniq — Sun, 21 Dec 2025 11:54:21 GMT

The first order dictionary would be a collection of the digits in the game followed by their observed transition percentages, such that

0 ( to 0 = 0.1, ... to 9 = 0.1)

...

9 ( to 0 = 0.1, ... to 9 = 0.1)

The percentage is calculated by the number of times a digit transitioned to each possible next digit. There are ten entries for 0 through 9. The expected value is ten percent per transition.

The second order dictionary will be similar...

00 ( to 0 = 0.01, ... to 9 = 0.01)

...

99 ( to 0 = 0.01, ... to 9 = 0.01)

This represents the transition for each combination of the previous 2 draws, such that on Thursday, column 1 was an 8 and on Friday, column one was a 6. There are 100 entries and the expected value is one percent per transition.

So this generates 10 entries for first order followed by 100 entries for second order. In the pick 3, that is 330.

So the key is to watch where the observed transition percentages deviate from the expected transition percentages.

This will require larger past draw collections for a second order study, thus this part will be strictly for the pick N games.

The next step will be to read the most recent draw, and only display the relevant entries...

If they picked 9 0 4, the column 1 output would be the 9 entry, the 0 entry and the 4 entry from each respective column... then, say the previous draw was 7 7 7, the second order entries for 79, 70 and 74 would be displayed.

I could go further and reduce the output to the highest percentage transition and just generate a pick based on all of that data without having to sift through all of it. In this way, running the script does all of the work in the background and then just presents a pick with the highest transition percentages.

There will be details to consider and edge cases to test for, such as percentage ties, but this is the basic plan to start with. This could then easily be added into the GUI framework.... [ More ]