Won't you want to do OH?Not for long
Well they said at one comp so i thought they meant that they got the average at their one and only comp so form london up until my next comp I think I would qualify for the criteriaWon't you want to do OH?
This guy
View attachment 7871
SELECT * FROM rankssingle WHERE personID IN(SELECT personID FROM ranksaverage WHERE eventID = '333' and best > 2999) AND eventID = '333fm' ORDER BY best ASC
19Least moves in FMC without a sub 30 avg?
That guy does have a sub-30 average in FMC...
I think you need to replace '333' by '333fm' in your query...
Edit: and I'm not sure if your query will include people that have no average in FMC at all, will it?
[td]Name | Comps | Average solves | Total solves | |
1 | Jesser Armando Ramírez Diaz | 2 | 103.5000 | 207 |
2 | Henri Gerber | 17 | 100.7059 | 1712 |
3 | Pavel Galaktionov | 23 | 95.1739 | 2189 |
4 | Robert Yau | 57 | 93.8947 | 5352 |
5 | Kevin Gerhardt | 21 | 93.2857 | 1959 |
6 | Bence Barát | 108 | 92.7593 | 10018 |
7 | Dorian Stein | 4 | 92.7500 | 371 |
8 | Callum Hales-Jepp | 43 | 92.6744 | 3985 |
9 | César Abraham Briones Arreola | 6 | 92.1667 | 553 |
10 | Stephano Saucedo Reyes | 13 | 91.0000 | 1183 |
11 | Vladislav Kaminskiy | 23 | 90.3913 | 2079 |
12 | Lucas Wesche | 25 | 90.3600 | 2259 |
13 | Vladyslav Zhydkov (????????? ??????) | 4 | 90.2500 | 361 |
14 | Oliver Fritz | 17 | 89.7059 | 1525 |
15 | Ivan Zabrodin | 45 | 89.0000 | 4005 |
16 | Alexis Rodrigo Cazu Mendoza | 12 | 88.4167 | 1061 |
17 | Jure Gregorc | 26 | 88.1154 | 2291 |
18 | Simon Westlund | 34 | 87.8235 | 2986 |
19 | Fabio Schwandt | 16 | 87.4375 | 1399 |
20 | Shivam Bansal | 38 | 87.1053 | 3310 |
21 | Pavel Yushkevich | 23 | 86.3913 | 1987 |
22 | Matic Omulec | 40 | 86.2250 | 3449 |
23 | Rok Glinek | 14 | 86.2143 | 1207 |
24 | Alexey Modenov | 1 | 86.0000 | 86 |
25 | Hunor Bózsing | 62 | 85.8226 | 5321 |
26 | Vladislav Ushakov | 12 | 85.7500 | 1029 |
27 | Francisco Alberto Castañeda Lima | 9 | 85.5556 | 770 |
28 | Sta Zupanc | 14 | 85.5000 | 1197 |
29 | Victor Rafael Ortiz Villaseñor | 9 | 85.3333 | 768 |
30 | Artem Yashkov | 14 | 85.2857 | 1194 |
31 | Carlos Macias Valadez | 6 | 85.1667 | 511 |
32 | Aldo José Gramajo de León | 6 | 85.0000 | 510 |
33 | Jakob Gunnarsson | 10 | 84.6000 | 846 |
34 | Nikita Bespalov | 4 | 84.5000 | 338 |
35 | Alexey Zharikov | 10 | 84.4000 | 844 |
36 | Kari Hyttinen | 7 | 84.2857 | 590 |
37 | Ben Whitmore | 26 | 84.1154 | 2187 |
38 | Antonio López | 12 | 84.0833 | 1009 |
39 | Wilhelm Kilders | 43 | 83.9767 | 3611 |
40 | Oleg Martynov | 12 | 83.5000 | 1002 |
41 | Pablo Say | 17 | 83.2353 | 1415 |
42 | Ciarán Beahan | 22 | 83.0000 | 1826 |
43 | Jan Bentlage | 116 | 82.9310 | 9620 |
44 | Laura Holzhauer | 7 | 82.8571 | 580 |
45 | Nathan Liang | 1 | 82.0000 | 82 |
46 | Kunal Oak | 13 | 81.9231 | 1065 |
47 | Lubo Bartík | 13 | 81.7692 | 1063 |
48 | Alberto Pérez de Rada Fiol | 38 | 81.5789 | 3100 |
49 | Róbert Maróti | 14 | 81.5000 | 1141 |
50 | Mattia Furlan | 17 | 81.2353 | 1381 |
eventId | country | 1% perc. | 5% perc. | 1% top mean. | 5% top mean. | best.10.mean |
---|---|---|---|---|---|---|
333 | USA | 9.98 | 13.12 | 8.95 | 11.19 | 7.24 |
333 | Germany | 9.28 | 11.60 | 8.13 | 10.03 | 7.95 |
333 | Korea | 9.09 | 11.63 | 8.07 | 10.02 | 8.07 |
333 | China | 10.63 | 12.79 | 9.57 | 11.41 | 8.12 |
333 | Canada | 10.34 | 13.27 | 9.15 | 11.49 | 8.30 |
333 | Poland | 9.47 | 12.18 | 8.80 | 10.52 | 8.44 |
333 | Russia | 9.62 | 12.35 | 8.86 | 10.67 | 8.75 |
333 | Japan | 9.34 | 11.76 | 8.85 | 10.42 | 8.80 |
333 | Taiwan | 9.62 | 11.86 | 8.71 | 10.31 | 8.80 |
333 | France | 9.80 | 12.65 | 8.99 | 10.99 | 8.85 |
444 | USA | 35.46 | 44.43 | 32.89 | 39.11 | 30.46 |
444 | China | 36.50 | 42.45 | 33.13 | 38.33 | 32.16 |
444 | Germany | 33.34 | 40.92 | 30.69 | 35.87 | 33.18 |
444 | Japan | 33.26 | 40.03 | 31.79 | 35.28 | 34.17 |
444 | Korea | 34.65 | 42.17 | 30.17 | 36.68 | 34.32 |
444 | Taiwan | 33.79 | 38.74 | 30.86 | 35.36 | 34.39 |
444 | Canada | 33.56 | 44.16 | 30.56 | 37.37 | 34.72 |
444 | Poland | 34.61 | 41.36 | 32.47 | 37.85 | 35.10 |
444 | Indonesia | 37.16 | 45.41 | 32.55 | 39.63 | 35.52 |
444 | Russia | 35.42 | 42.93 | 34.42 | 38.07 | 36.42 |
555 | USA | 67.24 | 79.14 | 60.84 | 71.79 | 60.29 |
555 | Taiwan | 60.61 | 70.19 | 53.94 | 63.24 | 64.78 |
555 | China | 68.47 | 76.96 | 64.31 | 71.25 | 65.15 |
555 | Germany | 65.63 | 73.91 | 57.78 | 67.94 | 66.39 |
555 | Japan | 63.80 | 72.42 | 61.18 | 65.76 | 67.04 |
555 | Indonesia | 65.35 | 77.22 | 57.82 | 68.25 | 68.97 |
555 | Canada | 64.73 | 78.11 | 60.15 | 67.56 | 69.37 |
555 | Korea | 64.03 | 79.96 | 54.83 | 66.85 | 69.57 |
555 | Russia | 68.92 | 79.40 | 61.81 | 70.57 | 71.45 |
555 | France | 71.43 | 77.56 | 58.82 | 70.35 | 72.04 |
Does that include FMC only competitions?
you know why? he attended the only comp in 1982 and the next comp waas 2003\http://www.worldcubeassociation.org/results/p.php?i=1982THAI01
All of his solves were NAR and one of them WR for 21 years.
Just fun country rank stats:
x% perc.- x% percentile of average result per person per event. I.e if for country xyz event abc there is 100 persons, then 1% perc.= mean of 10th person in this country rank
x% mean.- mean of the top x% averages per country per event
best.10.mean- most imprtant i think- mean of 10 best averages per country per event
All stats from ranksAverage table
eventId country 1% perc. 5% perc. 1% top mean. 5% top mean. best.10.mean 333 Australia 1052 1253 910.36 1109.62 896.60 333 Germany 928 1160 817.75 1003.59 798.60 333 Netherlands 1007 1262 805.00 1076.16 968.20 333 Korea 909 1163 807.50 1001.80 807.50 333 USA 1001 1315 899.91 1124.60 729.60 333 Canada 1034 1327 914.92 1148.72 830.20 333 Poland 947 1218 879.95 1052.25 843.50 333 United Kingdom 962 1217 852.67 1047.47 896.10 333 France 980 1267 899.31 1103.07 885.70 333 Taiwan 962 1186 871.33 1031.13 880.40 444 USA 3554 4459 3280.68 3919.18 3048.00 444 Germany 3334 4092 3122.25 3605.25 3339.00 444 Australia 3360 4526 2923.00 3838.18 3770.30 444 Netherlands 3345 4573 2654.00 3825.43 4097.50 444 Korea 3465 4217 3016.67 3674.19 3442.90 444 Canada 3356 4416 3055.67 3736.87 3472.20 444 Spain 3880 4646 3271.00 3980.83 3773.80 444 Taiwan 3379 3874 3086.50 3535.86 3438.90 444 France 3656 4243 3282.67 3857.94 3696.30 444 China 3654 4280 3369.53 3848.58 3270.60 555 Australia 6892 8096 4932.00 6887.20 7728.90 555 USA 6730 7970 6116.00 7187.04 6055.20 555 Taiwan 6061 7019 5394.00 6324.00 6478.30 555 Germany 6563 7391 5778.00 6794.54 6639.00 555 Korea 6403 7996 5483.00 6684.75 6956.90 555 Netherlands 7009 8205 5579.00 6671.33 8338.10 555 United Kingdom 6251 7728 5756.00 6773.80 7390.40 555 Indonesia 6535 7722 5782.00 6825.44 6908.30 555 France 7143 7756 5882.00 7035.25 7204.40 555 Thailand 5939 7190 5939.00 5939.00 7915.90
Ooops sorry. I made mistake, 1. It was based on old dataset 2. I forgot to sort it before cutting to top10 by best.10.mean.Can someone explain me how this is sorted?
Singles btw.Lowest 5x5/4x4 ratio? 4x4/3x3?
--wcaID-- 444 333 ratio name, country
2015MALY01 1:22.48 56,81 1,45 Aleksandr Malygin, m, Russia
2015BANS04 47,56 28,22 1,69 Alankar Bansal, m, India
2010LIYA01 52,9 29,63 1,79 Yalong Li (李亚隆), m, China
2013KESH01 3:23.03 1:31.76 2,21 Ketan Keshri, m, India
2017SCHL02 1:19.94 35,26 2,27 Heinz Schlatter, m, Switzerland
2012MARQ02 1:36.14 41,68 2,31 Gabriel Campos Marques, m, Brazil
2011LIUA02 1:12.91 30,84 2,36 Yiqi Liu (刘奕祺), m, China
2012ZHUS01 1:17.43 32,07 2,41 Shengnan Zhu (祝胜男), f, China
2007XING01 3:05.30 1:16.09 2,44 Xinghui Zhao (赵星辉), m, China
2013KARI01 27,81 10,91 2,55 Jithin Prakash, m, India
--wcaID-- 555 444 ratio name, country
2011TELA01 3:13.71 9:09.13 0,35 Julio Martín Gómez Telésforo, m, Mexico
2015POMA01 2:28.72 5:16.73 0,47 Abhinav Pomalapally, m, USA
2012MAGA01 1:51.44 3:37.09 0,51 Kelvin Maganes, m, Philippines
2009SHIN02 1:49.96 3:06.36 0,59 Forte Shinko, m, Canada
2013PITE01 2:22.57 3:12.84 0,74 Przemysław Piątek, m, Poland
2010AILE01 4:42.15 5:45.25 0,82 Alexx Ailes, m, USA
2012XULI02 1:29.07 1:43.72 0,86 Linqi Xu (徐琳淇), m, China
2011YAHI01 2:28.34 2:45.80 0,89 Vahid Mohammad Yahia (وحید محمد یحیی), m, Iran
2009GOMB01 2:00.68 2:07.71 0,94 Daniel Gömböš, m, Slovenia
2015GROB02 2:20.69 2:28.15 0,95 Ruben Grobler, m, South Africa
2015RICH13 1:40.28 1:44.30 0,96 Benjamin Richler, m, Canada
2009MOTT01 2:19.71 2:20.18 0,997 Eduardo Lins Motta, m, Brazil
On a similar note, highest/lowest ratio between 3bld/4bld, 3bld/5bld, 4bld/5bld?Lowest 5x5/4x4 ratio? 4x4/3x3?