Alex B71
Member
I felt the need to add an opinion to this.
I first heard of this approach from a video by Tao Yu and since then i have been messing around with this method on and off, my first approach was to mix it with TSLE+TTLL but i would not recommend that as it is extremely inefficient.
I then took the proposed approach of EO+DFDB and then ZBLL and i can say that i love it. My previous main methods were CFOP and ZZ (Both sub-12 but CFOP was faster on average with slightly under half of ZBLL known and committed to muscle memory) so blockbuilding in this manner was difficult and EO+DFDB was confusing. I generated MU based EO+DFDB algs that were as move efficient as they could be whilst still maintaining ergonomics, and i'm very glad i took an algorithmic approach.
ZBLL recognition is pretty much dismissed with this approach as you instantly know your corner recognition after F2B and you know the edge placement from as early as just ending EO+DFDB. The EO+DFDB algs are not even "algs" and seem as easy as commutators to learn (seasoned Roux uses will find this step extremely simple). Most of the cases are reducible into another case with one move and very quickly become second nature due to the intuitiveness of the step.
To summarize, This method harnesses the low move count and awesome flexibility of F2B, has an extremely easy to learn and fast to execute EO+DFDB stage and cuts the recognition time of ZBLL drastically. It's a great hybrid of two well proven methods and has potential to be very fast. The only problems i have with this method are the changing of hand placements between steps and that fact you will need full ZBLL to really harness this method.
It's worth trying.
I first heard of this approach from a video by Tao Yu and since then i have been messing around with this method on and off, my first approach was to mix it with TSLE+TTLL but i would not recommend that as it is extremely inefficient.
I then took the proposed approach of EO+DFDB and then ZBLL and i can say that i love it. My previous main methods were CFOP and ZZ (Both sub-12 but CFOP was faster on average with slightly under half of ZBLL known and committed to muscle memory) so blockbuilding in this manner was difficult and EO+DFDB was confusing. I generated MU based EO+DFDB algs that were as move efficient as they could be whilst still maintaining ergonomics, and i'm very glad i took an algorithmic approach.
ZBLL recognition is pretty much dismissed with this approach as you instantly know your corner recognition after F2B and you know the edge placement from as early as just ending EO+DFDB. The EO+DFDB algs are not even "algs" and seem as easy as commutators to learn (seasoned Roux uses will find this step extremely simple). Most of the cases are reducible into another case with one move and very quickly become second nature due to the intuitiveness of the step.
To summarize, This method harnesses the low move count and awesome flexibility of F2B, has an extremely easy to learn and fast to execute EO+DFDB stage and cuts the recognition time of ZBLL drastically. It's a great hybrid of two well proven methods and has potential to be very fast. The only problems i have with this method are the changing of hand placements between steps and that fact you will need full ZBLL to really harness this method.
It's worth trying.