How to Recognize ZBLL: An Explanation and comparison of the Main Systems (2022 Edition)

[OreKehStrah]

Hi everyone,

It seems like more and more people are interested in ZBLL, learning ZBLL, etc.
Naturally, a very common question that comes up is "How do/should I recognize ZBLL?" so I finally got around to putting together a sheet that explains how each of the 5 main recognition methods work, explain the pros and cons to each one, and provide examples for each one using the same ZBLL case or cases so you can compare the systems and determine what system you like the most

Hope it helps! If you end up reading through it, let me know what system you like the most! And if you use a different system than wasn't mentioned, I'd be interested in what it is.

Here is a link to the sheet:

OreKehStrah's ZBLL Recognition Method Comparison

docs.google.com

 

[DuckubingCuber347]

NCP FTW

Thanks for making this!

 

[PapaSmurf]

Good sheet. Tran style v2 is especially cool, although I'm not sure how much better it is than 4 sided BH/second twisty PLL bit.

 

[the_chad]

BH stands for Beyer-Hardwick

 

[ruffleduck]

BH stands for Beyer-Hardwick

You probably performed a single search on the wiki on "BH" and came across the Beyer-Hardwick method which is completely unrelated to the topic of this thread. In the context of ZBLL recognition, BH stands for Baum Harris. It seems that this term isn't on the wiki.

 

[OreKehStrah]

BH stands for Beyer-Hardwick

Yes in the context of the 18 comm bld method. It also stands for Baum-Harris as a ZBLL recognition method.

 

[PapaSmurf]

BH is also the 107th element, bohrium. Here's a list of other things it stands for. Enjoy!

 

[xyzzy]

I remember independently coming up with what's essentially Tran v2 in ~2015 (before I joined, and before I learnt of the original Tran recog) as a natural extension of NMLL. (Not claiming credit for this; I was only building on Athefre's idea.)

Yet another variation of the Tran-style recog that I use sometimes:
1. AUF for CLL recog
2. look at colour pattern between UR and surrounding corners
3. find edge with opposite colours of the UR edge

So e.g. this diag pi case would be recognised as "opposite, left", while this other diag pi case would be recognised as "front [RU matching URF], back".

Probably worse than normal Tran v2.

 

[Athefre]

I remember independently coming up with what's essentially Tran v2 in ~2015 (before I joined, and before I learnt of the original Tran recog) as a natural extension of NMLL. (Not claiming credit for this; I was only building on Athefre's idea.)

Yet another variation of the Tran-style recog that I use sometimes:
1. AUF for CLL recog
2. look at colour pattern between UR and surrounding corners
3. find edge with opposite colours of the UR edge

So e.g. this diag pi case would be recognised as "opposite, left", while this other diag pi case would be recognised as "front [RU matching URF], back".

Probably worse than normal Tran v2.

Interestingly, it is something that I have also thought about. To take the concept of NMLL (and NMCLL + ATCRM) and base a recognition method around those. As was the intention behind NMLL, the concept of opposites works well for pseudo applications as well. However, there probably isn't much interest in that kind of extension for ZBLL.

 

[abunickabhi]

Hi everyone,

It seems like more and more people are interested in ZBLL, learning ZBLL, etc.
Naturally, a very common question that comes up is "How do/should I recognize ZBLL?" so I finally got around to putting together a sheet that explains how each of the 5 main recognition methods work, explain the pros and cons to each one, and provide examples for each one using the same ZBLL case or cases so you can compare the systems and determine what system you like the most

Hope it helps! If you end up reading through it, let me know what system you like the most! And if you use a different system than wasn't mentioned, I'd be interested in what it is.

Here is a link to the sheet:

OreKehStrah's ZBLL Recognition Method Comparison

docs.google.com

Good sheet. Thanks for sharing!