Sub-cycle events for weighting

Started by VLS, Dec 21, 2022, 05:37 AM

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VLS

Within a single 37-spin cycle, there are many sub-cycles that can be used as indicators to weight events:

37 / 2 = 18 spins twice
37 / 3 = 12 spins thrice
37 / 4 = 9 spins four times
37 / 5 = 7 spins five times
37 / 6 = 6 spins six times
...
(rounded down)

Within a set of 3 full cycles, there are:

- Six 18-spin sub-cycles.
- Nine 12-spin sub-cycles.
- Twelve 9-spin sub-cycles.
- Fifteen 7-spin sub-cycles.
- Eighteen 6-spin sub-cycles.
...

There are trends within full cycles, smaller trends mid-cycle, mini-trends at quarter-cycle... and so forth. When overlapping them all. this is usually enough to determine the current "weight" for an event as compared to the other tracked ones, giving the current picture.

For instance, a 3-number event has 1 in 12. If it appeared on the last three 12-spin sub-cycles, it can be called consistent for the full cycle.

Now, within some sub-cycles, this same tracked event can have more shows at some trams, which also adds more weight to it in comparison to the other similar 3-number events that are also rated as consistent in the tracked events pool.

When using some frameworks, different events can add to the weight of the same straight-up location, hence each individual number gets more weight given how the many groups/sets it belongs to are faring on the current game status/picture.

In the end, monitoring the actual set of events and how they perform is king.

Whatever your set of arbitrary events, it is how they fare that matters (in relation to current state), with "current state" being updated dynamically, leading to different bets over time, even when some tracked events may have the same weight, because the spin history itself unties them differently when looking back, letting roulette "tell you" where to bet, according to what's going on with it.

This can be considered an example of those very few "player advantages" found hidden in the game's short term, along with money management. You can shuffle your numerical groups to create multiple dynamic timelines to choose from, even as there is one (1) single numerical stream coming from roulette.

For instance, there are many 3-number groups you can create, all with the same probability, yet pointing to different numbers as its cycles unfold.

This makes your betting dynamic.

Many players can play the same spins as you yet you are having your separate timeline; same as each of them having his/her own. This makes multi-player tracking & betting quite dynamic (which deserves a post of its own).

Trends within trends within trends, all within clearly delimited cycles/sub-cycles, in a rolling window basis.

(Like one of those overlapping sigma charts, dynamically generated, made possible by modern computing power)

sigma.png
Email/Paypal: betselection@gmail.com

-- Victor

VLS

Quote from: VLS on Dec 21, 2022, 05:37 AMLike one of those overlapping sigma charts, dynamically generated ...

...If you want to be picky with conceptual visualization, then like an actual weighting chart:

600px-Acoustic_weighting_curves_(1).svg.png

600px-Lindos3.svg.png

https://en.wikipedia.org/wiki/Weighting

O:-)
Email/Paypal: betselection@gmail.com

-- Victor

BlueBuzzard

Nice post.

Quote from: VLS on Dec 21, 2022, 05:37 AMFor instance, there are many 3-number groups you can create, all with the same probability, yet pointing to different numbers as its cycles unfold.

This makes your betting dynamic.

I've done quite well with this concept, but successful implementation takes some thought and care to get right. It can happen that some of the 3-number groups (or whatever size group you choose) are working against others (trend-wise). Actually, this is inevitable because if some locations are doing well it must mean that others aren't. The statistical concept of "correlation" can be used to good effect in this scenario. I'll start a new thread on it later and show how it can be measured.

Then there is the issue of "overlap". There is often a huge number of possible groups of a given size, but ideally you need to select the groups which have the minimum number of numbers in common. There is a technique for doing this which deserves its own thread.