Tutorial - Typical usage examples of GAT Engine

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Tutorial - Typical usage examples of GAT Engine

Post by lottoarchitect » Sat Feb 15, 2020 12:52 pm

This question comes up regularly, so it could be a good idea to post some general usage ideas of this program.

First, we need to clarify that the engine will never stop scanning; the process is open ended and means it will always scan to find even better overall solutions. So, no popup message will show to say "JOB DONE". Typical runs may require up to 1 or 2 million scanned GATs, less or more but the advice is usually to let the engine run for as long as we are willing to wait for predictions. In reality 6-24 hours will yield close to the best that may be found.

1) The default and original operation is the use of Run Factor. This mode is applicable in all panorama modes. The idea is to pick a GAT which demonstrates good desired qualities. The reality is all the proposed GATs that end up at a panorama mode are already competitive GATs to pick. It doesn't matter that much which one we may pick as long as we are expecting to keep using that GAT in the next few future draws; its proposed quality is expected to show up sooner or later. So we can equally pick randomly among all the proposed GATs. If we want to invest some more time in examining these GATs, the advice here is to pick GATs in the middle columns mostly (i.e. 3 or 4 hits category) as these have the property to produce more regularly at least some medium hits compared to the others as demonstrated by their hit performance. We also care for the regularity of hits observed in the red graph. That means we want our picked GAT to produce a desired hit all over its test range, not having all the good hits concentrated in only part of it. Once we decide on which GAT ID to use, we retain it at several future draws. The idea here is, this GAT may not be successful at the very next draw but its overall qualities are expected to show up within the next few draws (the 100/X rule). Remember we retain the ID, that means we perform a new scan with the new draws of the lottery to get the new numbers that GAT ID proposes (and Run Factor increases accordingly).

Make a search in the forum about regularity of hits, 100/X rule and Run Factor utilization for more detailed info on these terms.

2) Using several GATs as individual predictions. In this approach we set the engine to request the minimum of numbers our lottery can draw. So if our game is e.g. a 5/50 one, we'll examine only GATs requesting 5 numbers. The idea here is, most GATs will not deliver the good prediction at the next draw, some may do, but their overall characteristics are expected to emerge within the next few draws (the 100/X) rule. Typical advice here is to either use augmentative and GATs at the middle columns (see method 1) or the Hits+delay panorama to make our initial pick of GATs to use. Set the "GATs per column" setting to the total blocks we want to play i.e. if we want to play 20 lottery entries, set "GATs per column" = 20 and pick all the GATs in that column. Nobody forces us to a column however, if the user feels the need to pick GATs in other columns too, by all means use them.

3) A combination of method 2 above and method 1 with Run Factor. Once we have established a set of GAT ID tables to use as per method 2 above, we retain them and follow them in the future draws with the use of Run Factor.

4) Once-off approach with the build-up panorama mode. This aims to trap the GATs that seemingly have focused on the signature of the lottery, so we expect this to continue at the next lottery draw. This is typically an once-off approach, not suited to use with Run Factor. The only exception is if that particular GAT we picked does deliver the desired hit at the exact next draw in which case we may want to continue with that GAT expecting to perform again. We can also use method 2 with build-up mode by requesting the bare minimum of numbers and picking as many GATs as needed to pick our individual tickets.

5) There are even more advanced ways to use GAT ID tables. i.e. try to find the numbers most occurring among the various GATs as the best candidates to use. Variations could include various runs with different settings for tested draws/stat.data settings. This goes beyond this simple guide however. Users have also posted their methodologies, like the ablation approach where we try to suggest what numbers to eliminate from our current selection.

Finally, the absolute mode is more tailored in matrix constructions. If a good GAT is to be successful in this mode, it will be already proposed in the augmentative panorama, therefore no need to check the absolute panorama mode if we do not plan to make use of matrix constructions.

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