# All-Round Rankings (KinchRanks)



## kinch2002 (May 25, 2015)

I devised KinchRanks in May 2015 as a new system to rank all-round ability. Read all about it in the post below, or go to the website where I'm hosting the info and rankings,

https://wca.cuber.pro/kinch/persons

Homepage content:



Spoiler: Calculation



1. For each event, you get a score of WR/PB x 100 (this will be in the range 0-100)
2. The average across your 18 scores is the final KinchRank score.

The "events" used are:
Averages for 3x3x3, 4x4x4, 5x5x5, 2x2x2, OH, Feet, Megaminx, Pyraminx, Square-1, Clock, Skewb, 6x6x6, 7x7x7.
Singles for 4bld, 5bld, Multibld
Averages or Singles for 3bld and FM (whichever is best for you)

If you haven't done the event then of course you get 0 for that event.
For multibld, your result is adjusted to a single number Points + ProportionofHourLeft. At the time of writing, Marcin Kowalkcyk has a score of 41/41 54:14, which equates to 41.0961. Someone with e.g. 11 points in 45:00 would get (11+0.25)/41.0961 = 0.2737. This calculation ensures that more points always equals a better score, no matter the time spent. Less time spent still gives a better score.





Spoiler: Notes to help understanding



- Higher scores are of course better.
- If you're WR holder, you'd get a score of 100 for that event (WR/WR x 100)
- No matter how slow you are, you will always get some score
- Being double the WR gets you 50 for an event. Same in every event. Treble the WR would be 33.3 points.
- The maximum theoretical final score is 100, although you'd require WR in every event for that.
- For regional rankings, e.g. KinchRanks UK, the regional records are used as the benchmarks instead of the WR (basic calculation of NR/PB x 100).
- For country rankings, the NRs are used instead of PBs (basic calculation of WR/NR x 100).





Spoiler: Background and Reasoning



For a while I had been dissatisfied by the "Sum of Ranks" Single and Average calculation used to show an all-round ability. That's not to say it's a bad system, but I had in my mind that there were some underlying issues with it when trying to assess all-round ability.

After a few other attempts to eliminate the issues I had perceived, I think KinchRanks has finally hit the nail on the head.

Here are some things that I see as drawbacks of the current Sum of Ranks system:
1. There are 2 lists (single and average).
2. Not competing in an event can "kill" your ranking, even if you're amazing at everything else (e.g. Feliks not doing feet, Vincent not doing 7x7x7 for ages).
3. Massive event biases. For example, you have to be really good at 3x3 to get high up. Bigbld barely matters in the slightest. This is due to the depth of events and the participation rates.
4. Personal improvement doesn't always equate to any gains - indeed if you're the World Record holder then there's absolutely no chance of you gaining anything from improvement!

I believe my system has removed all 4 of these perceived issues, as explained:
1. There is one list, that uses a combination of averages and singles as appropriate.
2. No result in an event just means you don't gain any points for it, rather that essentially losing an unlimited number of points.
3. All events have a score range 0 to 100 and are weighted equally in the final result. Your scores do not rely on how good anyone else is (apart from the WR that is the benchmark)
4. Every PB (in average or single as appropriate) gives you points!





Spoiler: FAQs



_What is the reasoning behind the use of averages and/or singles?_
Averages are generally a better indicator of ability than singles, so those are used where available. For 3bld and FM, the average rankings are not yet established enough, so single ranks are allowed to be used instead if advantageous for a person. FM may be restricted to averages only in the future if the rankings reach a good point to do that.

_Doesn't gaining a second while near the WR gain you a lot more score than gaining a second while far away from it?_
This is a good thing, as a second is much harder to cut off when near the WR!

_Is it fair that everyone loses score when a new WR is set?_
Yes I believe it is. The rankings are supposed to be equal across events, and to do that we must use benchmarks that are as equal in ability as each other. Think of a WR as a recalibration of the system. Over time, it should converge towards an even more equal system than it is currently!

_Is FM a little easier to get a good score in?_
Yes I believe it is slightly. This is due to the completely different nature of the event compared to the others. The effect is not thought to be big enough to warrant implementing an exception.

_Are 2x2 and other short events a little harder to get a good score in?_
Yes I think short events are generally a little harder to get good scores because the WR is likely to be a lucky average. Again, the effect is not thought to be a big one

_Why are all the Multibld scores so low?!_
Up your game everyone! If Maskow can managed 41 cubes, I'm sure if people put time into it like they do with other events, they could get good scores. Being able to 2/2 just means you've learnt 3bld and have some extra locations. Practising 3bld doesn't count as practising Multibld, so I'm not concerned that 2/2 barely gets you anything 

_Why am I ranked much lower than I was in Sum of Ranks?_
The most likely cause is that you practised events that have a lot of depth to them a lot (3x3, 2x2 being the biggest culprits). You used to be higher because it's hard to get a low world ranking in those events, and many people were so far behind you in the rankings for them that their other events couldn't make up for it.

_Why am I ranked much higher than I was in Sum of Ranks?_
There are 3 main causes for this.
Firstly, you are missing some events completely, and were massively penalised for it before with huge ranking scores.
Secondly, you are relatively good at events without much depth to them (bigbld and feet the biggest culprits). You didn't gain much on anyone for your skill before, because others could just avoid them completely without a big penalty. Now you get a score for those events, and others get 0.
Thirdly, that pesky bad ranking in 3x3 no longer drags you down. Many people are very good at 3x3 but have a terrible world ranking because of the depth of the event.


----------



## Username (May 25, 2015)

I don't like this because I drop around 30 ranks :^)



Spoiler



in all seriousness I think this is a great, well thought out idea. I'd love to see this implemented!


----------



## Pedro (May 25, 2015)

Nice idea. It might be cool to do this for a single competition, to get the MVP of that particular comp.
Can you maybe post one with the top-1000? Or 10000 if doesn't take forever to compute.


----------



## Kit Clement (May 25, 2015)

I'm not sure that I like completely ignoring singles -- it might be interesting to have a system where events with both single and average have their WR/PB (or PB/WR for MBLD) score computed, and then are given some appropriate weight (25% single, 75% average) that still reflects the value in averages. It's a bit arbitrary, but I do feel like it's better than throwing away this information about the potential for getting very fast solves.

Regardless -- I've always been a fan of the proportional ranks system, but this is an even better improvement on that.



Pedro said:


> Nice idea. It might be cool to do this for a single competition, to get the MVP of that particular comp.



Now this is something I'd really like to try in the future.


----------



## Ninja Storm (May 25, 2015)

Pedro said:


> Nice idea. It might be cool to do this for a single competition, to get the MVP of that particular comp.



I really like this idea, especially at larger competitions such as US Nationals or Worlds.


----------



## qqwref (May 25, 2015)

Great idea. I'd also like to see:
- BLD only and non-BLD only
- Cubes only (i.e. NxNxN) and Non-Cubes only
- The same ranking, but until X year (2003, 2004, ..., 2014)


----------



## Pedro (May 25, 2015)

Kit Clement said:


> I'm not sure that I like completely ignoring singles -- it might be interesting to have a system where events with both single and average have their WR/PB (or PB/WR for MBLD) score computed, and then are given some appropriate weight (25% single, 75% average) that still reflects the value in averages. It's a bit arbitrary, but I do feel like it's better than throwing away this information about the potential for getting very fast solves.
> 
> Regardless -- I've always been a fan of the proportional ranks system, but this is an even better improvement on that.
> 
> ...



From what I understood, singles are included for the bld and FM events. If you check his table, you see for example that Feliks' result for 333bf is 0.526, which is 21.17 / 40.28.


----------



## Kit Clement (May 25, 2015)

For fun -- this is an individual competition result from Peach State using this ranking system. Events with multiple rounds were based solely on round 1.



Spoiler




*Name**333**444**222**333bf**333oh**333fm**clock**skewb**666**777**333mbf**SUM**Daniel Wannamaker*0.8939544110.8459119500.8021120290.5686274510.7907692310.76959620106.670971271*Callum Hales-Jepp*0.71872510.724231290.63593380610.8631422920.8529411760.7795753290.6022304830006.176779476*Kit Clement*0.5922521340.53877140.5123809520.3044619420.6068079190.90625110005.460924348*Ben Cofield*0.6307692310.7260566110.5512295080.4382258290.6673032850000.971559412014.985143876*Jacob Ambrose*0.5716096320.5646863690.43387096800.499856938100.466187050.8071039560.62077883304.964093748*Anthony Brooks*10.55687732310.4801471720.953602620000003.990627115*Sydney Weaver*0.6207845840.7457188370.6314553990.5541401270.51671103200.4026109660.4625267670003.933947712*Thomas Kohann*0.7940140850.6229208250.71164021200.765892153000.9878048780003.882272152*Clark Cheng*0.7091194970.6582879240010000.8476322650.65828125403.87332094*David Ludwig*0.7947136560.8432785410.6241299300.863568957000.3188976380003.444588722*Jonas Ventresca*0.5609452740.5031573290.43527508100.378794449000.550084890.7413197003.169576722*Jared Stinson*0.5427196150.5762424990.56751054900.402441834000.4883195180002.577234014*Chris Tran*0.5782051280.674774775000.4437388870.604166667000002.300885457*Chris Foster*0.6169630640.653920028000.662495260000001.933378352*Phillip Lane*0.3796296300.553497942000.46774193500.4119516850001.812821192*Ramone Jackson*0.6013333330.3806668020.36698499300.4417193430000001.790704471*Aussie Greene*0000000010.63774780301.637747803*James LaChance*0.5410917820.43724460.539078156000000001.517414538*Austin Pitchford*0.4828693790.445780264000000.5543199320001.482969575*Parker Oxford*0.46614987100.40695915300000.353904970001.227013994*Aarya Kapani*0.39929172200.38872832400000.4369521240001.22497217*Nathan Graves*0.4873041600.390420900000.3328197230001.210544782*Dylan Miller*0.55644663800.64354067000000001.199987308*Raymond Goslow*0.68075471700.504690432000000001.185445149*Chaewon Min*0.4465346530.587450980000000001.033985634*Tristan Rappon*0.43428021200.28495762700000.2794307890000.998668628*Robbie Inglett*0.27250755300.33292079200000.3614054660000.966833811*Caleb Saiyasak*0.40124555200.28286014700000.2147828970000.898888596*Ethan Botelho*0.27865307400.39442815200000.2017434620000.874824688*Michael Lu*0.47373949600.346649485000000000.82038898*Seth Bailey*0.41662817600.321385902000000000.738014078*Kendal Reid*0.43701550400.27962578000000000.716641284*Calvin Bernardo*0.30248155600.313885648000000000.616367204*Eric Vande Linde*0.35414212800.253057385000000000.607199513*James Bobbitt*0.30985915500.270896274000000000.580755429*Brian Weinshenker*0.27458143100.301907969000000000.576489399*Noah Joiner*0.24944690300.304298643000000000.553745545*Eldridge Harris III*0.2829360100.269809428000000000.552745438*Jackson McNab*000.25717017200000.269102990000.526273162*Jack Hayhurst*0.15345355600.335830212000000000.489283768*Jorge Luis Avila*0.25559648600.214171975000000000.469768461*Chris Krueger*0.450099800000000000.4500998*Reese McGee*0.221785100.223980017000000000.445765116*Ty Fazenbaker*0.23489583300.184246575000000000.419142409*Tejas Manem*0.32728592200000000000.327285922*Neil Verma*0.12787071200.163030303000000000.290901015*Tanner Corbett*0.13660457400.066028473000000000.202633047*Agastya Gudipati*0.12026666700000000000.120266667*Jacob Freed*000.113790186000000000.113790186*Yonatan Nigussie*000.107000796000000000.107000796*Ved Rao*0.08455193100000000000.084551931






Pedro said:


> From what I understood, singles are included for the bld and FM events. If you check his table, you see for example that Feliks' result for 333bf is 0.526, which is 21.17 / 40.28.



I meant that within any event with both average and single rankings (like 3x3x3) it would be nice to have some sort of weighted average of the two WR/PB scores.


----------



## tseitsei (May 25, 2015)

qqwref said:


> Great idea. I'd also like to see:
> - BLD only



Me too


----------



## MatsBergsten (May 25, 2015)

tseitsei said:


> Me too



Me too . Even if it is just the opposite of what Matthew says/wants, *one list!*

I just summed up BLD and finds that Grzegorz is the best, followed by Oliver and Marcell.
(And you are in 31:th place)


----------



## tseitsei (May 25, 2015)

MatsBergsten said:


> Me too . Even if it is just the opposite of what Matthew says/wants, *one list!*
> 
> I just summed up BLD and finds that Grzegorz is the best, followed by Oliver and Marcell.
> (And you are in 31:th place)



Yeah I thought Grzegorz and Ollie would be 1 & 2 

Maskow wins amazingly much at MBLD but doesn't really do bigBLD so...

EDIT: In a month I'll hopefully be much higher  we have a comp coming with all events. Hopefully I can get sub-4 4bld sub-11 5bld and sup-15 points mbld


----------



## DuffyEdge (May 25, 2015)

I think this is a great and well thought out idea, much better than the current system


----------



## TheCoolMinxer (May 25, 2015)

I have to agree it's a great system but I am not in the top100 anymore  xD


----------



## XTowncuber (May 25, 2015)

who is the 2013 all-around world champion?


----------



## DanpHan (May 25, 2015)

Kit Clement said:


> For fun -- this is an individual competition result from Peach State using this ranking system.
> 
> 
> 
> ...



Wait, shouldn't my 7x7 be 1?


----------



## Kit Clement (May 25, 2015)

DanpHan said:


> Wait, shouldn't my 7x7 be 1?



Yep -- fixed.


----------



## Dene (May 26, 2015)

Hang on a second, something is wrong with that list. Why aren't I in the top 100?


----------



## Iggy (May 26, 2015)

Nice idea, I like it :tu



Spoiler



Top 15


----------



## Tim Major (May 26, 2015)

Spoiler: individualresults



3x3: 0.535
4x4: 0.468
5x5: 0.443
2x2: 0.478
3bld: 0.199
3oh: 0.539
fmc: 0.714
mega: 0.363
pyra: 0.684
sq1: 0.326
clock: 0.389
sk: 0.678
6x6: 0.350
7x7: 0
4bld: 0
5bld: 0
multi: 0



Total: 6.166

Nice effort Daniel, I really like sum of ranks related stats. It's funny how my results suggest my best events are FMC->Pyra->Skewb->OH->3x3, when regionally 3x3 is one of my worst events. I guess it suggests Oceania is strong at 3x3. These sorts of stats whilst flawed are really good for comparing regions.

My only suggestion would be to make the value used the top 1% result, instead of the WR result. I think the top 1% result is more indicative than the WR, as several events have WRs as outliers at the moment (OH, 5x5, multi). I can't do fancy enough excel stuff to try this, but would be grateful if someone else did.

Also personal request, top 100 for Oceanic "Danielranks"? 
Data: https://www.worldcubeassociation.or...Id=_Oceania&eventId=&years=&separate=Separate
People: https://www.worldcubeassociation.org/results/misc/sum_of_ranks/?regionId=_Oceania&average=Average


----------



## biscuit (May 26, 2015)

How difficult would it be to write a program to do the calculations automatically? I may make a website to do it later (once I understand how to calculate it lol)


----------



## AlexMaass (May 26, 2015)

Pedro said:


> Nice idea. It might be cool to do this for a single competition, to get the MVP of that particular comp.


It would be nice to do that, and give a prize for it, you'd need a bot/program to go on cubecomps and use the cubecomps results to determine the MVP of the comp though. Anyone want to make this?


----------



## kinch2002 (May 26, 2015)

Thanks for the feedback everyone.



Pedro said:


> Nice idea. It might be cool to do this for a single competition, to get the MVP of that particular comp.
> Can you maybe post one with the top-1000? Or 10000 if doesn't take forever to compute.


Results for all people is included in the OP as a csv download


Kit Clement said:


> I'm not sure that I like completely ignoring singles -- it might be interesting to have a system where events with both single and average have their WR/PB (or PB/WR for MBLD) score computed, and then are given some appropriate weight (25% single, 75% average) that still reflects the value in averages. It's a bit arbitrary, but I do feel like it's better than throwing away this information about the potential for getting very fast solves.


Interesting idea. I don't mind ignoring singles . I haven't seen much evidence that some people are more likely to get good singles than others, and to be honest, if they have the same average as someone else then they probably also get more bad singles too, and therefore no need to be rewarded for it haha.



qqwref said:


> Great idea. I'd also like to see:
> - BLD only and non-BLD only
> - Cubes only (i.e. NxNxN) and Non-Cubes only
> - The same ranking, but until X year (2003, 2004, ..., 2014)


I can do the first 2 easily (will try to remember tomorrow), but historic results will be much more tricky as rankings at other points in time are not stored explicitly in a table. We only have current rankings tables, and you'd have to derive everyone's old PBs from the full results table.



Tim Major said:


> My only suggestion would be to make the value used the top 1% result, instead of the WR result. I think the top 1% result is more indicative than the WR, as several events have WRs as outliers at the moment (OH, 5x5, multi). I can't do fancy enough excel stuff to try this, but would be grateful if someone else did.
> Also personal request, top 100 for Oceanic "Danielranks"?


Thanks for the idea - I didn't really thing of that, although I think there are 3 reasons in my mind that tell me not to do that.
- Using top 1% result re-introduces some event biases (e.g. so many more noobs in 3x3, not enough depth in 5bld) that I was so happy to have eliminated 
- I don't see a problem with WRs seeming like outliers. Remember that if the WR is really far ahead, then everyone (apart from the WR holder) is getting a lower score so there's not really any relative difference, and we can't deny that the WR holder deserves to gain a big amount on everyone else.
- The calculation is really simple at the moment (aside from a small adjustment in multibld), and that's a really appealing factor to me 



Tim Major said:


> Also personal request, top 100 for Oceanic "Danielranks"?


Will do tomorrow hopefully. Should be easy enough to edit the query. Also lolname


----------



## Tim Major (May 27, 2015)

kinch2002 said:


> Thanks for the idea - I didn't really thing of that, although I think there are 3 reasons in my mind that tell me not to do that.
> - Using top 1% result re-introduces some event biases (e.g. so many more noobs in 3x3, not enough depth in 5bld) that I was so happy to have eliminated
> - I don't see a problem with WRs seeming like outliers. Remember that if the WR is really far ahead, then everyone (apart from the WR holder) is getting a lower score so there's not really any relative difference, and we can't deny that the WR holder deserves to gain a big amount on everyone else.
> - The calculation is really simple at the moment (aside from a small adjustment in multibld), and that's a really appealing factor to me



-Whilst there are "so many more noobs" in 3x3, there are also so many more fast solvers in 3x3. With not enough depth in 5bld, again, I chose 1% not say... 5th so that the % would factor in the total number of competitors in 5bld.
-The problem with the outlier WRs in my opinion is the misrepresentation of what's good/bad in the event. For example, in your top 100, Tim Wong is the only person who got over .5 in multi bld, with 0.512. It makes it seem that multi is one of his worst events, but it is no doubt his best (5th in the world, NAR holder, 2nd at WC2013...). Your scoring lists 3x3 as his best when it obviously isn't. Using the 1% method, he'd get 10.57/10.21=1.035 for 3x3 and 22.05/17.01=1.30.
Now it shows a more accurate representation of his skill in each event against the general population, instead of against an outlier WR. I'm not saying this way is necessarily better than your method, and I don't think it would shuffle the order of the list at all(?), but it would allow more accurate comparisons of someone's skill in each event.
-This calculation would not really be much more complex. Just where your file searches for the WR in each event, it needs to search for the 1% average.




kinch2002 said:


> Will do tomorrow hopefully. Should be easy enough to edit the query. Also lolname



Thanks, and I wasn't sure if "rank" had the same derogatory meaning in the UK as it does it Australia but let's pretend it was accidental.


----------



## Hssandwich (May 27, 2015)

My results...
0.495 3x3
0.475 4x4
0.439 5x5
0.509 2x2
0.066 BLD
0.414 OH
0 Feet
0 FMC
0.312 Megaminx
0.663 Pyraminx
0.208 Sq-1
0.373 Clock
0.722 Skewb
0 6x6
0 7x7
0 MBLD
0 4BLD

4.676


----------



## kinch2002 (May 27, 2015)

Spoiler: No BLD




*Rank**Name**Score**333**444**555**222**333oh**333ft**minx**pyram**sq1**clock**skewb**666**777**333fm*1Feliks Zemdegs*10.592*1.0000.9931.0000.8890.9270.0000.8410.5650.5500.5030.5510.9810.9920.8002Yu Nakajima (中島悠)*10.235*0.7440.7650.8590.6500.7190.7980.7420.5800.6450.7150.5390.8320.8130.8333Robert Yau*9.949*0.7970.7820.8390.7080.7350.4700.7800.6480.6540.5810.5850.7950.8060.7694Louis Cormier*9.849*0.7270.7290.7540.6900.7170.7050.9370.6500.4630.6480.4920.7790.8160.7415Antoine Cantin*9.839*0.8160.7050.7060.6021.0000.7590.6480.6430.4710.7440.6830.7010.7150.6456Jayden McNeill*9.748*0.7520.7310.7180.8940.7990.2200.6210.7070.6340.5501.0000.7480.6590.7147Lucas Wesche*9.482*0.6800.7560.8700.5330.6640.4780.8370.4210.6540.5130.5310.9040.9500.6908Vincent Hartanto Utomo*9.443*0.7700.8190.8220.6320.7090.5190.6370.8150.5280.6560.5350.7720.4590.7699Evan Liu*9.270*0.6490.6980.7120.5780.6510.4640.5830.5240.4721.0000.6900.7330.7760.74110Mats Valk*9.075*0.8780.9300.8290.7410.8120.2030.4680.4470.5680.4350.3870.8320.7770.76911Bence Barát*9.062*0.6790.6840.7880.7340.5950.2720.6300.4380.6290.4390.4810.8550.9690.87012Wojciech Knott*9.016*0.6480.7620.7100.4780.6710.8110.5700.5460.4100.9050.5140.7120.6730.60613Kevin Costello III*8.954*0.8500.8910.7940.5860.8080.2570.6740.4480.3240.4380.5360.8230.8980.62514Daniel Sheppard*8.908*0.6240.6630.6640.6060.5750.4540.5890.5940.5440.8200.5750.6770.6880.83315John Brechon*8.896*0.6900.7400.7740.6300.6470.2490.6870.6040.5080.5810.4630.7810.8280.71416Simon Westlund*8.878*0.7090.7180.7250.6110.5820.4660.8920.5510.4850.6160.3230.7310.7290.74117Erik Akkersdijk*8.876*0.7440.7780.7730.5970.6020.6170.6670.5310.3860.5490.3180.7550.7250.83318Dan Cohen*8.812*0.6880.7320.7960.7020.6790.2480.5850.5240.7020.6210.2790.8050.8060.64519Mitchell Lane*8.745*0.6880.7500.7120.6020.6540.3330.6140.5550.4230.5840.6680.7570.7600.64520Rami Sbahi*8.657*0.6860.6540.5400.9470.6100.7660.6570.6810.5460.5080.8880.0000.4340.74121Emanuel Rheinert*8.539*0.7120.7510.6990.6530.6100.2470.3950.4400.8480.4270.4580.7710.7280.80022Jong-Ho Jeong (정종호)*8.477*0.6990.8000.8150.5880.7630.3070.7890.4420.4430.2290.4530.7460.7570.64523Przemysław Kaleta*8.456*0.7340.7700.7260.6990.9170.7480.3170.3790.3070.0000.6710.7640.7790.64524Jan Bentlage*8.452*0.5830.5910.5720.5970.5910.4870.6010.5280.5150.5980.6620.6510.6750.80025Ivan Zabrodin*8.449*0.6040.6350.6620.5480.5500.4420.5560.4430.3530.7650.7000.7320.7440.71426SeungBeom Cho (조승범)*8.330*0.7290.7490.7600.8700.7140.0000.5740.6480.4880.0000.7230.7570.7130.60627Walker Welch*8.328*0.5830.6030.6950.5350.5180.3130.5360.4770.4000.7170.6080.6970.7770.87028Hunor Bózsing*8.232*0.7150.7360.7880.5900.7760.0000.5170.4160.3710.4360.6870.7780.7750.64529Christopher Olson*8.179*0.7130.6670.6650.9360.6640.2150.4230.4290.3720.6160.5390.6850.6490.60630Nathan Dwyer*8.163*0.6580.6650.6790.5570.5960.0000.5980.4610.6930.6610.5160.6800.6580.74131Carlos Méndez García-Barroso*8.119*0.7520.6380.6620.8330.6820.1710.5040.5260.4510.6820.3700.6190.5630.66732Milán Baticz*8.096*0.7180.7210.7410.6320.6820.2450.6570.4790.4340.5720.0000.7450.7550.71433Justin Thomas*7.944*0.6210.7140.7270.4890.6010.0000.6330.4150.6100.5850.3240.8610.7760.58834Daniel Wallin*7.933*0.6270.5830.5260.5250.5500.2940.4790.5410.5290.9050.8070.5180.5470.50035Jorge Castillo Matas*7.930*0.6280.6530.7070.5840.5970.0000.5840.5580.4180.4870.4690.7130.7640.76936Jakub Kipa*7.902*0.7730.6410.6720.7480.6681.0000.5600.6000.0000.0000.4010.6300.5430.66737Michał Halczuk*7.868*0.6650.7110.8620.5250.5880.1690.5900.2640.7690.4540.3660.9260.9790.00038Yu Sajima (佐島優)*7.865*0.7300.7200.7350.4800.5960.6930.5140.4270.2560.7890.2780.6490.4990.50039Callum Hales-Jepp*7.861*0.5610.6040.6050.5650.6170.6650.6420.3930.3530.6790.3560.5870.5880.64540Yuhei Takagi (高木佑平)*7.843*0.6310.6350.6380.3430.7330.9240.5730.3720.4070.4570.2230.5960.6440.66741Maarten Smit*7.805*0.6390.5760.6150.5690.5590.1270.6030.5240.5200.8200.2730.6310.5790.76942Yinghao Wang (王鹰豪)*7.742*0.6990.6960.6240.8330.5970.2110.4770.4920.2800.6370.4890.5860.5490.57143Wilhelm Kilders*7.707*0.5790.5800.6360.4470.5530.1920.4420.4120.5000.6090.5980.7260.8070.62544Sébastien Auroux*7.689*0.5320.5640.5270.5820.4820.3460.3690.5570.3670.7670.4900.5660.5880.95245Drew Brads*7.679*0.8080.7090.6000.8210.5860.0000.4351.0000.2090.0000.6540.6130.6170.62546Matic Omulec*7.678*0.6070.6190.7080.4760.4930.2760.8500.4800.4540.3550.2910.7110.7310.62547Dmitry Zvyagintsev*7.677*0.7140.7380.7560.5610.7830.0000.4870.3940.3780.2170.3810.8120.8300.62548Nathaniel Berg*7.652*0.6820.6350.6330.4380.4970.1810.4590.5800.2980.9570.6460.5620.6090.47649Michał Pleskowicz*7.648*0.8060.6560.6730.7510.8710.1680.7690.5460.4870.2730.0000.5050.4260.71450Lucas Etter*7.623*0.8700.7890.7291.0000.6780.0000.4070.5210.2920.3580.5280.5980.5010.35151Michael Gottlieb*7.609*0.6110.6570.7410.4170.6780.0000.5740.3790.4380.5400.3860.7260.8180.64552Ciarán Beahan*7.569*0.6730.7620.8010.4530.6410.4220.4440.7440.3560.0000.5360.9060.8320.00053Wataru Hashimura (端村航)*7.529*0.6390.5720.6280.4520.5330.3940.5910.4080.2920.6230.5210.5390.5960.74154Dmitry Kryuzban*7.508*0.5860.5080.5830.4050.4290.4020.6150.6900.5170.6900.2200.6750.6170.57155Ainesh Sevellaraja*7.485*0.6100.5470.5500.5100.5280.2760.5910.6880.5350.6610.4260.4730.5190.57156Chris Wall*7.469*0.4930.5110.6090.3520.5240.3340.8030.3800.3880.5880.5760.5940.6720.64557Mattia Furlan*7.438*0.6340.7120.8790.5020.5440.0000.5730.4670.0000.7080.4980.9201.0000.00058Daniel Cano Salgado*7.415*0.5930.6150.6340.4130.5980.4290.5730.3990.3070.6370.3710.6430.6140.58859Kim Jokinen*7.405*0.6820.6200.5860.5480.6080.1380.4420.6510.2840.6600.5130.5110.5150.64560Sei Sugama (洲鎌星)*7.398*0.7810.8010.8320.5820.7590.0000.6420.4090.1900.1630.1920.7240.7540.57161Blake Thompson*7.394*0.6970.6740.6560.5780.5370.0000.4240.4290.4510.3790.5130.6730.6140.76962Cornelius Dieckmann*7.390*0.8690.7210.7430.6610.8360.0000.4470.3670.3280.3100.1260.6510.6420.69063AJ Blair*7.383*0.5700.5700.6110.4530.5170.3860.5560.3820.5500.6370.4260.5250.5760.62564Ivan Torgashov*7.363*0.6540.7090.7860.5390.6910.0000.3790.4880.2100.2850.3520.8030.8220.64565Corey Sakowski*7.276*0.5560.5620.6050.4410.5510.2710.5020.5410.4210.3760.6260.6300.5490.64566Jakob Kogler*7.275*0.5780.6460.7010.4380.5190.2610.3880.4080.1960.6710.3920.6830.6530.74167Yumu Tabuchi (田渕雄夢)*7.269*0.7930.6810.7620.4600.7060.5560.3650.3300.4610.2560.1990.5310.4790.69068Dmitry Aniskin*7.263*0.6980.6510.6090.5560.6760.0000.6430.4020.3230.5060.4780.5240.5730.62569Artem Melikian (Артем Мелікян)*7.257*0.6080.5610.5080.3920.6110.7060.4690.5240.2480.3510.6290.5240.4800.64570Dániel Varga*7.256*0.5910.5980.6500.5020.5910.2600.4580.4700.3780.4030.3660.6490.6520.69071Chunyu Zhang 2 (张春雨)*7.230*0.5180.5790.6630.5190.4740.1590.4360.5360.4960.3610.3990.6840.7150.69072Edward Lin*7.221*0.7430.7180.7490.6450.7150.2320.3540.3840.2580.3790.2130.6250.5820.62573Michael Young*7.221*0.5660.5780.5800.4620.6360.0000.4970.3800.6870.4070.5090.5690.6080.74174Antonie Paterakis*7.218*0.7160.6550.6370.7770.4650.0990.4150.5130.3010.3410.5150.5990.5990.58875Wojciech Szatanowski*7.209*0.6250.6250.5610.5330.6410.2830.4410.4830.2270.4890.4640.5610.5870.69076Hendry Cahyadi*7.208*0.7030.6430.6670.5390.6190.4810.4560.5170.0000.5900.3690.5690.5290.52677Akash Rupela*7.185*0.6540.5790.6360.4750.6030.2810.4540.3880.5310.3760.3920.6160.6290.57178Morten Arborg*7.180*0.7530.7710.7300.5110.7450.0000.5610.4190.3380.4160.0000.6580.6320.64579Ihor Bilchenko (Ігор Більченко)*7.162*0.6990.6980.6000.5710.6300.1650.3610.4620.2160.5330.4920.5850.5620.58880Zijia Feng (冯子甲)*7.160*0.5530.6700.5800.4110.5100.3050.3030.3730.5390.8140.4160.4570.7170.51381Henrik Buus Aagaard*7.139*0.5580.4920.4870.5180.6080.7680.4010.3820.3900.5440.2690.4780.5030.74182Lee Chiang (蔣礪)*7.137*0.6040.6470.6980.4880.6570.0000.5030.6260.2620.7030.6650.6150.6690.00083Yi-Fan Wu (吳亦凡)*7.132*0.7480.7960.8340.5460.7810.0000.3510.4010.3650.4920.3610.7740.6820.00084Thompson Clarke*7.130*0.7320.6900.5150.6110.7350.3140.5160.4600.2050.6460.1770.5130.4760.54185Niko Ronkainen*7.126*0.5270.5170.5480.5110.4950.2750.3500.4820.2350.8200.6150.5630.5190.66786Timothy Sun*7.124*0.6480.5840.6470.4660.5050.5430.4220.3130.4490.5790.2790.4740.4730.74187Asia Konvittayayotin (เอเชีย กรวิทยโยธิน)*7.119*0.7690.7400.8330.5670.6830.2250.4900.3420.1540.2260.0000.7920.7720.52688Pavel Yushkevich*7.119*0.6860.6210.6220.5060.6800.0000.4100.3730.0000.6610.4550.6930.7210.69089Ben Whitmore*7.111*0.6610.5490.6150.6840.5170.1680.5130.3750.4500.4260.3720.5660.6100.60690Sungho Hong (홍성호)*7.070*0.6960.6030.6480.5300.7620.0000.4450.3470.3900.7110.3530.5920.4930.50091Andy Denney*7.052*0.7020.6150.6470.5320.5740.0000.5610.3600.3120.3660.6150.6060.6080.55692David Woner*7.049*0.6160.5970.5620.4950.6180.2200.4190.5050.5510.7040.0000.5580.4630.74193Weixing Zhang (张炜星)*7.033*0.7060.7570.7940.5800.5940.1720.3950.3300.3140.0000.2830.7370.7460.62594James Hildreth*7.020*0.5220.5660.6830.5080.5730.1430.3380.2990.3720.4600.4320.7110.7480.66795Michael Röhrer*7.017*0.5420.5990.6700.3410.3490.0000.6420.3360.5470.5760.2590.7760.7750.60696Jure Gregorc*7.001*0.7080.6080.6670.5080.5550.0000.6030.3810.2690.5670.4260.5470.5550.60697Alexis Rodrigo Cazu Mendoza*6.991*0.5930.5660.6730.3640.5440.3490.6450.5040.3180.0000.5000.6680.7550.51398Viktor Ejlertsson*6.985*0.5650.5940.5940.4830.5130.2500.4990.4370.2790.5760.5940.6300.6580.31399Sebastian Werb*6.975*0.6080.5900.5790.4830.6080.4080.4300.4090.5410.3490.3940.5390.5920.444100Xiao Hu (胡霄)*6.972*0.5780.6360.6280.4310.6060.0000.4310.3770.4010.4420.5330.6020.6620.645






Spoiler: Only BLD




*Rank**Name**Score**333bf**444bf**555bf**333mbf*1Grzegorz Jałocha*3.271*0.8380.8090.9900.6342Oliver Frost*3.245*0.6851.0001.0000.5603Marcell Endrey*3.178*0.8100.8660.9170.5854Marcin Kowalczyk*2.674*1.0000.6740.0001.0005Cale Schoon*2.663*0.4950.8780.8680.4216Oleg Gritsenko*2.628*0.6350.6800.8740.4397Kaijun Lin (林恺俊)*2.604*0.8830.7190.6320.3708Roman Strakhov*2.379*0.5040.6760.9500.2509Zane Carney*2.351*0.6820.4930.6630.51210Linus Fresz*2.343*0.6040.7780.6660.29511Daniel Sheppard*2.320*0.4550.6610.7890.41612Ainesh Sevellaraja*2.298*0.6550.6870.5410.41513Marcin Zalewski*2.281*0.8890.6550.4200.31714Noah Arthurs*2.203*0.7740.6180.3970.41415Gabriel Alejandro Orozco Casillas*2.154*0.8360.5250.3790.41416Andreas Pohl*2.015*0.5900.5930.5620.27017Matthew Sheerin*1.985*0.5080.5480.6600.26918Tomoyuki Hiraide (平出智之)*1.930*0.6200.5370.4270.34519Callum Hales-Jepp*1.879*0.4100.5420.6070.32020Taku Yanai (矢内拓)*1.876*0.7190.4790.4310.24821Yu Nakajima (中島悠)*1.866*0.5280.5070.6350.19622Tim Wong*1.854*0.6530.4070.2820.51223Muhammad Iril Khairul Anam*1.850*0.6700.3530.4370.39124Dmitry Karyakin*1.840*0.3730.5470.6260.29425Jakob Kogler*1.787*0.4830.4500.4640.39026Matteo Colombo*1.738*0.5530.5970.4920.09527Riley Woo*1.725*0.6660.4490.2700.34128Angel Lim*1.720*0.7110.4140.3510.24329Ville Seppänen*1.685*0.5360.4620.5710.11630Janne Lehtimäki*1.677*0.5720.5120.3730.22131Corey Sakowski*1.663*0.4750.4750.4380.27532Kai Jiptner*1.653*0.5140.4300.3640.34433Anton Rostovikov*1.629*0.6480.3840.3270.27134Chester Lian*1.592*0.4180.3680.3890.41635Feliks Zemdegs*1.553*0.5260.5990.2940.13436Evan Brown*1.552*0.4540.3700.4810.24737István Kocza*1.546*0.3680.3770.4340.36638Yuxin Wang (王宇欣)*1.533*0.7070.2980.1380.39039Mark Boyanowski*1.509*0.5000.4300.2590.31940Yuhei Takagi (高木佑平)*1.485*0.5320.4440.3580.15141Kabyanil Talukdar*1.460*0.8270.2640.0000.36942Mike Hughey*1.449*0.3760.3550.4480.26943Rafał Guzewicz*1.449*0.3900.3840.4050.27044François Courtès*1.438*0.4420.3660.3830.24745Gianfranco Huanqui*1.434*0.7070.2870.0000.44046Nikhil Mande*1.434*0.4280.3290.3600.31847Tim Habermaas*1.431*0.2820.3250.4310.39248Sebastiano Tronto*1.411*0.7820.4040.0000.22449Dennis Strehlau*1.402*0.3750.3590.3930.27450Julian David*1.388*0.4110.4260.3920.15951Olli Vikstedt*1.386*0.4770.3550.3810.17352Ragil Setyadi*1.382*0.4900.2370.4590.19653Vincent Hartanto Utomo*1.377*0.4270.4470.2120.29254Brandon Mikel*1.376*0.3390.3610.4060.27055Bence Barát*1.366*0.5840.3370.2450.20156Mats Bergsten*1.338*0.3440.3680.3550.27057Vojtěch Dvořák*1.318*0.5080.1110.3570.34258Zhizhe Liang (梁稚喆)*1.299*0.2980.4230.4540.12459Liliya Kamaltdinova*1.299*0.5020.2960.3210.18060Aldo Feandri*1.293*0.4210.3700.2590.24361Chris Hardwick*1.289*0.2560.4560.4310.14662Liping Jia (贾立平)*1.266*0.5620.4610.0000.24363Timothy Sun*1.255*0.3760.3510.3720.15564Aan Candra Nugroho*1.241*0.4140.3790.4180.03065Walter Pereira Rodrigues de Souza*1.238*0.4590.2870.1990.29266Simon Westlund*1.237*0.3700.2880.3300.24967Didiet Aditya Bayu Kusuma*1.179*0.3730.3470.3030.15668Nevins Chan Pak Hoong (陈百鸿)*1.164*0.2330.3820.3970.15369Bill Wang*1.155*0.5640.5440.0000.04770Tom Nelson*1.146*0.2820.2670.2320.36671Jan Bentlage*1.141*0.2870.2980.3560.20072Antoine Cantin*1.137*0.4050.2440.2670.22173Amos Tay Swee Hui*1.131*0.5090.5080.0000.11474Fabrizio Cirnigliaro*1.104*0.2280.2600.2720.34375Henrik Olsson*1.085*0.2570.2040.3060.31976Kevin Montano*1.068*0.3990.0000.3970.27277Chunyu Zhang 2 (张春雨)*1.063*0.2980.3510.3620.05378Shivam Bansal*1.063*0.3970.2490.1740.24379Tomoki Kubo (久保友樹)*1.048*0.3390.2480.2850.17680Kun Zhu (朱坤)*1.043*0.2820.2860.4760.00081Adrian Lehmann*1.015*0.6400.2820.0000.09382Evan Liu*1.013*0.2740.2890.2930.15683Jong-Ho Jeong (정종호)*1.012*0.6070.3360.0000.06984Eric Limeback*1.008*0.5510.0910.0000.36685Manu Vereecken*1.006*0.3220.2450.3260.11386Arvid Skarrie*1.001*0.4110.2060.2280.15587Cornelius Dieckmann*0.992*0.4370.2300.1760.15088Akash Rupela*0.990*0.4070.2850.1230.17589Tomas Kristiansson*0.988*0.4580.1620.0000.36790Lucas Wesche*0.980*0.3510.2270.2280.17591Gregor Billing*0.961*0.4460.2040.1610.15092Yumu Tabuchi (田渕雄夢)*0.960*0.3840.2500.1790.14793Fakhri Raihaan*0.958*0.3230.2820.1950.15894Shiori Sato (佐藤詩織)*0.957*0.3900.1750.0000.39295Sei Sugama (洲鎌星)*0.956*0.4290.2560.1240.14796Shuto Ueno (上野柊斗)*0.953*0.3080.2890.2020.15497Lars Vennike Nielsson*0.944*0.1980.2790.3800.08798Jiawen Wu (吴嘉文)*0.929*0.3430.2450.2310.11099Alessandro Solito*0.927*0.8320.0000.0000.095100Andrey Ivanov*0.926*0.3910.3050.0000.230






Spoiler: 2x2x2-7x7x7




*Rank**Name**Score**333**444**555**222**666**777*1Feliks Zemdegs*5.855*1.0000.9931.0000.8890.9810.9922Mats Valk*4.986*0.8780.9300.8290.7410.8320.7773Kevin Hays*4.885*0.7230.8130.9420.4521.0000.9554Kevin Costello III*4.843*0.8500.8910.7940.5860.8230.8985Robert Yau*4.727*0.7970.7820.8390.7080.7950.8066Bence Barát*4.708*0.6790.6840.7880.7340.8550.9697Lucas Wesche*4.694*0.6800.7560.8700.5330.9040.9508Michał Halczuk*4.669*0.6650.7110.8620.5250.9260.9799Yu Nakajima (中島悠)*4.663*0.7440.7650.8590.6500.8320.81310Mattia Furlan*4.647*0.6340.7120.8790.5020.9201.00011SeungBeom Cho (조승범)*4.577*0.7290.7490.7600.8700.7570.71312Dan Cohen*4.529*0.6880.7320.7960.7020.8050.80613Jayden McNeill*4.501*0.7520.7310.7180.8940.7480.65914Louis Cormier*4.496*0.7270.7290.7540.6900.7790.81615Lucas Etter*4.488*0.8700.7890.7291.0000.5980.50116Vladislav Shavelskiy*4.480*0.6210.7910.8460.3410.9000.98017Sei Sugama (洲鎌星)*4.474*0.7810.8010.8320.5820.7240.75418Asia Konvittayayotin (เอเชีย กรวิทยโยธิน)*4.473*0.7690.7400.8330.5670.7920.77219Przemysław Kaleta*4.472*0.7340.7700.7260.6990.7640.77920Sameer Mahmood*4.448*0.6810.6980.7200.8160.7570.77621John Brechon*4.443*0.6900.7400.7740.6300.7810.82822Ciarán Beahan*4.427*0.6730.7620.8010.4530.9060.83223Dmitry Zvyagintsev*4.412*0.7140.7380.7560.5610.8120.83024Jong-Ho Jeong (정종호)*4.405*0.6990.8000.8150.5880.7460.75725Hunor Bózsing*4.383*0.7150.7360.7880.5900.7780.77526Yi-Fan Wu (吳亦凡)*4.379*0.7480.7960.8340.5460.7740.68227Erik Akkersdijk*4.373*0.7440.7780.7730.5970.7550.72528Weixing Zhang (张炜星)*4.320*0.7060.7570.7940.5800.7370.74629Emanuel Rheinert*4.315*0.7120.7510.6990.6530.7710.72830Christopher Olson*4.314*0.7130.6670.6650.9360.6850.64931Ivan Torgashov*4.314*0.6540.7090.7860.5390.8030.82232Milán Baticz*4.312*0.7180.7210.7410.6320.7450.75533Lin Chen (陈霖)*4.301*0.6880.5790.7730.4080.8900.96334Cornelius Dieckmann*4.286*0.8690.7210.7430.6610.6510.64235Kailong Li (李开隆)*4.283*0.7000.7020.8340.5050.7370.80536Vincent Hartanto Utomo*4.274*0.7700.8190.8220.6320.7720.45937Mitchell Lane*4.268*0.6880.7500.7120.6020.7570.76038Antoine Cantin*4.246*0.8160.7050.7060.6020.7010.71539Dario Roa Sánchez*4.234*0.8590.8070.8060.5570.6770.52840Keaton Ellis*4.227*0.7960.7340.7600.4340.7220.78141Simon Westlund*4.223*0.7090.7180.7250.6110.7310.72942Howard Wong Jun Yen (黄俊仁)*4.201*0.6900.7360.7590.5650.7330.71843Justin Thomas*4.189*0.6210.7140.7270.4890.8610.77644Nipat Charoenpholphant (นิพัฒน์ เจริญพลพันธุ์)*4.171*0.7800.7320.7430.6270.6750.61345Drew Brads*4.169*0.8080.7090.6000.8210.6130.61746Syuhei Omura (大村周平)*4.166*0.6790.6950.7830.4830.7400.78547Pedro Henrique Da Silva Roque*4.162*0.7190.7840.7940.4690.7460.65048Niko Paavilainen*4.149*0.6030.6010.7840.4090.8340.91749Evan Liu*4.146*0.6490.6980.7120.5780.7330.77650Dmitry Dobrjakov*4.145*0.7930.7310.7350.5330.6460.70751Giovanni Contardi*4.135*0.7660.8070.8020.4370.7150.60852Emily Wang*4.128*0.6650.7230.7580.3420.8810.76053Seung Hyuk Nahm (남승혁)*4.125*0.8200.8250.7880.4600.6490.58454Bill Wang*4.104*0.8720.8060.8600.8790.6870.00055Ivan Vynnyk (Іван Винник)*4.078*0.7470.7310.6860.5520.7160.64656Alexandre Carlier*4.076*0.7800.7830.8180.6480.5540.49357Congbiao Jiang (蒋丛骉)*4.068*0.6860.5920.7420.4030.8560.78958Carlos Méndez García-Barroso*4.067*0.7520.6380.6620.8330.6190.56359Jacob Hutnyk*4.066*0.7230.7860.7470.6020.6780.53060Sebastian Weyer*4.064*0.8271.0000.8520.4220.5410.42261Edward Lin*4.062*0.7430.7180.7490.6450.6250.58262Morten Arborg*4.056*0.7530.7710.7300.5110.6580.63263Jorge Castillo Matas*4.048*0.6280.6530.7070.5840.7130.76464Georgy Vershinin*4.019*0.6630.7420.7330.3970.7590.72565Yongting You (尤永庭)*4.017*0.6850.7810.7690.4120.7010.67066Jakub Kipa*4.007*0.7730.6410.6720.7480.6300.54367Paolo Moriello*4.004*0.7710.7320.6810.4470.6930.68068Simon Stannek*4.002*0.5760.6630.6830.4200.7710.88969Daniel Wannamaker*4.001*0.6800.6530.7140.6610.6130.68070Yinghao Wang (王鹰豪)*3.988*0.6990.6960.6240.8330.5860.54971Wojciech Knott*3.983*0.6480.7620.7100.4780.7120.67372Antonie Paterakis*3.981*0.7160.6550.6370.7770.5990.59973Samantha Raskind*3.981*0.6280.6810.7550.4860.6860.74474Abdelhak Kaddour*3.978*0.6010.7610.7460.4310.7200.72075Kuo-Hau Wu (吳國豪)*3.972*0.5970.6410.7660.3780.8510.73976Michael Gottlieb*3.970*0.6110.6570.7410.4170.7260.81877Qingbin Chen (陈庆斌)*3.968*0.5830.6410.7040.6930.6180.72878Yueh-Lin Tsai (蔡岳霖)*3.967*0.6490.6450.8430.3150.7560.75979Nathan Soria*3.967*0.6850.7390.6860.6180.6520.58880Kam Chor Kin (甘楚健)*3.966*0.5470.5580.7480.3550.8230.93681Ivan Zabrodin*3.925*0.6040.6350.6620.5480.7320.74482Martin Kraut*3.923*0.7690.7330.7350.4480.6500.58783Daniel Sheppard*3.923*0.6240.6630.6640.6060.6770.68884Shane Grogan*3.922*0.6750.7220.6620.6400.6270.59785Breandan Vallance*3.917*0.7860.6760.8790.1350.7660.67586Zhiqing Shi (石志庆)*3.908*0.5640.5630.7310.2900.8300.92987Kevin Gerhardt*3.904*0.6940.6620.6600.7800.6140.49388Mitsuki Gunji (郡司光貴)*3.903*0.7120.7350.7460.3960.6920.62189Nathan Dwyer*3.897*0.6580.6650.6790.5570.6800.65890Tomoya Iida (飯田朋也)*3.897*0.7250.7180.8300.0000.8010.82391Blake Thompson*3.891*0.6970.6740.6560.5780.6730.61492Walker Welch*3.890*0.5830.6030.6950.5350.6970.77793Jonah Crosby*3.887*0.7640.6460.6420.5330.6330.66894Shenchuan Mao (毛神川)*3.875*0.5230.5640.6810.4160.7540.93795Cezary Rokita*3.875*0.5970.5390.6920.3530.7870.90796Ping-Yueh Huang (黃品越)*3.870*0.6690.6640.7150.5840.5010.73697Massimiliano Iovane*3.868*0.7230.7050.7250.4760.7820.45798Florian Harrer*3.858*0.6210.5610.7140.3700.7550.83699Matic Omulec*3.853*0.6070.6190.7080.4760.7110.731100Pavel Yushkevich*3.849*0.6860.6210.6220.5060.6930.721






Spoiler: Side puzzles




*Rank**Name**Score**minx**pyram**sq1**clock**skewb*1Jayden McNeill*3.513*0.6210.7070.6340.5501.0002Rami Sbahi*3.280*0.6570.6810.5460.5080.8883Evan Liu*3.269*0.5830.5240.4721.0000.6904Daniel Wallin*3.263*0.4790.5410.5290.9050.8075Robert Yau*3.248*0.7800.6480.6540.5810.5856Yu Nakajima (中島悠)*3.222*0.7420.5800.6450.7150.5397Louis Cormier*3.191*0.9370.6500.4630.6480.4928Antoine Cantin*3.189*0.6480.6430.4710.7440.6839Vincent Hartanto Utomo*3.172*0.6370.8150.5280.6560.53510Daniel Sheppard*3.123*0.5890.5940.5440.8200.57511Feliks Zemdegs*3.010*0.8410.5650.5500.5030.55112Lucas Wesche*2.956*0.8370.4210.6540.5130.53113Wojciech Knott*2.945*0.5700.5460.4100.9050.51414Nathaniel Berg*2.939*0.4590.5800.2980.9570.64615Nathan Dwyer*2.929*0.5980.4610.6930.6610.51616Jan Bentlage*2.905*0.6010.5280.5150.5980.66217Ainesh Sevellaraja*2.901*0.5910.6880.5350.6610.42618Simon Westlund*2.867*0.8920.5510.4850.6160.32319Mitchell Lane*2.845*0.6140.5550.4230.5840.66820John Brechon*2.843*0.6870.6040.5080.5810.46321Ivan Zabrodin*2.817*0.5560.4430.3530.7650.70022Lee Chiang (蔣礪)*2.759*0.5030.6260.2620.7030.66523Maarten Smit*2.740*0.6030.5240.5200.8200.27324Walker Welch*2.738*0.5360.4770.4000.7170.60825Chris Wall*2.735*0.8030.3800.3880.5880.57626Dmitry Kryuzban*2.731*0.6150.6900.5170.6900.22027Chia-Liang Tai (戴嘉良)*2.724*0.3420.6740.5130.6640.53228Dan Cohen*2.710*0.5850.5240.7020.6210.27929Oscar Roth Andersen*2.705*0.8480.8650.3070.1390.54530Albin Xhemajlaj*2.701*0.4140.8080.2950.5730.61131Rok Glinšek*2.684*0.4780.4270.4820.5150.78332Bence Barát*2.618*0.6300.4380.6290.4390.48133Jules Desjardin*2.608*0.3810.9550.5160.4310.32534Brian Johnson*2.599*0.4630.5480.5590.4750.55435Joshua Feran*2.585*0.4970.5250.2100.7960.55736Emanuel Rheinert*2.568*0.3950.4400.8480.4270.45837Justin Thomas*2.566*0.6330.4150.6100.5850.32438Wilhelm Kilders*2.562*0.4420.4120.5000.6090.59839Kim Jokinen*2.551*0.4420.6510.2840.6600.51340AJ Blair*2.551*0.5560.3820.5500.6370.42641Sébastien Auroux*2.550*0.3690.5570.3670.7670.49042Carlos Méndez García-Barroso*2.533*0.5040.5260.4510.6820.37043Paweł Kowol*2.523*0.3980.6260.5370.5380.42544Jorge Castillo Matas*2.516*0.5840.5580.4180.4870.46945Niko Ronkainen*2.503*0.3500.4820.2350.8200.61546Anthony Lafourcade*2.501*0.2900.6630.1720.5780.79747Michael Young*2.480*0.4970.3800.6870.4070.50948Corey Sakowski*2.467*0.5020.5410.4210.3760.62649Erik Akkersdijk*2.451*0.6670.5310.3860.5490.31850Zijia Feng (冯子甲)*2.444*0.3030.3730.5390.8140.41651Michał Halczuk*2.443*0.5900.2640.7690.4540.36652Tim Major*2.441*0.3630.6840.3260.3890.67853Wataru Hashimura (端村航)*2.435*0.5910.4080.2920.6230.52154SeungBeom Cho (조승범)*2.432*0.5740.6480.4880.0000.72355Matic Omulec*2.431*0.8500.4800.4540.3550.29156Hunor Bózsing*2.427*0.5170.4160.3710.4360.68757Ryan Jones*2.425*0.4550.4270.2850.7800.47858Callum Hales-Jepp*2.422*0.6420.3930.3530.6790.35659Kevin Costello III*2.420*0.6740.4480.3240.4380.53660Alexandra Daryl Ariawan*2.402*0.4320.4090.4950.5220.54461Kit Clement*2.399*0.3130.3680.3200.8230.57562Rui-Jun Liu (劉睿鈞)*2.391*0.4100.4670.7590.3600.39463Viktor Ejlertsson*2.385*0.4990.4370.2790.5760.59464Christopher Olson*2.380*0.4230.4290.3720.6160.53965Yinghao Wang (王鹰豪)*2.375*0.4770.4920.2800.6370.48966Henri Gerber*2.364*0.7210.4590.2720.3510.56167Michael Röhrer*2.359*0.6420.3360.5470.5760.25968Jong-Ho Jeong (정종호)*2.357*0.7890.4420.4430.2290.45369Mason Langenderfer*2.355*0.3770.4190.2980.5020.76070Dmitry Aniskin*2.351*0.6430.4020.3230.5060.47871Henry Savich*2.329*0.3780.7730.2710.4720.43572Daniel Gracia Ortiz*2.320*0.4280.4720.5440.3540.52273Michael Gottlieb*2.316*0.5740.3790.4380.5400.38674Shao-Heng Hung (洪紹恆)*2.306*0.3600.4650.3060.6800.49575Mats Valk*2.305*0.4680.4470.5680.4350.38776Logan McGraw*2.299*0.3870.2760.2670.7650.60377Drew Brads*2.298*0.4351.0000.2090.0000.65478Daniel Cano Salgado*2.287*0.5730.3990.3070.6370.37179Marcin Jakubowski*2.282*0.3600.4440.2410.7690.46780Nikita Loyko*2.280*0.3680.4240.3210.7350.43281Thomas Schmidt*2.266*0.3400.6050.2290.4780.61482Yu Sajima (佐島優)*2.263*0.5140.4270.2560.7890.27883Laura Ohrndorf*2.247*0.3180.4310.3630.7760.35984Mattia Furlan*2.247*0.5730.4670.0000.7080.49885Jure Gregorc*2.246*0.6030.3810.2690.5670.42686Sungho Hong (홍성호)*2.245*0.4450.3470.3900.7110.35387Filip Pasławski*2.238*0.4490.6370.2290.1960.72888Alex Thielemier*2.236*0.3120.6810.4400.5770.22689Chunyu Zhang 2 (张春雨)*2.229*0.4360.5360.4960.3610.39990Artem Melikian (Артем Мелікян)*2.220*0.4690.5240.2480.3510.62991Andy Denney*2.213*0.5610.3600.3120.3660.61592Mharr Justhinne Ampong*2.211*0.6050.5580.2630.1640.62193Hippolyte Moreau*2.207*0.5630.4550.2360.5260.42894Brandon Lin*2.203*0.4520.3240.7960.3600.27195Luke Hubbard*2.203*0.3190.6750.3690.5400.30096Brady Metherall*2.201*0.4220.3910.6670.4260.29597Nathan Azaria*2.198*0.2850.3070.5700.7710.26498Blake Thompson*2.196*0.4240.4290.4510.3790.51399Xiao Hu (胡霄)*2.184*0.4310.3770.4010.4420.533100Evan Brown*2.183*0.4120.3280.3610.7520.330






Spoiler: Oceania (using Oceania Records)




*Rank**Name**Score**333**444**555**222**333oh**333ft**minx**pyram**sq1**clock**skewb**666**777**333bf**333fm**444bf**555bf**333mbf*1Feliks Zemdegs*14.424*1.0001.0001.0000.9941.0000.0001.0000.7990.8670.7370.5511.0001.0000.7701.0001.0000.4430.2622Jayden McNeill*11.737*0.7520.7360.7181.0000.8620.6950.7391.0001.0000.8061.0000.7630.6640.1100.8930.0000.0000.0003Tim Major*7.268*0.5350.4720.4430.5340.5810.0000.4320.9680.5140.5700.6780.3570.0000.2920.8930.0000.0000.0004Zane Carney*6.659*0.5190.4420.3720.2850.4890.0000.3630.3670.0000.0000.0000.0000.0001.0000.0000.8231.0001.0005Brock Hamann*6.607*0.5980.5730.6060.6730.5330.0000.3590.3570.0000.7420.0000.6480.6440.3120.0000.2710.0000.2926Richie Lim*6.344*0.5560.5790.4900.4110.5450.0000.8180.7080.6430.0000.2820.4220.0000.1750.7140.0000.0000.0007Dene Beardsley*5.997*0.4870.5050.6350.0000.4810.7760.4420.4640.7590.0000.0000.6790.6630.1070.0000.0000.0000.0008Nick Pappas*5.922*0.5630.4610.5740.4840.2800.0000.4640.6800.5360.0000.0000.5410.4410.3030.5560.0000.0000.0409Bryson Azzopardi*5.709*0.4600.5130.5630.4230.4360.0000.4670.3920.4120.4720.1560.5660.5820.1650.0000.0000.0000.10310Jason Kilbourn*5.551*0.4960.4610.4710.3850.4360.0000.4870.8500.4130.0000.1620.4330.0000.3330.6250.0000.0000.00011Cameron Stollery*5.333*0.6220.4240.4120.8990.5470.0000.0000.5920.1900.3460.2200.3460.0000.0000.7350.0000.0000.00012Hugo Adams*5.269*0.4890.4710.3610.7310.4900.0000.0000.9710.5770.3040.4050.0000.0000.0000.4720.0000.0000.00013Alex Asbery*5.186*0.5040.5470.4940.3960.4780.0000.3650.2280.4220.0000.1120.5270.4560.0000.6580.0000.0000.00014Ben Adcock*5.133*0.4910.4440.4440.3230.4301.0000.4270.3640.2180.0000.1970.4330.3630.0000.0000.0000.0000.00015Christian Foyle*4.971*0.5480.5170.5010.5230.5180.0000.4170.3760.5680.0000.0000.4440.3870.1720.0000.0000.0000.00016David Lim*4.590*0.4650.3960.4810.3260.4820.0000.3040.5390.4250.3180.0000.4270.4270.0000.0000.0000.0000.00017Monty Wain*4.510*0.5020.4520.4920.4210.3060.0000.5360.3850.0000.0000.2440.0000.5110.2060.4550.0000.0000.00018Kirt Protacio*4.425*0.7200.6270.4780.6130.6160.0000.0000.4360.0000.0000.2640.0000.0000.0760.5950.0000.0000.00019Aneurin Hunt*4.299*0.3910.3890.4160.4720.3680.0000.3820.4780.3310.0000.2670.3960.4100.0000.0000.0000.0000.00020Jack O'Mahony*4.210*0.5410.4740.4180.4480.4880.0000.0000.2980.8290.0000.1590.0000.0000.0000.5560.0000.0000.00021Alex Chen*4.144*0.5850.5450.4820.4380.6730.0000.0000.5110.0000.0000.4520.4580.0000.0000.0000.0000.0000.00022Angus Hannelly*4.057*0.5750.5040.5170.5280.4020.0000.3480.4840.2170.0000.4820.0000.0000.0000.0000.0000.0000.00023Tom Nelson*3.967*0.4010.3120.0000.3830.2160.0000.0000.3280.2840.0000.1200.0000.0000.4130.0000.4450.3490.71524Aron Puddy-Mathew*3.910*0.4970.3290.3920.3130.3960.0000.0000.1930.0000.6280.0000.0000.0000.8720.0000.0000.0000.29125Jonathan Adlam*3.710*0.5210.4510.4470.0000.4300.0000.3700.2330.0000.0000.3050.3570.0000.0000.5950.0000.0000.00026Luke Bruce*3.661*0.5510.4970.5810.3990.4230.0000.0000.2780.0000.0000.0000.5110.4220.0000.0000.0000.0000.00027Dhanasit Srijamorn*3.599*0.4460.4760.5620.3070.4310.0000.0000.2670.0000.0000.0000.5790.5330.0000.0000.0000.0000.00028Joshua Evely*3.596*0.4020.4400.5170.2770.2810.0000.2690.3460.0000.0000.0000.5270.5370.0000.0000.0000.0000.00029Ramesh Vidyasagar*3.567*0.4870.4830.3750.5100.3840.0000.4230.4590.0000.0000.0000.4460.0000.0000.0000.0000.0000.00030Rhys Campbell*3.539*0.4690.4780.3990.4430.3650.0000.0000.3720.0000.0000.5340.4780.0000.0000.0000.0000.0000.00031Chris Wilkinson*3.539*0.5530.3810.4650.3770.5670.0000.0000.3490.0000.0000.0000.0000.0000.1270.6760.0000.0000.04332Jeremy Lu*3.516*0.3380.3140.3130.2780.3220.0000.0000.4370.2550.5250.0000.3700.0000.0000.3620.0000.0000.00033Tomas Macadam*3.508*0.3940.3470.0000.4110.0000.0000.0000.2300.2931.0000.1880.0000.0000.1640.4810.0000.0000.00034Nathan Seeto*3.451*0.3960.4390.4090.3380.3390.0000.0000.3530.0000.0000.2270.0000.0000.2930.5210.0000.0000.13635Lorandt Mozsa*3.440*0.5320.5110.3400.4700.6470.0000.0000.4390.0000.0000.2520.0000.0000.2500.0000.0000.0000.00036Christian Houghton*3.440*0.4250.4350.3490.4390.3780.0000.5550.3720.2130.0000.0000.2750.0000.0000.0000.0000.0000.00037Chris Chan*3.355*0.4970.5800.6380.0000.4970.0000.0000.0000.0000.0000.0000.6160.5270.0000.0000.0000.0000.00038William Tao*3.246*0.5670.5200.4440.4630.5160.0000.0000.4680.0000.0000.2700.0000.0000.0000.0000.0000.0000.00039Bradley Speed*3.122*0.5030.4200.0000.5140.4790.0000.0000.3960.0000.0000.2780.0000.0000.0000.5320.0000.0000.00040Matthew Wanstall*3.105*0.5150.3610.3890.3330.3960.0000.0000.2510.4730.0000.0000.0000.0000.3030.0000.0000.0000.08441Joshua Li*3.041*0.4190.3640.4360.3110.4930.0000.3760.0000.4670.0000.0000.0000.0000.1730.0000.0000.0000.00042Jarvis H'Jinn*2.812*0.5790.3670.3490.3560.3710.0000.0000.2740.0000.0000.0520.0000.0000.0000.4630.0000.0000.00043Anthony Shao*2.787*0.3340.3230.3790.2860.2290.0000.2100.2340.0000.0000.0000.3950.3970.0000.0000.0000.0000.00044Cameron Hobbs*2.756*0.3820.2850.0000.3160.3470.0000.0000.4250.2900.7110.0000.0000.0000.0000.0000.0000.0000.00045Tommy Kiprillis*2.681*0.5220.2970.0000.4990.2780.0000.0000.4870.0000.0000.5980.0000.0000.0000.0000.0000.0000.00046Jun Sasagawa (笹川純)*2.679*0.5150.4470.0000.4170.3770.0000.0000.3610.0000.0000.5630.0000.0000.0000.0000.0000.0000.00047Kevin Tam*2.678*0.3610.4060.0000.3270.3810.0000.0000.0000.3980.0000.0000.0000.0000.0000.8060.0000.0000.00048Louis McDonald*2.646*0.3530.3130.3020.3120.2500.0000.4800.2880.0000.0000.0000.0000.2830.0640.0000.0000.0000.00049Zhaohan Xiong*2.522*0.5600.3370.0000.6170.6470.0000.0000.0000.3600.0000.0000.0000.0000.0000.0000.0000.0000.00050Ethan Pride*2.498*0.3040.3290.0000.3830.2140.0000.0000.4280.0000.0000.3930.0000.0000.0000.4460.0000.0000.00051Filbert Sim*2.450*0.4160.3530.0000.2950.3600.0000.0000.2980.0000.0000.2290.0000.0000.0000.5000.0000.0000.00052Robert Hickingbotham*2.444*0.4040.3660.4240.2680.3940.0000.0000.4490.0000.0000.0000.0000.0000.1390.0000.0000.0000.00053Edbert Sim*2.406*0.3100.3500.0000.3980.0000.0000.0000.4910.0000.4730.3240.0000.0000.0610.0000.0000.0000.00054Victor Kuo*2.393*0.5660.4680.3990.3400.4550.0000.0000.0000.0000.0000.0000.0000.0000.1640.0000.0000.0000.00055Jake Holah*2.392*0.4540.3360.0000.3660.3660.0000.0000.1970.0000.0000.0000.0000.0000.0990.5320.0000.0000.04256Malcolm Granville*2.386*0.3470.0000.1380.3160.4270.0000.0000.1820.0000.0000.0000.0000.0000.3350.6410.0000.0000.00057David Yan*2.354*0.4010.3410.3240.3020.4110.0000.0000.2860.2880.0000.0000.0000.0000.0000.0000.0000.0000.00058Jayden Johnson*2.349*0.4030.3670.0000.3640.3320.0000.0000.2880.0000.0000.1550.4400.0000.0000.0000.0000.0000.00059Anthony Huynh*2.260*0.3320.2760.0000.2820.3660.0000.0000.4090.0000.4600.0000.0000.0000.1350.0000.0000.0000.00060Coen Johnson*2.172*0.4120.3260.0000.3790.3910.0000.0000.4760.0000.0000.1890.0000.0000.0000.0000.0000.0000.00061Angelu Cayanan*2.167*0.4850.2710.0000.4140.3980.0000.0000.2560.0000.3430.0000.0000.0000.0000.0000.0000.0000.00062Luke Burong*2.140*0.4290.4130.0000.2510.2290.0000.0000.2220.0000.2770.3200.0000.0000.0000.0000.0000.0000.00063Joshua Walker*2.117*0.4770.3540.0000.4750.3240.0000.0000.2740.0000.0000.2120.0000.0000.0000.0000.0000.0000.00064Matt Jones*2.065*0.4570.3240.0000.3490.4510.0000.0000.2810.0000.0000.0000.0000.0000.2030.0000.0000.0000.00065Rhys Lloyd*2.051*0.4350.3760.0000.3120.4340.0000.0000.2900.0000.0000.2040.0000.0000.0000.0000.0000.0000.00066Ben Woo*2.042*0.5070.0000.0000.3120.5330.0000.0000.0000.0000.0000.0000.0000.0000.1090.5810.0000.0000.00067Mitchell Chiew*2.020*0.3300.2580.3380.2980.1370.0000.0000.0000.0000.0000.0000.3230.3360.0000.0000.0000.0000.00068Michael Taran*1.966*0.3290.3160.3090.2610.1890.0000.0000.2300.0000.0000.0000.3330.0000.0000.0000.0000.0000.00069Vinzbrylle Navales*1.883*0.5090.4290.0000.3910.2870.0000.0000.2670.0000.0000.0000.0000.0000.0000.0000.0000.0000.00070Garry Vong*1.878*0.4770.3360.0000.3860.4280.0000.0000.2510.0000.0000.0000.0000.0000.0000.0000.0000.0000.00071Martin Henry*1.867*0.4120.3540.2790.3270.0000.0000.0000.3270.0000.0000.1670.0000.0000.0000.0000.0000.0000.00072Sam Paul*1.794*0.3590.2690.0000.3570.2080.0000.0000.2920.3100.0000.0000.0000.0000.0000.0000.0000.0000.00073Joshua Brungar*1.781*0.2910.2600.2160.1820.2450.0000.0000.0000.0000.5870.0000.0000.0000.0000.0000.0000.0000.00074Alastair Whitely*1.765*0.2980.0000.0000.3890.2880.0000.0000.5890.2010.0000.0000.0000.0000.0000.0000.0000.0000.00075Jack Gerring*1.753*0.3640.2710.0000.2800.2420.0000.0000.3840.0000.0000.1180.0000.0000.0930.0000.0000.0000.00076Matthew Flay*1.747*0.2780.2010.0000.0000.2870.0000.0000.5340.0000.0000.0000.0000.0000.0000.4460.0000.0000.00077Ben Soh*1.728*0.4410.0000.0000.6610.3560.0000.0000.2710.0000.0000.0000.0000.0000.0000.0000.0000.0000.00078Mossimo Ebeling*1.727*0.2640.2600.3240.2670.2340.0000.0000.2240.1540.0000.0000.0000.0000.0000.0000.0000.0000.00079Aaron Cuta*1.710*0.4410.4530.0000.3720.4430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.00080Alex Tedeschi*1.704*0.3170.2830.0000.3730.2500.0000.0000.3120.0000.0000.1690.0000.0000.0000.0000.0000.0000.00081Joel Ernest*1.689*0.3830.2550.0000.3160.2090.0000.0000.3260.0000.0000.0000.0000.0000.2000.0000.0000.0000.00082Jerry Jiang*1.678*0.3530.3140.3080.3480.3550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.00083Ming Kim Low*1.665*0.3280.3540.0000.3200.2270.0000.0000.2780.0000.0000.1570.0000.0000.0000.0000.0000.0000.00084Duy Khuu*1.661*0.6040.3060.0000.4010.3500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.00085Ben Fiala*1.657*0.3800.3530.0000.3200.0000.0000.0000.3330.0000.0000.2720.0000.0000.0000.0000.0000.0000.00086Max Malouf*1.648*0.3790.0000.0000.2930.2810.0000.0000.3700.0000.0000.3250.0000.0000.0000.0000.0000.0000.00087Wesley Lee*1.646*0.4640.4980.0000.3130.3030.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.00088Eddy Shao*1.624*0.3410.0000.0000.2680.3350.0000.5020.1770.0000.0000.0000.0000.0000.0000.0000.0000.0000.00089Sugun Wadhwa*1.606*0.4170.2880.0000.4270.1880.0000.0000.2870.0000.0000.0000.0000.0000.0000.0000.0000.0000.00090Sam Chaplin*1.544*0.3850.0000.0000.4310.0000.0000.0000.3790.0000.0000.3480.0000.0000.0000.0000.0000.0000.00091Chloe Vo*1.542*0.3570.3820.0000.3240.3320.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0000.00092Luke Mayn*1.539*0.3590.2180.2450.2570.2520.0000.0000.2080.0000.0000.0000.0000.0000.0000.0000.0000.0000.00093Bryce Hayes*1.535*0.3500.0000.0000.2740.2820.0000.0000.3180.0000.0000.3100.0000.0000.0000.0000.0000.0000.00094Domantas Kuzinkovas*1.524*0.3790.4120.0000.3650.0000.0000.0000.0000.0000.0000.3680.0000.0000.0000.0000.0000.0000.00095Jack Cai*1.481*0.3120.2500.0000.3040.3140.0000.0000.3000.0000.0000.0000.0000.0000.0000.0000.0000.0000.00096Hugh McGlone*1.463*0.3440.2760.0000.3070.2770.0000.0000.2590.0000.0000.0000.0000.0000.0000.0000.0000.0000.00097Karel Doorman*1.463*0.3270.2940.0000.2460.2600.0000.0000.2520.0840.0000.0000.0000.0000.0000.0000.0000.0000.00098Alan Ma*1.451*0.3940.2960.0000.3030.4590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.00099Lachlan Ho*1.435*0.2760.0000.0000.4040.0000.0000.0000.4440.0000.0000.3110.0000.0000.0000.0000.0000.0000.000100Emmanuel Ramli*1.428*0.3190.2350.2130.3100.0000.0000.0000.3520.0000.0000.0000.0000.0000.0000.0000.0000.0000.000


----------



## LucidCuber (May 27, 2015)

Could you please do a UK one with UK rankings?


----------



## Genius4Jesus (May 27, 2015)

Request: Rankings for all continents based off CRs.


----------



## PenguinsDontFly (May 27, 2015)

Lol good idea!
2x2: 0.34
3x3: 0.59
4x4: 0.32


----------



## CyanSandwich (May 28, 2015)

70th for BLD and 23rd for Oceania. I'm content with that, but should be improving both on June 21st.

Thanks Daniel.


----------



## Kit Clement (May 28, 2015)

Tim Major said:


> -Whilst there are "so many more noobs" in 3x3, there are also so many more fast solvers in 3x3. With not enough depth in 5bld, again, I chose 1% not say... 5th so that the % would factor in the total number of competitors in 5bld.
> -The problem with the outlier WRs in my opinion is the misrepresentation of what's good/bad in the event. For example, in your top 100, Tim Wong is the only person who got over .5 in multi bld, with 0.512. It makes it seem that multi is one of his worst events, but it is no doubt his best (5th in the world, NAR holder, 2nd at WC2013...). Your scoring lists 3x3 as his best when it obviously isn't. Using the 1% method, he'd get 10.57/10.21=1.035 for 3x3 and 22.05/17.01=1.30.
> Now it shows a more accurate representation of his skill in each event against the general population, instead of against an outlier WR. I'm not saying this way is necessarily better than your method, and I don't think it would shuffle the order of the list at all(?), but it would allow more accurate comparisons of someone's skill in each event.
> -This calculation would not really be much more complex. Just where your file searches for the WR in each event, it needs to search for the 1% average.



My problem with using the percentile as a baseline is that it does get away from the main purpose of the ranks, which is to determine an all-around skill. Maskow is about twice as good as Tim at MBLD, so I feel that should be reflected in the rankings. If you want to compare relative strengths of events within a competitor, you could just compute the percentile of your rankings.

Of course, using a percentile would still preserve the fact that Maskow is 2x better than Tim at MBLD, but you're putting a higher weight on events that have outlier WRs. Not going to do the actual calculation, but I'm guessing Maskow would have a score around 2 for MBLD, which would lose Daniel's goal of making each event as important. 

Now that I think about it, I guess you could argue that MBLD is less significant relative to other events for anyone not named Maskow, as the numbers are so deflated. So I guess I'm not sure which one I like better now...


----------



## Tim Major (May 28, 2015)

Kit Clement said:


> Of course, using a percentile would still preserve the fact that Maskow is 2x better than Tim at MBLD, but you're putting a higher weight on events that have outlier WRs. Not going to do the actual calculation, but I'm guessing Maskow would have a score around 2 for MBLD, which would lose Daniel's goal of making each event as important.



I somehow completely missed this point, and it does make my suggestion a little unviable. Still be interesting to see but I don't think it's a perfect solution anymore.

Thanks for generating the Oceanic one Daniel


----------



## kinch2002 (May 28, 2015)

Tim Major said:


> -Whilst there are "so many more noobs" in 3x3, there are also so many more fast solvers in 3x3. With not enough depth in 5bld, again, I chose 1% not say... 5th so that the % would factor in the total number of competitors in 5bld.


The number of noobs outweighs the increase in number of fast solvers for 3x3 unfortunately. I found that out when I made percentile ranks a while back. It was ludicrously easy to be in the top 1% or 10% in 3x3 compared to, say bigbld.


Tim Major said:


> -The problem with the outlier WRs in my opinion is the misrepresentation of what's good/bad in the event. For example, in your top 100, Tim Wong is the only person who got over .5 in multi bld, with 0.512. It makes it seem that multi is one of his worst events, but it is no doubt his best (5th in the world, NAR holder, 2nd at WC2013...). Your scoring lists 3x3 as his best when it obviously isn't. Using the 1% method, he'd get 10.57/10.21=1.035 for 3x3 and 22.05/17.01=1.30.
> Now it shows a more accurate representation of his skill in each event against the general population, instead of against an outlier WR. I'm not saying this way is necessarily better than your method, and I don't think it would shuffle the order of the list at all(?), but it would allow more accurate comparisons of someone's skill in each event.


 I would actually argue that Tim is indeed better at 3x3 than multi. The reason you say that multi is no doubt his best is because he has high rankings, but that's because of the lack of depth in the event . I also have multi as one of my worst and I totally agree with that. I spent far less time on multi/3bld practise than e.g. 3x3 practise. These rankings are supposed to be an objective view of how good you are at events, without dependency on anyone else. Unfortunately we need some sort of benchmark, so we use the WR, which is dependent on other people, but hopefully does not contain much bias between events.


----------



## DuffyEdge (May 28, 2015)

kinch2002 said:


> Spoiler: Side puzzles
> 
> 
> 
> ...


Perhaps I miscalculated, but how come I’m not 83rd for side puzzles…?


Spoiler



 42.89/117.75 + 2.56/6.53 + 10.21/33.15 + 5.94/8.64 + 3.10/6.17 = 2.254


----------



## ottozing (May 28, 2015)

kinch2002 said:


> Spoiler: Side puzzles
> 
> 
> 
> ...



Yay me. Really makes me want to fix my Clock and Mega averages though lol.


----------



## Mollerz (May 28, 2015)

DuffyEdge said:


> Perhaps I miscalculated, but how come I’m not 83rd for side puzzles…?
> 
> 
> Spoiler
> ...



Pre-WGC export


----------



## Stefan (May 29, 2015)

I like it. Very straight-forward and non-arbitrary, just like sum of ranks, and does have those nice advantages.


----------



## kinch2002 (May 29, 2015)

Made an update
All
Top300


----------



## Lid (Jun 3, 2015)

I have now added "kinch ranks" to my stats page (see link in signature), top1000 + a top100 for females.


----------



## kinch2002 (Jun 6, 2015)

*An Analysis using KinchRanks*

RobertY came up with the idea of just looking at your best X events using KinchRanks, to see what happens as X varies. For example, for X=1, you would just look at your best score of any of the 18 events. For X=2, you sum your best event and your 2nd best event. For X=18, it's just normal KinchRanks.

I've plotted graphs just for the top 10 people of normal KinchRanks as they become too cluttered with more lines.

Kind of hard to see much on here, but basically we know that Feliks is a long way ahead most of the way, no matter how many events we include. Note the flat line between 17-18 events for Feliks (no feet score). If we plotted everyone on this graph, we'd see that Kevin Hays and Oliver Frost both equal him for X=1 and X=2 (they all have WRs in 2 events).






In order to flatten out the above graph so we can zoom in closer, I've looked at how close each person is to the best score for each X.
- Feliks leads for every X so he's always at 100%.
- The general shape of everyone else's line is a dip down to start, then coming back up at the end. This means that over the first few events, Felik's has really high scores compared to everyone else. Once X hits about 7, everyone starts to catch up with him, but he holds them off. That indicates that e.g. most people have a better 8th-best event than Feliks - he's not THAT strong at quite a few events.
- Looking at Yu's line - he doesn't start off very well - that shows he has no crazy strong events. However, he gains a massive amount over everyone else as the number of events increases - he is very good at almost every event. Similar story with my line. The lines that have the best gradient at the end show a lack of weak events.
- Robert Yau and Louis Cormier are 3rd and 4th respectively when their best 14 events are included. However, their weakness in bld is exposed here as their line drops off as their worst events start to be included.





Although the above graph shows most of the interesting things, I also did a plot of where each person would be ranked if only your best X events counts.
If you look at the points I made from the previous graphs, you can spot almost all of them here too.


----------



## LucidCuber (Jun 7, 2015)

Could you do a UK Kinchranks?


----------



## Coolster01 (Jun 13, 2015)

kinch2002 said:


> Spoiler: Side puzzles
> 
> 
> 
> ...



After my last comp: 2.56/3.28 = 0.780 for pyraminx, 42.89 / 64.63 = .664 for mega, 5.94 / 11.28 = .527 for clock now. 
.78+.664+.527+.546+.888 = 3.405 now. Yay, I'm getting closer to Jay.


----------



## Antonie faz fan (Jun 13, 2015)

wuuut i am not even top 300 P


----------



## kinch2002 (Jun 14, 2015)

Rob and I briefly discussed changing the magnitude of the scoring system. Describing your scores as 0.xxx is a bit weird and cumbersome.
How about if we multiply everything by 100? It seems more obvious what it all means, and everyone will start speaking to the same level of accuracy (i.e. 2 sig figs) 
The final all-round score would be an average of all the scores (sum them and divide by 18). This gives it a more comprehensible score than the current way.

Instead of
Feliks has a score of 12.2 (out of possible 18), with a score of 0.86 in 5x5, 0.21 in multibld and 1 in 3x3
It would be
Feliks has a score of 67, with a score of 86 in 5x5, 21 in multi, and 100 in 3x3.
I think the latter may be better. Any thoughts?






Further thoughts on the system generally:
- Over time, the depth in events will increase, and I think this will result in higher Sum of Ranks scores in the future. However, I think the best KinchRanks scores should not get worse, as they do not depend on the depth of the event. This is a positive imo.
- In KinchRanks, a second gained when you're near the WR is worth a lot, while a second gained when you're not near the WR is not worth so much.
In Sum of Ranks it's generally the opposite way round. You gain a lot from improving your not-so-good events a tiny bit and aren't rewarded for the super difficult improvements at the top end of the event.
Again, a big positive for KinchRanks imo.


----------



## cashis (Jun 14, 2015)

Nice.


----------



## Kit Clement (Jun 15, 2015)

kinch2002 said:


> - In KinchRanks, a second gained when you're near the WR is worth a lot, while a second gained when you're not near the WR is not worth so much.
> In Sum of Ranks it's generally the opposite way round. You gain a lot from improving your not-so-good events a tiny bit and aren't rewarded for the super difficult improvements at the top end of the event.
> Again, a big positive for KinchRanks imo.



I was just thinking about this myself -- and while it's great for those among the top, it makes it very difficult to distinguish middle-of-the-pack cubers, especially for events like 2x2 where the WR time is so small. Maybe some form of log transformation would be interesting to apply.


----------



## not_kevin (Jun 17, 2015)

I really like this system - many props for formalizing your thoughts like this, Daniel!



kinch2002 said:


> Rob and I briefly discussed changing the magnitude of the scoring system. Describing your scores as 0.xxx is a bit weird and cumbersome.
> How about if we multiply everything by 100? It seems more obvious what it all means, and everyone will start speaking to the same level of accuracy (i.e. 2 sig figs)
> The final all-round score would be an average of all the scores (sum them and divide by 18). This gives it a more comprehensible score than the current way.
> 
> ...



I also like this modification, because as you said, it's easier to visualise the output. In particular, I like the averaging of the overall score, because it's easier to compare across some of the other modifications proposed so far (such as the ones limiting events (side events only, BLD only, etc.)).


----------



## kinch2002 (Jun 18, 2015)

Kit Clement said:


> I was just thinking about this myself -- and while it's great for those among the top, it makes it very difficult to distinguish middle-of-the-pack cubers, especially for events like 2x2 where the WR time is so small. Maybe some form of log transformation would be interesting to apply.


I don't see why 2x2 would be any different to other events. In fact, it should appear to separate people who are close in shorter events more than in longer events.
The system reflects the fact that it's easier to improve from e.g. 8.1 to 8.0 at 2x2 than it is to go from 2.1 to 2.0. That's why the top people appear to have larger separation - their skill difference is much bigger imo. It's perfectly natural for there to be a spacing out at the top of any ranking (cubing or not).
The logarithm, while interesting perhaps, would go against the basic idea of the system. Not many people can interpret logarithmic scores - pretty sure we all work best in linear.
Sorry, that was the same thing explained many times badly


----------



## kinch2002 (Jun 18, 2015)

not_kevin said:


> I also like this modification, because as you said, it's easier to visualise the output. In particular, I like the averaging of the overall score, because it's easier to compare across some of the other modifications proposed so far (such as the ones limiting events (side events only, BLD only, etc.)).


Thanks - you make a good point about subset rankings.

I thought of a downside - it's not as easy to figure out how your overall score will change when you improve at something.
But I still think I'll change it anyway.

ANOTHER IDEA:
I mentioned that KinchRanks uses FM single and 3bld Single because overall they reflect abilities better than their respective Means still. However, I think we can improve on this by using the better score of a person's Single and Mean. Given that in the ideal world we'd use Means for these events, I think a good Mean should be rewarded with the option to use it, as it is a truer reflection of ability.
In the future I'd like to fully shift to Means only, but that is still over a year away imo.


----------



## JustinTimeCuber (Jun 18, 2015)

Someone should make a program for this so you can tell what your score is without waiting for it to load (it can take FOREVER)


----------



## Keroma12 (Jun 19, 2015)

I like this. Is it possible for this to be on the WCA page along with sum of ranks? If not, it would be really nice if it were hosted on the web somewhere. I would also like to be able to see ranks within each country (with respect to NRs instead of WRs).

Edit: http://hem.bredband.net/_zlv_/rubiks/stats/kinch_ranks.html (thanks Kit) (still waiting on by country though!)


----------



## Kit Clement (Jun 19, 2015)

kinch2002 said:


> I don't see why 2x2 would be any different to other events. In fact, it should appear to separate people who are close in shorter events more than in longer events.
> The system reflects the fact that it's easier to improve from e.g. 8.1 to 8.0 at 2x2 than it is to go from 2.1 to 2.0. That's why the top people appear to have larger separation - their skill difference is much bigger imo. It's perfectly natural for there to be a spacing out at the top of any ranking (cubing or not).
> The logarithm, while interesting perhaps, would go against the basic idea of the system. Not many people can interpret logarithmic scores - pretty sure we all work best in linear.
> Sorry, that was the same thing explained many times badly



Consider this simple example -- to get a 50% score in 2x2, you need to have a 3.20 average. That takes quite a bit of skill, and likely takes knowing some EG, at the very least CLL. To get the same score in 5x5, you need to have a 1:48.40 average. That's a considerably easier level to attain -- and it's just because the numerator for 2x2 is so small. It's even sillier for FMC -- getting that score takes a solve of 40, or a mean of 50, which someone can do having never tried an FMC attempt, assuming basic 3x3 knowledge.

I do agree though -- It is rather desirable that the score function for an event is convex, but I'm just arguing that it may be a bit _too_ convex. I do see the appeal for ease of interpretability though, and I'm starting to see how applying logs is just getting arbitrary.


----------



## tseitsei (Jun 19, 2015)

kinch2002 said:


> ANOTHER IDEA:
> I mentioned that KinchRanks uses FM single and 3bld Single because overall they reflect abilities better than their respective Means still. However, I think we can improve on this by using the better score of a person's Single and Mean. Given that in the ideal world we'd use Means for these events, I think a good Mean should be rewarded with the option to use it, as it is a truer reflection of ability.
> In the future I'd like to fully shift to Means only, but that is still over a year away imo.



3bld mean really just means how safely you want to play it... if I get "good enough" single on first solve then I will rush the remaining two attempts probably leading to DNF. But if I get reasonably bad times on 1st and 2nd attempts I'll probably just attempt to get asome kind of a good time without rushing it on the final attempt. After all winner is decided by the single and not mo3.. 

So mo3 3bld doesnt reqlly represent skill. So pls don't use that in your ranks...


----------



## not_kevin (Jun 19, 2015)

tseitsei said:


> 3bld mean really just means how safely you want to play it... if I get "good enough" single on first solve then I will rush the remaining two attempts probably leading to DNF. But if I get reasonably bad times on 1st and 2nd attempts I'll probably just attempt to get asome kind of a good time without rushing it on the final attempt. After all winner is decided by the single and not mo3..
> 
> So mo3 3bld doesnt reqlly represent skill. So pls don't use that in your ranks...



Yeah, I kinda agree with this - in particular, as long as competitions continue to use bo3 instead of mo3 for BLD, I don't think we should switch to using it, because the current times aren't optimized for it.

I think that we can eventually switch to FMC means, 'tho, since it's getting increasingly popular to hold mo3 competitions rather than best-of competitions.


----------



## Genius4Jesus (Jun 19, 2015)

Keroma12 said:


> Edit: http://hem.bredband.net/_zlv_/rubiks/stats/kinch_ranks.html (thanks Kit) (still waiting on by country though!)



Cool site. I'm 181 for Kinch-rankings as opposed to 92 for normal sum of single ranks.

Also, could we have average Kinch-rankings?


----------



## Tim Major (Jun 19, 2015)

KinchRank 261 vs 181 and 251 for single and average ranks.

Whose rank (within the top 300, or whatever...) increased the most? Decreased the most?


----------



## obelisk477 (Jun 19, 2015)

Sorry if I missed this, but why wouldn't you divide everyone's total score by the first place holder's total score (and then multiply by 100, if you wanted it to look nicer)? So its more obvious where you are and who is at the top. So Feliks would score 100, Yu Nakajima 99.7, etc.

EDIT: I of course mean this for the sum of the kinch ranks only, and not the individual events themselves


----------



## Tim Major (Jun 19, 2015)

obelisk477 said:


> Sorry if I missed this, but why wouldn't you divide everyone's total score by the first place holder's total score (and then multiply by 100, if you wanted it to look nicer)? So its more obvious where you are and who is at the top. So Feliks would score 100, Yu Nakajima 99.7, etc.



This should tighten up the graph a lot, so if you've missed something I've missed something too


----------



## kinch2002 (Jun 19, 2015)

tseitsei said:


> 3bld mean really just means how safely you want to play it... if I get "good enough" single on first solve then I will rush the remaining two attempts probably leading to DNF. But if I get reasonably bad times on 1st and 2nd attempts I'll probably just attempt to get asome kind of a good time without rushing it on the final attempt. After all winner is decided by the single and not mo3..
> 
> So mo3 3bld doesnt reqlly represent skill. So pls don't use that in your ranks...





not_kevin said:


> Yeah, I kinda agree with this - in particular, as long as competitions continue to use bo3 instead of mo3 for BLD, I don't think we should switch to using it, because the current times aren't optimized for it.
> 
> I think that we can eventually switch to FMC means, 'tho, since it's getting increasingly popular to hold mo3 competitions rather than best-of competitions.


Thanks for the input. I've changed it for now so that you use your average if it gives you a better score than your single - I think that's still fair right? Will help those who haven't had a lucky single yet but have demonstrated their skill with an average.



Genius4Jesus said:


> Cool site. I'm 181 for Kinch-rankings as opposed to 92 for normal sum of single ranks.
> Also, could we have average Kinch-rankings?


KinchRanks uses mostly averages already, apart from 4bld, 5bld, multibld. 3bld and 3fm uses whichever is better for you.



obelisk477 said:


> Sorry if I missed this, but why wouldn't you divide everyone's total score by the first place holder's total score (and then multiply by 100, if you wanted it to look nicer)? So its more obvious where you are and who is at the top. So Feliks would score 100, Yu Nakajima 99.7, etc.
> 
> EDIT: I of course mean this for the sum of the kinch ranks only, and not the individual events themselves


The reasons not to do this are
- The final score would no longer be an objective measure of how good you are overall
- Your final score would depend on the top person's overall score, which means that you can't track your own improvement properly.


----------



## not_kevin (Jun 19, 2015)

Thanks for the update on the main post - cool to see how the countries compare to each other. All the more confirmation that Poland is a huge cubing powerhouse - even the huge populations and competitive depth of the United States and China can't overcome Polish talent 

Mostly as a personal request (getting something like this set up would take quite a few more resources, I'd imagine), but I'd love to see how people compare within their regions (like how sum of ranks is set up on the WCA site). We now know how strong the regions are compared to each other - but how strong am I within my region? I can calculate my own regional KinchRanks without too much difficulty (not including the 3bld and 3fm mean change, my score is 45.38 in NA), but how does that rank against the rest of NA?


----------



## Iggy (Jun 19, 2015)

Wat I'm 13th now, sub Jay


----------



## ryanj92 (Jun 19, 2015)

Tim Major said:


> KinchRank 261 vs 181 and 251 for single and average ranks.
> 
> Whose rank (within the top 300, or whatever...) increased the most? Decreased the most?


I'm not in the top 300 reguarly, and I'm 224th in KinchRanks


----------



## kinch2002 (Jun 19, 2015)

Kit Clement said:


> Consider this simple example -- to get a 50% score in 2x2, you need to have a 3.20 average. That takes quite a bit of skill, and likely takes knowing some EG, at the very least CLL. To get the same score in 5x5, you need to have a 1:48.40 average. That's a considerably easier level to attain -- and it's just because the numerator for 2x2 is so small. It's even sillier for FMC -- getting that score takes a solve of 40, or a mean of 50, which someone can do having never tried an FMC attempt, assuming basic 3x3 knowledge.
> 
> I do agree though -- It is rather desirable that the score function for an event is convex, but I'm just arguing that it may be a bit _too_ convex. I do see the appeal for ease of interpretability though, and I'm starting to see how applying logs is just getting arbitrary.


Ah thanks, I understand now I guess. I actually wouldn't agree that 3.20 even requires CLL. Good ortega scrambles could achieve that. I think the effect is not so much that 2x2 inherently has a small numerator, but rather that the best averages are likely to be lucky and better than e.g. the general home average of theirs.
Fewest moves is a different kettle of fish, because it is not a speedsolving event and I think we're quite lucky that the spread of results is anywhere near the spread of speedsolving events.
I don't think there's any obvious or neat way to "fix" these cases, and I really think that the bias in them is tiny compared to the bias removed compared to e.g. sum of ranks by the whole system generally. So I'm not really bothered by it, although it's nice to have it at the back of the mind.



not_kevin said:


> Thanks for the update on the main post - cool to see how the countries compare to each other. All the more confirmation that Poland is a huge cubing powerhouse - even the huge populations and competitive depth of the United States and China can't overcome Polish talent
> 
> Mostly as a personal request (getting something like this set up would take quite a few more resources, I'd imagine), but I'd love to see how people compare within their regions (like how sum of ranks is set up on the WCA site). We now know how strong the regions are compared to each other - but how strong am I within my region? I can calculate my own regional KinchRanks without too much difficulty (not including the 3bld and 3fm mean change, my score is 45.38 in NA), but how does that rank against the rest of NA?



Yes, indeed the Polish bld skills really come to light once you treat them as equal events to all others!

Yep, regional KinchRanks is relatively easy to calculate - I already did UK although I can't remember whether I ever posted it. Hopefully I'll get round to generating a few countries and continents soon. I'd love to get something set up like the WCA Sum of Ranks page, although I don't have the knowledge to do that.



Keroma12 said:


> I like this. Is it possible for this to be on the WCA page along with sum of ranks? If not, it would be really nice if it were hosted on the web somewhere. I would also like to be able to see ranks within each country (with respect to NRs instead of WRs).
> 
> Edit: http://hem.bredband.net/_zlv_/rubiks/stats/kinch_ranks.html (thanks Kit) (still waiting on by country though!)


WCA page is an eventual consideration. I didn't suggest it immediately as I wanted to continue discussing it for some time first to make sure it's right. Indeed this thread has helped a lot, so thanks everyone for the feedback.

As mentioned above, I can calculate ranks within countries one at a time.

On the topic of regional KinchRanks: I actually don't like them too much, because you end up comparing against NRs, which are much more volatile and also prone to biases within countries. It's a nice rank for sure, but it's not quite as objective as comparing to WRs. Ideally I wouldn't use WRs at all in the calculation, but it is the concession I have to make to provide a benchmark that is as close to equal from event to event as possible. So using CRs and NRs is a further step away from the theoretical perfect benchmark, but still provides nice rankings.


----------



## not_kevin (Jun 19, 2015)

kinch2002 said:


> Yes, indeed the Polish bld skills really come to light once you treat them as equal events to all others!



Related to this, I'm somewhat curious what events are most strongly correlated to each other - as in, if I'm good at [some event], what events am I most likely to also be good in, or what events am I most likely to do poorly in? For example, it seems that a good 333mbf score translates almost always to great 333bf/444bf/555bf scores, and that 666 and 777 are very similar, but what would feet and Square-1 correlate with?



kinch2002 said:


> On the topic of regional KinchRanks: I actually don't like them too much, because you end up comparing against NRs, which are much more volatile and also prone to biases within countries. It's a nice rank for sure, but it's not quite as objective as comparing to WRs. Ideally I wouldn't use WRs at all in the calculation, but it is the concession I have to make to provide a benchmark that is as close to equal from event to event as possible. So using CRs and NRs is a further step away from the theoretical perfect benchmark, but still provides nice rankings.



I do agree with the negatives with the volatility, and the lack of objective comparison to the "ideal cuber", but the main reason that I'm interested in regional KinchRanks is precisely because of the biases within countries - for example, because Southern California (at least in the past ~7 years) has been rather weak at BLD, there's not much reason for competitions to hold BLD - which, of course, creates a negative feedback loop. Meanwhile, OH is pretty heavily emphasized here, which means that even for someone who doesn't consider themselves very good at OH, the nature of the scene means that one tends to actually be fairly good globally (an easy example here is myself - even when I got sub-20, I didn't consider myself very good because 19 averages rarely podium, but sub-20 is actually pretty good - a KinchRank of >54, and a rank of <577 out of ~9500). Although my example is of a region we probably wouldn't calculate for, it hopefully gives some idea as to why I'm interested in the statistics, even though they deviate somewhat from that comparison with the "ideal" in the normal KinchRanks.


----------



## kinch2002 (Jun 19, 2015)

I've massively overhauled the OP, and made a website to host the info and latest rankings on.



not_kevin said:


> Related to this, I'm somewhat curious what events are most strongly correlated to each other - as in, if I'm good at [some event], what events am I most likely to also be good in, or what events am I most likely to do poorly in? For example, it seems that a good 333mbf score translates almost always to great 333bf/444bf/555bf scores, and that 666 and 777 are very similar, but what would feet and Square-1 correlate with?


I may have a look into that sometime - sounds interesting. Or else you could do some correlation analysis yourself 



not_kevin said:


> I do agree with the negatives with the volatility, and the lack of objective comparison to the "ideal cuber", but the main reason that I'm interested in regional KinchRanks is precisely because of the biases within countries - for example, because Southern California (at least in the past ~7 years) has been rather weak at BLD, there's not much reason for competitions to hold BLD - which, of course, creates a negative feedback loop. Meanwhile, OH is pretty heavily emphasized here, which means that even for someone who doesn't consider themselves very good at OH, the nature of the scene means that one tends to actually be fairly good globally (an easy example here is myself - even when I got sub-20, I didn't consider myself very good because 19 averages rarely podium, but sub-20 is actually pretty good - a KinchRank of >54, and a rank of <577 out of ~9500). Although my example is of a region we probably wouldn't calculate for, it hopefully gives some idea as to why I'm interested in the statistics, even though they deviate somewhat from that comparison with the "ideal" in the normal KinchRanks.


That does make it sound interesting. I've done UK and US ranks on my website, and I'll add some more soon.


----------



## Coolster01 (Jun 21, 2015)

Can we have an update with this? I noticed my 2x2 rankings aren't updated.

EDIT: Yeah, as a note: For sum of ranks, my 2x2 barely changes (2 --> 1 does basically nothing), but for this it makes me got from 94.7 --> 100, which is pretty big. I like this; it motivates you to still keep getting faster at events you're already good at as much as get faster at events you're bad at as well, unlike sum of ranks, which heavily relies on getting fast at events you're bad at.


----------



## Robert-Y (Jun 21, 2015)

Don't forget the other effect: You push everyone else's 2x2x2 score down too.


----------



## kinch2002 (Jun 22, 2015)

Coolster01 said:


> Can we have an update with this? I noticed my 2x2 rankings aren't updated


Not sure that the latest public export has your results in. I'm going to aim to update once a week - sometime Weds-Fri so that previous weekend's results are in.


----------



## not_kevin (Jun 25, 2015)

Random anecdotal info for advocating KinchRanks (albeit only on a regional scale): as of this post, my NA ranks in 3x3 are exactly the same as my NA ranks in feet for sum of ranks, in both single and average - although I've never competed in feet 

https://www.worldcubeassociation.or..._ranks/?regionId=_North+America&single=Single
https://www.worldcubeassociation.or...anks/?regionId=_North+America&average=Average


----------



## abunickabhi (Jun 25, 2015)

Only BLD ranking would be great yo


----------



## Sidster (Jun 25, 2015)

Will continental KinchRanks be a thing in the future?


----------



## the super cuber (Jun 25, 2015)

i ranked 75th  (compared to 98th for world sum of ranks)


----------



## kinch2002 (Jun 29, 2015)

Sidster said:


> Will continental KinchRanks be a thing in the future?


Yes, maybe. I'd have to actually put a bit of effort into that though, as an extra link would be needed from Person to Country and then to Continent as well.


----------



## AlphaSheep (Jun 30, 2015)

I wrote a short python script last night to calculate continent KinchRanks. The script is here. I tested it by generating the KinchRanks for Africa. The results are here.

I've also added all 6 continents now.

Edit1: Except MBLD results seem to be problematic...  Getting scores over 100 for some reason...
Edit2: The error with MBLD scores is fixed now.


----------



## not_kevin (Jul 5, 2015)

Having just gotten a single in feet, but failing to make the cutoff (and therefore no mean), I'd like to see some kind of single weight involved. That being said, I understand that since the purpose of this is to compare against the "ideal" cuber (and of course the ideal cuber would always make cutoffs and never deal with this issue), the clear solution is just to git gud, but it's still a bit of a feelbad to not have any improvement in an event, despite making "improvements" in sum of ranks.


----------



## spyr0th3dr4g0n (Jul 7, 2015)

Hey, if you get a chance will you update the kinch rank for countries? I am pretty interested to see how things changed after the comp.


----------



## JustinTimeCuber (Jul 8, 2015)

On your website, the ranks only go up to 1000. There should be a "show more" or "show all" option that shows more ranks.


----------



## Coolster01 (Jul 14, 2015)

Update again pls.


----------



## cashis (Jul 14, 2015)

so how long until the wca realizes this is better than sum of ranks and implements into their website?


----------



## kinch2002 (Aug 6, 2015)

I've done an update to all the lists on my website. I've been away in Brazil, and then decided to wait until after US Nats for the latest update. Anyway, gives a nice chance to see some significant movement.


----------



## henrysavich (Aug 6, 2015)

The change column is change in kinchranks since last update?

If that is the case than wow, everybody else needs to get on my level!


----------



## not_kevin (Aug 20, 2015)

Not sure where best to put this, but I've been idly thinking about the "most balanced" cuber for a while, based on some comment that someone made about their KinchRanks standard deviation (alas, I can't seem to find it now T__T). But now that I wanted to calculate this, I'm having trouble justifying exactly what it should look like.

So, the naive approach is to simply take the standard deviation of the per-event values - but then cubers who only do a couple events and are somewhat slow are greatly advantaged here. So I thought maybe using a metric like "standard deviation of all non-0 values", but it still greatly rewards slower cubers over faster ones (which isn't bad in and of itself, I guess, but I'd prefer rewarding doing more events more). I thought about standard deviation / overall KinchRank, which would help that, but there's the issue that competitors who do few events are rewarded. I also thought of things like total range of event KinchRanks over overall Kinchrank, but it still runs into similar problems, and also isolates out the best and worst (eg, a competitor who has event KRs of 40 for all but two events, which are at 0 and 100, will have a score of 100; meanwhile, a competitor who evenly has KRs ranging from 0 to 80 would be "more well rounded" by this metric).

Anyone have any thoughts for a good statistic for measuring both well-roundedness and speed?


----------



## not_kevin (Sep 9, 2015)

Another KinchRanks-related thought, based on measuring the relative strengths of each country: the country rank on Daniel's site (https://sites.google.com/site/danielsheppardcuber/home/KinchRanksCountries) could suggest that Poland produces the best all-round cubers, but this ends up being rather false - instead, it produces the best _specialized_ cubers (what, with Marcin Kowalczyk contributing two 100% values despite having a personal KinchRank of 24.52 [coming in a 878th]). To answer the question of which region produces the best all-round cubers, perhaps we could take the average KinchRank of some top section of each country (either a flat number, like the top 10, or a percentage, like the top 1%), and compare those? Just curious what countries would stand on top with such a metric.


----------



## Isaac Lai (Sep 9, 2015)

not_kevin said:


> Another KinchRanks-related thought, based on measuring the relative strengths of each country: the country rank on Daniel's site (https://sites.google.com/site/danielsheppardcuber/home/KinchRanksCountries) could suggest that Poland produces the best all-round cubers, but this ends up being rather false - instead, it produces the best _specialized_ cubers (what, with Marcin Kowalczyk contributing two 100% values despite having a personal KinchRank of 24.52 [coming in a 878th]). To answer the question of which region produces the best all-round cubers, perhaps we could take the average KinchRank of some top section of each country (either a flat number, like the top 10, or a percentage, like the top 1%), and compare those? Just curious what countries would stand on top with such a metric.



The only reason why Poland is so good and other countries are dragged down is because of multi-BLD.


----------



## not_kevin (Sep 9, 2015)

Isaac Lai said:


> The only reason why Poland is so good and other countries are dragged down is because of multi-BLD.



Not the only reason - they actually are fairly good at all events (although I definitely agree that mbld is by far the largest factor). Consider that their worst event KinchRank is 75.61 (in 2x2), for example. But more telling towards my point, their OH, Feet, Clock, Skewb, 6x6, 7x7, 3bf, _and_ 5bf are all above 90 (along with mbld, of course), but of those 8 events, 7 different people hold titles (the only repeat being 6x6 and 7x7 by Michał Halczuk). This suggests that the region doesn't actually have very many true all-rounders (or, maybe, no all-rounders can hold a NR compared to the specialists in the region).

As an easy comparison, see that Australia is basically dominated by Feliks, with Jay taking most of the other events except some BLD events (which Zane has) and Clock (which Tomas Macadam has).


----------



## kinch2002 (Oct 3, 2015)

http://www.kinch2002.com/kinchranks/
Updated for all those September comps you went to and did good stuff at


----------



## yoinneroid (Oct 4, 2015)

kinch2002 said:


> http://www.kinch2002.com/kinchranks/
> Updated for all those September comps you went to and did good stuff at



I don't do enough good stuff to say at top 5


----------



## kinch2002 (Oct 4, 2015)

Just made a load of improvements to the pages. You can now see all the columns  All the names and countries link to the relevant WCA profiles as well.


----------



## Stefan (Nov 4, 2015)

kinch2002 said:


> KINCHRANKS WEBSITE


Can you make that link go directly to the KinchRanks page? The page it does point to only has them hidden in a sub-menu.


----------



## kinch2002 (Nov 4, 2015)

Stefan said:


> Can you make that link go directly to the KinchRanks page? The page it does point to only has them hidden in a sub-menu.



Thanks for pointing that out, edited it now


----------



## Hssandwich (Nov 4, 2015)

Were there ever single kinchranks?


----------



## JustinTimeCuber (Nov 4, 2015)

Hssandwich said:


> Were there ever single kinchranks?



I think it's supposed to be just an overall thing so no


----------



## NevinsCPH (Nov 4, 2015)

Not sure if this is in google doc function. Forgive me for being lazy to look it up. I think it will be easier to view the KinchRanks if you can freeze the top row and the name column?


----------



## Theo Leinad (Apr 19, 2019)

Bump!!
Now that means will be recognized for big blinds and there's kinda a new structure, will these be taken into account? 
If so, is the code prepared for this change?


----------



## CarterK (Apr 19, 2019)

Theo Leinad said:


> Bump!!
> Now that means will be recognized for big blinds and there's kinda a new structure, will these be taken into account?
> If so, is the code prepared for this change?


Code probably isn't prepared, but it'll probably act like it does for fmc and 3bld where it's whatever is better between your single and mean.


----------



## highnickk (Aug 27, 2020)

I really want to see my sum of ranks or Kinchrank, but all the tools are either just the top 100/1000, or there are thousands of pages.


----------



## Spacey10 (Aug 27, 2020)

nicholas the cuber said:


> I really want to see my sum of ranks or Kinchrank, but all the tools are either just the top 100/1000, or there are thousands of pages.


What the heck is that?


----------



## CrispyCubing (Aug 27, 2020)

nicholas the cuber said:


> I really want to see my sum of ranks or Kinchrank, but all the tools are either just the top 100/1000, or there are thousands of pages.


I’ve wondered about this too but I don’t know where you can find individual kinchrank by searching.


Spacey10 said:


> What the heck is that?


Kinchrank is a way to rank overall skill. I’m assuming this is the website @nicholas the cuber is referencing:https://wcadb.net/kinchranks.php

This is an explanation on how the system works.


Spoiler: Kinchrank












All-Round Rankings (KinchRanks)


I devised KinchRanks in May 2015 as a new system to rank all-round ability. Read all about it in the post below, or go to the website where I'm hosting the info and rankings, https://wca.cuber.pro/kinch/persons Homepage content:




www.speedsolving.com


----------



## Mike Hughey (Aug 27, 2020)

nicholas the cuber said:


> I really want to see my sum of ranks or Kinchrank, but all the tools are either just the top 100/1000, or there are thousands of pages.


Found you in the world rankings. It wasn't easy. 
(it's here for now, but of course it will move as other competitions are held)
Rank is tied for 58580, score is 2.36.


Kinch Rank - Persons


Nicholas Trofimov 2.36 21.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 18.66 0.00 0.00 0.00 0.00

The way I did it was to first calculate your kinch rank, then use the thousands of pages and binary search for that value.  It didn't really take that long, but it's not exactly easy.


----------

