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I'm pretty sure Alex is the only Roux solver in the top 100, and if not there aren't hardly any others, so I doubt it will.
Now all we need is a ZZ user somewhere up there
ZZ doesn't have as many outright advantages over CFOP that Roux does. The only real advantage I see with ZZ at a high level is potential LL methods like ZBLL. ZZ F2L sucks.
Congrats to Alex, anyone know if he's still cubing much? The less he cares about cubing, the less pressure when he competes :tu
I have heard some beginners say that they will switch to roux because Alex is good at it. It annoys me SO MUCH when they say that
That's the goal!I'm pretty sure that if you get a new WR (like your 5.11) official, every beginner will switch to cfop and roux will become the most common method !
kaijun lin is in the top 100, but you have a good point, there still far fewer rouxers than cfoppers.
Looking at it from another angle.... top 100 results, I see that Alex has a fair few against his name. Impressive!
Top 100 results:
Feliks Zemdegs 49
Alexander Lau 15
Mats Valk 9
Cornelius Dieckmann 8
Bill Wang 3
Kevin Costello III 3
Philipp Weyer 3
Lucas Etter 2
Sebastian Weyer 2
Antoine Cantin 1
Collin Burns 1
Dario Roa Sánchez 1
Drew Brads 1
Haowei Fan (樊浩玮) 1
Patrick Ponce 1
Richard Jay S. Apagar 1
These counts don't include Guildford.
and what makes it more impressive is he's gone to far fewer competitions than most of the people on this list.
[NOPARSE]SET @average100 := (SELECT max(average) FROM (SELECT average FROM Results WHERE eventId='333' and average>0 ORDER BY average LIMIT 100) tmp);
SELECT personId, entries/comps, entries, comps FROM
(SELECT personId, personName, count(*) entries
FROM Results
WHERE eventId='333' and average>0 and average<=@average100
GROUP BY personId) tmp
NATURAL JOIN
(SELECT personId, count(distinct competitionId) comps
FROM Results
GROUP BY personId) tmp2
ORDER BY entries/comps desc, personName;[/NOPARSE]
Indeed.
Using data from WCA_export710_20150413 and Stefan's WCA Data Tools.
[TABLE="class:grid,align:left"]
[TR][TD]personId[/TD][TD]entries/comps[/TD][TD]entries[/TD][TD]comps[/TD][/TR]
[TR][TD]Feliks Zemdegs[/TD][TD="align:right"]1.4412[/TD][TD="align:right"]49[/TD][TD="align:right"]34[/TD][/TR]
[TR][TD]Alexander Lau[/TD][TD="align:right"]0.9375[/TD][TD="align:right"]15[/TD][TD="align:right"]16[/TD][/TR]
[TR][TD]Seung Hyuk Nahm (남승혁)[/TD][TD="align:right"]0.2000[/TD][TD="align:right"]1[/TD][TD="align:right"]5[/TD][/TR]
[TR][TD]Bill Wang[/TD][TD="align:right"]0.1875[/TD][TD="align:right"]3[/TD][TD="align:right"]16[/TD][/TR]
[TR][TD]Cornelius Dieckmann[/TD][TD="align:right"]0.1778[/TD][TD="align:right"]8[/TD][TD="align:right"]45[/TD][/TR]
[TR][TD]Mats Valk[/TD][TD="align:right"]0.1343[/TD][TD="align:right"]9[/TD][TD="align:right"]67[/TD][/TR]
[TR][TD]Philipp Weyer[/TD][TD="align:right"]0.0968[/TD][TD="align:right"]3[/TD][TD="align:right"]31[/TD][/TR]
[TR][TD]Patrick Ponce[/TD][TD="align:right"]0.0769[/TD][TD="align:right"]1[/TD][TD="align:right"]13[/TD][/TR]
[TR][TD]Lucas Etter[/TD][TD="align:right"]0.0741[/TD][TD="align:right"]2[/TD][TD="align:right"]27[/TD][/TR]
[TR][TD]Dario Roa Sánchez[/TD][TD="align:right"]0.0714[/TD][TD="align:right"]1[/TD][TD="align:right"]14[/TD][/TR]
[TR][TD]Drew Brads[/TD][TD="align:right"]0.0714[/TD][TD="align:right"]1[/TD][TD="align:right"]14[/TD][/TR]
[TR][TD]Kevin Costello III[/TD][TD="align:right"]0.0698[/TD][TD="align:right"]3[/TD][TD="align:right"]43[/TD][/TR]
[TR][TD]Sebastian Weyer[/TD][TD="align:right"]0.0667[/TD][TD="align:right"]2[/TD][TD="align:right"]30[/TD][/TR]
[TR][TD]Antoine Cantin[/TD][TD="align:right"]0.0400[/TD][TD="align:right"]1[/TD][TD="align:right"]25[/TD][/TR]
[TR][TD]Haowei Fan (樊浩玮)[/TD][TD="align:right"]0.0370[/TD][TD="align:right"]1[/TD][TD="align:right"]27[/TD][/TR]
[TR][TD]Collin Burns[/TD][TD="align:right"]0.0323[/TD][TD="align:right"]1[/TD][TD="align:right"]31[/TD][/TR]
[TR][TD]Richard Jay S. Apagar[/TD][TD="align:right"]0.0323[/TD][TD="align:right"]1[/TD][TD="align:right"]31[/TD][/TR]
[/TABLE]
Code:[NOPARSE]SET @average100 := (SELECT max(average) FROM (SELECT average FROM Results WHERE eventId='333' and average>0 ORDER BY average LIMIT 100) tmp); SELECT personId, entries/comps, entries, comps FROM (SELECT personId, personName, count(*) entries FROM Results WHERE eventId='333' and average>0 and average<=@average100 GROUP BY personId) tmp NATURAL JOIN (SELECT personId, count(distinct competitionId) comps FROM Results GROUP BY personId) tmp2 ORDER BY entries/comps desc, personName;[/NOPARSE]
so if there's no video, would it be possible for reconstructions?
If Alex can remember his solutions.
which is actually pretty hard to do.