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Most events in a one-day competition? Has there ever been an FMC mean in one day?

Too lazy to check, but this one-day competition had 15 events: https://www.worldcubeassociation.org/results/c.php?i=Indiana2012
Also, besides the obvious FMC-dedicated competitions (Fantastic/Fabulous/Fantabulous competitions in USA, FMC USA/Europe), this competition was one day and had a mean: https://www.worldcubeassociation.org/results/c.php?i=DixonSpring2014

between rounds or between competitions?
what event?
averages or singles?

There's no need to be pedantic, this stat has been requested several times. It's a PB streak between competitions -- getting at least 1 PB in a competition keeps the streak alive.
 
Last edited:
Too lazy to check, but this one-day competition had 15 events: https://www.worldcubeassociation.org/results/c.php?i=Indiana2012
Also, besides the obvious FMC-dedicated competitions (Fantastic/Fabulous/Fantabulous competitions in USA, FMC USA/Europe), this competition was one day and had a mean: https://www.worldcubeassociation.org/results/c.php?i=DixonSpring2014



There's no need to be pedantic, this stat has been requested several times. It's a PB streak between competitions -- getting at least 1 PB in a competition keeps the streak alive.

oh well then I have that just in 3x3 lol
*but I've only been to four
 
I've seen this somewhere before, but I think I didn't search for the correct term, so I can't find it. Could someone do a fastest first official single and average for each event?
 
what about fastest 3x3 single compared to average? I have an 11.90 single and a 15.03 average but I think Kit has a sub 10 single and a low 14 average.

There's Pavan Ravindra's 5.58 single and 10.60 average.

edit: Went through the database and found the biggest difference between an average and best single in that average. Daniel Sheppard got an average of 1:52.86 with a 16.46 single. Full average was 18.43, 20.22, (16.46), (DNF), 4:59.94.

I thought that one would be exceptional and the rest would be lucky singles, but it seems like most of these are the same way--a mostly good average and then a DNF paired with a complete collapse on one solve that skews the whole average. Even after I filtered to only see sub-30 averages, it's mostly stuff like someone who's comfortably sub-20 or sub-15 getting a DNF then a 40.xx or something.

The biggest difference that I found that seems to be a lucky single is Slowest 3x3 single compared to average?[/QUOTE]

Scrolled up and realized I could answer this too! [url="https://www.worldcubeassociation.org/results/p.php?i=2010MICH03"]Michael recently got a (25.06), (25.03), 25.03, 25.05, 25.03.

Sorry for the double post. I tried to edit it into the end of my other post and it got weird and deleted a bunch of the middle of the post when I submitted.
 
Last edited by a moderator:
Sure he did, but I would not count it.

Why not? Did he do it intentionally?

edit: in retrospect, it does look like he cheesed it. If that is indeed what you meant, then I found Yang Liu 3, who got (20.68), 20.71, 20.71, (21.34), 20.68, which also seems cheesed to me. The next-closest is Muhammad Iril Khairul Anam, whose (29.71), 29.56, (29.46), 29.50, 29.50, also seems to be intentional. I'm looking through a lot of these and it's hard for me to tell if they got their numbers this close or are just really consistent. Here are the top twenty ranked by (average - best)/average:

WCA IDAverage - BestAverageRatio
2010MICH030.0125.040.000399361022364217
2011YANG170.0220.700.000966183574879227
2009ANAM010.0629.520.00203252032520325
2010MAUL030.0519.990.00250125062531266
2012ORTU010.0415.650.00255591054313099
2009ANAM010.0930.220.00297816015883521
2012LIUY030.0413.380.00298953662182362
2008MIZU010.0413.200.00303030303030303
2012SALA050.0413.150.00304182509505703
2011DIRK010.0515.050.00332225913621262
2007LUCH020.0514.710.00339904826648538
2008MEIS010.2365.640.00350396099939062
2010HANS010.1130.190.00364359059291156
2011RODR110.0615.640.00383631713554987
2009TANG050.0819.860.0040281973816717
2008PAWL010.0819.360.00413223140495868
2004GALL020.0716.280.0042997542997543
2009GENN010.1328.790.00451545675581799
2013TOMA010.0612.330.0048661800486618
2015VALD070.2856.560.00495049504950495
 
Last edited:
top 100 singles achieved during sup 15 averages?
sup 20?
sup 10?

>20s:
WCA IDSolveAverage
2013SEOH018.2121.93
2012LIIH018.6820.09
2013SEOH019.1421.93
2009PICH019.2120.25
2009ROTA019.5622.45
2012SLIZ019.9221.90
2009PICH019.9320.25
2013GUMP0110.0024.85
2007ESPI0110.0628.10
2012LIFL0110.2720.85
2006DZOA0310.3825.48
2013TORR0210.4322.27
2013SEOH0110.4321.93
2013KAPL0110.5920.43
2007COOP0110.6122.11
2013MIRA0210.6527.84
2007WEIN0110.6520.28
2012LIIH0110.6720.09
2010PATZ0110.6929.60
2010BADR0110.7222.06
2009PICH0110.8620.25
2015ARYA0210.8725.52
2012HWAN0110.8925.69
2012HWAN0110.9025.69
2013FERR0310.9421.78
2014CHIE0111.0121.94
2011ATAS0111.1823.34
2011ZAKA0111.1823.61
2010NOUV0111.2720.78
2007ESPI0111.3328.10
2007CHAN0611.3922.60
2012PARA0311.4023.07
2006DZOA0311.4325.48
2008LIUE0111.4421.35
2009CUST0111.5020.17
2010BADR0111.5822.06
2008DANG0111.5922.95
2012SOKO0111.5945.65
2014YUJA0111.6123.94
2012SOKO0111.6345.65
2009KOCH0211.6621.76
2015KENN0111.6724.18
2010BOON0111.6821.57
2010TANA0211.7520.47
2011BENZ0111.7522.43
2007SUGA0111.8026.45
2011BEAT0111.8220.08
2014PENA0711.8320.32
2010LIUD0211.8324.01
2008LINM0311.8420.91
2008TRAN0211.8520.01
2012BOGD0111.8633.27
2013ZORL0111.9120.77
2009JOHN0511.9622.77
2011BRUS0111.9620.60
2009GUIL0111.9621.01
2014MCGA0111.9726.28
2010CHEN2711.9823.20
2010PATZ0112.0029.60
2010BINL0112.0024.02
2013KOIL0112.0120.57
2010YUPH0112.0220.57
2010CARA0112.0820.02
2008YOUN0212.1126.59
2010YUPH0112.1420.90
2011DILA0112.1820.50
2011ATAS0112.1823.34
2007SUGA0112.2726.45
2013VOLO0112.2721.94
2011RETZ0112.2820.08
2010GIEN0112.3020.45
2009HUJI0112.3121.07
2013CONT0112.3121.01
2011SALG0112.3430.84
2013MOTT0112.3420.93
2014GONZ1812.3720.02
2007CERP0212.3861.39
2011SVEN0212.3925.64
2013VARG0212.4021.87
2009INGE0112.4120.72
2011SVEN0212.4325.64
2011JESU0112.4522.96
2007CHAN0612.4522.60
2015KIMG0112.4620.18
2010RUGA0112.4621.27
2013MIRA0212.5027.84
2014CHAY0112.5020.07
2014JAUR0112.5120.47
2009CHEN3512.5222.34
2014SZNE0112.5222.03
2011ESQU0112.5320.32
2014NURU0112.5320.82
2010YUPH0112.5521.05
2008LIUE0112.5825.69
2013BOGA0112.5820.50
2012CHEN2512.6021.39
2014SARK0112.6120.09
2013LING0312.6120.12
2007CERP0212.6320.80
2012HOFL0112.6320.07

>15s:
WCA IDSolveAverage
2013SCHM027.7918.57
2011MERL017.9617.01
2013SEOH018.2121.93
2009KORB028.4315.32
2006SHEU018.5517.11
2011SANJ018.5615.31
2009HILD018.6316.53
2007CLAE038.6317.30
2012LIIH018.6820.09
2013MACI018.7817.25
2011NATH028.8016.26
2011RECH018.9015.25
2007SHIR018.9115.79
2008JADE018.9315.18
2014YANG118.9615.88
2010STOL019.0219.88
2011ALFA029.0915.83
2007HESS019.1316.31
2013SEOH019.1421.93
2008LINH019.2115.25
2009PICH019.2120.25
2008LINH019.2219.55
2005AKKE019.2216.00
2008BRAN019.2815.62
2010DUON019.3116.99
2009NATE019.3416.07
2013ANDR049.3515.13
2012PERK019.3515.09
2013KHUU019.3615.52
2014WANG269.3717.36
2013KASH019.4019.54
2007THOM029.4016.64
2007VALK019.4115.61
2009OHRN019.4217.66
2007LING019.4315.05
2008CLEM019.4317.39
2009KIEL019.4417.86
2009GUGL019.4415.68
2013CHEU019.4515.87
2012MAAL019.4615.39
2010ANDE039.4616.01
2011SALG019.5319.39
2007PERG019.5316.01
2010GOLD019.5415.58
2012KIMD019.5516.14
2003BRUC019.5516.24
2008JINX019.5515.61
2008LOMB019.5615.53
2009ROTA019.5622.45
2003POCH019.5617.21
2007PERG019.5815.79
2010KOUG019.5915.43
2014CYBU029.6416.76
2011FOYL019.6516.16
2015MARC039.6615.04
2003BRUC019.6815.51
2011HAVL019.6915.71
2013VIDA019.7115.89
2009CHEN359.7115.10
2012CROS019.7216.99
2013OLIV019.7215.08
2008DIEW019.7215.06
2005AKKE019.7516.00
2008MORA029.7515.61
2009JAGI019.7716.85
2012MORA069.7816.24
2013CHIX019.7817.63
2008LINH019.7815.25
2009VERA019.7815.10
2008BERE019.8015.32
2007LUJI019.8015.22
2006GARR019.8115.19
2006ANDO019.8315.34
2008LIZH029.8416.84
2013HANN019.8617.44
2012HUAN129.8718.65
2009HAYA019.8815.91
2007GOUL019.8815.59
2003BRUC019.8815.82
2007LIME019.8815.10
2006TABU019.9015.86
2009LAIY019.9216.80
2012SLIZ019.9221.90
2003BRUC019.9315.43
2012DOED019.9315.10
2009PICH019.9320.25
2013LINK019.9416.01
2011EBEL019.9417.23
2012CHIN019.9416.33
2011BRZE019.9616.65
2011WANG529.9715.92
2011BANS029.9816.64
2012IVAN029.9815.02
2012KONG019.9817.23
2006AUST019.9915.18
2008LINH019.9915.25
2010MARI0210.0015.35
2011SZAT0110.0017.28
2013GUMP0110.0024.85
2010BECK0110.0215.05

>10s:
WCA IDSolveAverage
2013RAVI065.5810.60
2010WANG535.8110.49
2010BURN015.9310.53
2013NAHM016.1510.07
2010THOM036.2111.14
2010LANE026.2511.39
2010PROT016.2710.33
2012MAHV016.3210.07
2011HAMS016.5012.09
2009CHAR036.5010.69
2006TSAI036.5011.28
2008HASH026.5611.51
2007VALK016.6110.25
2010KIPA016.6110.34
2010YOUY016.6111.16
2011KNOT016.6413.19
2011BARB036.6410.92
2010PROT016.6512.27
2007VALL016.6510.36
2009MORI056.7710.04
2010MALL016.8010.50
2013WOJT026.8111.24
2012ROQU016.8210.18
2009FUSH016.8410.21
2009BOZS016.8410.12
2010ADHI016.8411.38
2012SZEW016.8713.45
2008CHUC026.8912.06
2011GROG026.8910.15
2011HOFF026.9010.78
2009YAUR016.9110.40
2012RUPE016.9110.37
2013ZHOU196.9211.67
2005BATI016.9310.49
2008SEAR016.9312.22
2007HESS016.9410.52
2012PONC027.0010.60
2014KARN027.0011.49
2007NAKA037.0010.27
2010WEYE027.0010.96
2011LIYI027.0211.31
2007HESS017.0212.04
2010WEYE027.0210.25
2006GUNJ017.0310.38
2013HUAN307.0413.18
2015CHEA017.0511.00
2007HESS017.0611.26
2009YAUR017.0610.12
2007VALL017.0810.43
2005AKKE017.0812.26
2013BAUT017.0914.06
2010WEYE027.1110.20
2012KALE017.1110.93
2008SEAR017.1310.66
2011ZALE027.1311.02
2014GERB017.1310.11
2011ZENG067.1411.36
2010CANT027.1610.15
2007NAKA037.1610.63
2009LIUE017.1611.81
2013BALI017.1711.14
2009JIAY017.1810.44
2013UVES017.1811.61
2012PONC027.1810.68
2009ZHAN537.1810.90
2010KIPA017.1910.35
2012KALE017.1910.46
2012LEWI017.1911.04
2010WANG537.2010.10
2012ROZA017.2112.19
2012WESC017.2110.77
2009BARB017.2110.06
2009KORB017.2511.41
2012IVAN037.2511.97
2008LINE027.2510.89
2009DIEC017.2510.08
2009BARB017.2510.37
2005AKKE017.2510.98
2007WOOR017.2710.81
2009BARB017.2810.35
2013LEJE037.2810.35
2009WHIT017.2810.46
2007COHE017.2813.67
2010SCHM017.2810.24
2011RHEI017.2810.14
2013JOKI017.2810.02
2012KALE017.2912.06
2012DYGI017.2910.59
2012CHOS017.3010.01
2012CHOS017.3010.74
2014HOUS017.3011.90
2010LEED027.3010.41
2007GUIM017.3011.29
2011FATE017.3011.20
2011ALVI017.3012.05
2009KORB017.3111.69
2011TORG017.3111.21
2010PROT017.3110.16
2013PARK037.3110.47
2008SEAR017.3110.42

R code:
Code:
raw_data = read.csv(file = "WCA_export_Results.tsv", head = TRUE, sep = "\t")
valid_333 = data.frame(raw_data$personId, raw_data$eventId, 
  raw_data$value1, raw_data$value2, raw_data$value3, raw_data$value4, raw_data$value5, raw_data$average)
names(valid_333) = c("person", "event", "s1", "s2", "s3", "s4", "s5", "average")
valid_333 = subset(valid_333, event == "333" & average > 0)

s1s = data.frame(valid_333$person, valid_333$s1, valid_333$average)
s2s = data.frame(valid_333$person, valid_333$s2, valid_333$average)
s3s = data.frame(valid_333$person, valid_333$s3, valid_333$average)
s4s = data.frame(valid_333$person, valid_333$s4, valid_333$average)
s5s = data.frame(valid_333$person, valid_333$s5, valid_333$average)
n = c("person", "solve", "average")
names(s1s) = n
names(s2s) = n
names(s3s) = n
names(s4s) = n
names(s5s) = n
all_valid_333_solves = rbind(s1s, s2s, s3s, s4s, s5s)
all_valid_333_solves = subset(all_valid_333_solves, solve > 0)

sup20s = subset(all_valid_333_solves, average > 2000)
sup20s = sup20s[order(sup20s$solve),][1:100,]

sup15s = subset(all_valid_333_solves, average > 1500)
sup15s = sup15s[order(sup15s$solve),][1:100,]

sup10s = subset(all_valid_333_solves, average > 1000)
sup10s = sup10s[order(sup10s$solve),][1:100,]
 
>20s:
WCA IDSolveAverage
2013SEOH018.2121.93
2012LIIH018.6820.09
2013SEOH019.1421.93
2009PICH019.2120.25
2009ROTA019.5622.45
2012SLIZ019.9221.90
2009PICH019.9320.25
2013GUMP0110.0024.85
2007ESPI0110.0628.10
2012LIFL0110.2720.85
2006DZOA0310.3825.48
2013TORR0210.4322.27
2013SEOH0110.4321.93
2013KAPL0110.5920.43
2007COOP0110.6122.11
2013MIRA0210.6527.84
2007WEIN0110.6520.28
2012LIIH0110.6720.09
2010PATZ0110.6929.60
2010BADR0110.7222.06
2009PICH0110.8620.25
2015ARYA0210.8725.52
2012HWAN0110.8925.69
2012HWAN0110.9025.69
2013FERR0310.9421.78
2014CHIE0111.0121.94
2011ATAS0111.1823.34
2011ZAKA0111.1823.61
2010NOUV0111.2720.78
2007ESPI0111.3328.10
2007CHAN0611.3922.60
2012PARA0311.4023.07
2006DZOA0311.4325.48
2008LIUE0111.4421.35
2009CUST0111.5020.17
2010BADR0111.5822.06
2008DANG0111.5922.95
2012SOKO0111.5945.65
2014YUJA0111.6123.94
2012SOKO0111.6345.65
2009KOCH0211.6621.76
2015KENN0111.6724.18
2010BOON0111.6821.57
2010TANA0211.7520.47
2011BENZ0111.7522.43
2007SUGA0111.8026.45
2011BEAT0111.8220.08
2014PENA0711.8320.32
2010LIUD0211.8324.01
2008LINM0311.8420.91
2008TRAN0211.8520.01
2012BOGD0111.8633.27
2013ZORL0111.9120.77
2009JOHN0511.9622.77
2011BRUS0111.9620.60
2009GUIL0111.9621.01
2014MCGA0111.9726.28
2010CHEN2711.9823.20
2010PATZ0112.0029.60
2010BINL0112.0024.02
2013KOIL0112.0120.57
2010YUPH0112.0220.57
2010CARA0112.0820.02
2008YOUN0212.1126.59
2010YUPH0112.1420.90
2011DILA0112.1820.50
2011ATAS0112.1823.34
2007SUGA0112.2726.45
2013VOLO0112.2721.94
2011RETZ0112.2820.08
2010GIEN0112.3020.45
2009HUJI0112.3121.07
2013CONT0112.3121.01
2011SALG0112.3430.84
2013MOTT0112.3420.93
2014GONZ1812.3720.02
2007CERP0212.3861.39
2011SVEN0212.3925.64
2013VARG0212.4021.87
2009INGE0112.4120.72
2011SVEN0212.4325.64
2011JESU0112.4522.96
2007CHAN0612.4522.60
2015KIMG0112.4620.18
2010RUGA0112.4621.27
2013MIRA0212.5027.84
2014CHAY0112.5020.07
2014JAUR0112.5120.47
2009CHEN3512.5222.34
2014SZNE0112.5222.03
2011ESQU0112.5320.32
2014NURU0112.5320.82
2010YUPH0112.5521.05
2008LIUE0112.5825.69
2013BOGA0112.5820.50
2012CHEN2512.6021.39
2014SARK0112.6120.09
2013LING0312.6120.12
2007CERP0212.6320.80
2012HOFL0112.6320.07

>15s:
WCA IDSolveAverage
2013SCHM027.7918.57
2011MERL017.9617.01
2013SEOH018.2121.93
2009KORB028.4315.32
2006SHEU018.5517.11
2011SANJ018.5615.31
2009HILD018.6316.53
2007CLAE038.6317.30
2012LIIH018.6820.09
2013MACI018.7817.25
2011NATH028.8016.26
2011RECH018.9015.25
2007SHIR018.9115.79
2008JADE018.9315.18
2014YANG118.9615.88
2010STOL019.0219.88
2011ALFA029.0915.83
2007HESS019.1316.31
2013SEOH019.1421.93
2008LINH019.2115.25
2009PICH019.2120.25
2008LINH019.2219.55
2005AKKE019.2216.00
2008BRAN019.2815.62
2010DUON019.3116.99
2009NATE019.3416.07
2013ANDR049.3515.13
2012PERK019.3515.09
2013KHUU019.3615.52
2014WANG269.3717.36
2013KASH019.4019.54
2007THOM029.4016.64
2007VALK019.4115.61
2009OHRN019.4217.66
2007LING019.4315.05
2008CLEM019.4317.39
2009KIEL019.4417.86
2009GUGL019.4415.68
2013CHEU019.4515.87
2012MAAL019.4615.39
2010ANDE039.4616.01
2011SALG019.5319.39
2007PERG019.5316.01
2010GOLD019.5415.58
2012KIMD019.5516.14
2003BRUC019.5516.24
2008JINX019.5515.61
2008LOMB019.5615.53
2009ROTA019.5622.45
2003POCH019.5617.21
2007PERG019.5815.79
2010KOUG019.5915.43
2014CYBU029.6416.76
2011FOYL019.6516.16
2015MARC039.6615.04
2003BRUC019.6815.51
2011HAVL019.6915.71
2013VIDA019.7115.89
2009CHEN359.7115.10
2012CROS019.7216.99
2013OLIV019.7215.08
2008DIEW019.7215.06
2005AKKE019.7516.00
2008MORA029.7515.61
2009JAGI019.7716.85
2012MORA069.7816.24
2013CHIX019.7817.63
2008LINH019.7815.25
2009VERA019.7815.10
2008BERE019.8015.32
2007LUJI019.8015.22
2006GARR019.8115.19
2006ANDO019.8315.34
2008LIZH029.8416.84
2013HANN019.8617.44
2012HUAN129.8718.65
2009HAYA019.8815.91
2007GOUL019.8815.59
2003BRUC019.8815.82
2007LIME019.8815.10
2006TABU019.9015.86
2009LAIY019.9216.80
2012SLIZ019.9221.90
2003BRUC019.9315.43
2012DOED019.9315.10
2009PICH019.9320.25
2013LINK019.9416.01
2011EBEL019.9417.23
2012CHIN019.9416.33
2011BRZE019.9616.65
2011WANG529.9715.92
2011BANS029.9816.64
2012IVAN029.9815.02
2012KONG019.9817.23
2006AUST019.9915.18
2008LINH019.9915.25
2010MARI0210.0015.35
2011SZAT0110.0017.28
2013GUMP0110.0024.85
2010BECK0110.0215.05

>10s:
WCA IDSolveAverage
2013RAVI065.5810.60
2010WANG535.8110.49
2010BURN015.9310.53
2013NAHM016.1510.07
2010THOM036.2111.14
2010LANE026.2511.39
2010PROT016.2710.33
2012MAHV016.3210.07
2011HAMS016.5012.09
2009CHAR036.5010.69
2006TSAI036.5011.28
2008HASH026.5611.51
2007VALK016.6110.25
2010KIPA016.6110.34
2010YOUY016.6111.16
2011KNOT016.6413.19
2011BARB036.6410.92
2010PROT016.6512.27
2007VALL016.6510.36
2009MORI056.7710.04
2010MALL016.8010.50
2013WOJT026.8111.24
2012ROQU016.8210.18
2009FUSH016.8410.21
2009BOZS016.8410.12
2010ADHI016.8411.38
2012SZEW016.8713.45
2008CHUC026.8912.06
2011GROG026.8910.15
2011HOFF026.9010.78
2009YAUR016.9110.40
2012RUPE016.9110.37
2013ZHOU196.9211.67
2005BATI016.9310.49
2008SEAR016.9312.22
2007HESS016.9410.52
2012PONC027.0010.60
2014KARN027.0011.49
2007NAKA037.0010.27
2010WEYE027.0010.96
2011LIYI027.0211.31
2007HESS017.0212.04
2010WEYE027.0210.25
2006GUNJ017.0310.38
2013HUAN307.0413.18
2015CHEA017.0511.00
2007HESS017.0611.26
2009YAUR017.0610.12
2007VALL017.0810.43
2005AKKE017.0812.26
2013BAUT017.0914.06
2010WEYE027.1110.20
2012KALE017.1110.93
2008SEAR017.1310.66
2011ZALE027.1311.02
2014GERB017.1310.11
2011ZENG067.1411.36
2010CANT027.1610.15
2007NAKA037.1610.63
2009LIUE017.1611.81
2013BALI017.1711.14
2009JIAY017.1810.44
2013UVES017.1811.61
2012PONC027.1810.68
2009ZHAN537.1810.90
2010KIPA017.1910.35
2012KALE017.1910.46
2012LEWI017.1911.04
2010WANG537.2010.10
2012ROZA017.2112.19
2012WESC017.2110.77
2009BARB017.2110.06
2009KORB017.2511.41
2012IVAN037.2511.97
2008LINE027.2510.89
2009DIEC017.2510.08
2009BARB017.2510.37
2005AKKE017.2510.98
2007WOOR017.2710.81
2009BARB017.2810.35
2013LEJE037.2810.35
2009WHIT017.2810.46
2007COHE017.2813.67
2010SCHM017.2810.24
2011RHEI017.2810.14
2013JOKI017.2810.02
2012KALE017.2912.06
2012DYGI017.2910.59
2012CHOS017.3010.01
2012CHOS017.3010.74
2014HOUS017.3011.90
2010LEED027.3010.41
2007GUIM017.3011.29
2011FATE017.3011.20
2011ALVI017.3012.05
2009KORB017.3111.69
2011TORG017.3111.21
2010PROT017.3110.16
2013PARK037.3110.47
2008SEAR017.3110.42

R code:
Code:
raw_data = read.csv(file = "WCA_export_Results.tsv", head = TRUE, sep = "\t")
valid_333 = data.frame(raw_data$personId, raw_data$eventId, 
  raw_data$value1, raw_data$value2, raw_data$value3, raw_data$value4, raw_data$value5, raw_data$average)
names(valid_333) = c("person", "event", "s1", "s2", "s3", "s4", "s5", "average")
valid_333 = subset(valid_333, event == "333" & average > 0)

s1s = data.frame(valid_333$person, valid_333$s1, valid_333$average)
s2s = data.frame(valid_333$person, valid_333$s2, valid_333$average)
s3s = data.frame(valid_333$person, valid_333$s3, valid_333$average)
s4s = data.frame(valid_333$person, valid_333$s4, valid_333$average)
s5s = data.frame(valid_333$person, valid_333$s5, valid_333$average)
n = c("person", "solve", "average")
names(s1s) = n
names(s2s) = n
names(s3s) = n
names(s4s) = n
names(s5s) = n
all_valid_333_solves = rbind(s1s, s2s, s3s, s4s, s5s)
all_valid_333_solves = subset(all_valid_333_solves, solve > 0)

sup20s = subset(all_valid_333_solves, average > 2000)
sup20s = sup20s[order(sup20s$solve),][1:100,]

sup15s = subset(all_valid_333_solves, average > 1500)
sup15s = sup15s[order(sup15s$solve),][1:100,]

sup10s = subset(all_valid_333_solves, average > 1000)
sup10s = sup10s[order(sup10s$solve),][1:100,]

In relation to this, what is the lowest number for "official PB single / official PB avg"?
 
In relation to this, what is the lowest number for "official PB single / official PB avg"?

For all people who have a PB single and average:
WCA IDPB SinglePB AveragePB Single / Average
2014ZHUR02111.68514.220.217183306755863
2014LOMB0188.76398.660.222645863643205
2014PATW0278.78320.920.245481739997507
2015TYMO0114.2256.520.251592356687898
2014KANT0313.3648.870.273378350726417
2013VINO0122.4174.860.299358803099118
2015NAGA0224.7581.370.304166154602433
2014BROA0177.00234.720.328050443081118
2012DIVA0193.27278.670.334696953385725
2015THOD0116.7349.170.340248118771609
2008KULI0121.2061.220.346292061417837
2014ANCC01112.81320.510.351970297338617
2006TANA0138.96108.320.359675036927622
2014THAK0568.82187.270.367490788700806
2012WEIB0284.26219.870.38322645199436
2014PHLE0132.0283.250.384624624624625
2009SOST0169.46180.540.384734684834386
2007OKAD0123.0959.380.388851465139778
2013ZHON0554.24137.890.393357023714555
2012SUZU0138.9097.930.397222505871541
2014WILL07150.46378.590.397422013259727
1982VALD0129.6274.440.397904352498657
2014MACH07158.15394.890.400491276051559
2012SANT0357.84143.760.402337228714524
2014WESO0218.6746.080.405164930555556

For PB sub-30 averages:
WCA IDPB SinglePB AveragePB Single / Average
2013GUMP0110.0024.420.40950040950041
2015ARYA0210.8725.520.425940438871473
2013TORR0210.4321.800.478440366972477
2015ESTU0214.0029.180.47978067169294
2014IVAN0113.2226.580.497366440933032
2014CHIE0111.0121.940.501823154056518
2013FERR0310.9421.780.502295684113866
2013BAUT017.0913.680.51827485380117
2013FANJ0214.2026.360.538694992412747
2015YUYO0114.7126.700.550936329588015
2007CLAE038.6315.640.551790281329923
2010LIUD0211.8321.430.552029864675688
2012BOBJ0116.5329.790.554884189325277
2012PARA0311.4020.530.555284948855334
2015HUAN2510.9019.600.556122448979592
2013VOLO0112.2721.940.559252506836828
2014LILI0115.3127.320.560395314787701
2014POPA0114.3425.430.563900904443571
2014SZNE0112.5222.030.568315932818883
2014LEIR0114.6525.720.569595645412131
2007COOP0110.6118.530.572584997301673
2014CYBU029.6416.760.575178997613365
2010ZHAO1814.1524.570.575905575905576
2014MERH0115.3426.560.577560240963855
2015QUAD0114.0324.210.579512598099959

Sub-20:
WCA IDPB SinglePB AveragePB Single / Average
2013BAUT017.0913.680.51827485380117
2007CLAE038.6315.640.551790281329923
2015HUAN2510.9019.600.556122448979592
2007COOP0110.6118.530.572584997301673
2014CYBU029.6416.760.575178997613365
2011MERL017.9613.580.586156111929308
2014WANG269.3715.970.586725109580463
2010HYUN017.9013.380.590433482810164
2015VALA0110.6117.930.591745677635248
2014SHEN068.5314.390.59277275886032
2013ZHOU196.9211.670.592973436161097
2010DUON019.3115.660.594508301404853
2014ZABI0111.0818.580.596340150699677
2013RAHS0111.5019.260.597092419522326
2010DOTR0110.4117.390.598619896492237
2008JADE018.9314.900.599328859060403
2013GRAE0110.1616.890.60153937240971
2014RAMA0610.7817.900.602234636871508
2009JAGI019.7716.200.603086419753086
2013SCHM027.7912.900.603875968992248
2014RUDA0111.2818.640.605150214592275
2009OHRN019.4215.520.606958762886598
2015MAGA027.5212.350.608906882591093
2013LUDW015.969.710.613800205973223
2012MAAL019.4615.390.614684860298895

Sub-15:
WCA IDPB SinglePB AveragePB Single / Average
2013BAUT017.0913.680.51827485380117
2011MERL017.9613.580.586156111929308
2010HYUN017.9013.380.590433482810164
2014SHEN068.5314.390.59277275886032
2013ZHOU196.9211.670.592973436161097
2008JADE018.9314.900.599328859060403
2013SCHM027.7912.900.603875968992248
2015MAGA027.5212.350.608906882591093
2013LUDW015.969.710.613800205973223
2011JOVI018.0513.060.616385911179173
2012TERE017.3611.900.618487394957983
2014HERN087.7312.420.622383252818035
2009SUPC018.5513.700.624087591240876
2013HUAN307.0411.180.629695885509839
2015BROK018.3013.150.631178707224335
2011NATH028.8013.910.632638389647735
2013CHIA038.9214.090.633073101490419
2013KATO017.5511.890.634987384356602
2009ZAMZ019.4114.800.635810810810811
2008MCKE019.4714.890.635997313633311
2012SZEW016.8710.730.640260950605778
2011VAND019.2814.480.640883977900553
2013PIEC028.7913.660.643484626647145
2007JOHN027.9412.320.644480519480519
2013MACI018.7813.610.645113886847906

Sub-10:
WCA IDPB SinglePB AveragePB Single / Average
2013LUDW015.969.710.613800205973223
2010BURN015.258.060.65136476426799
2012MAHV016.329.580.659707724425887
2013RAVI065.588.360.667464114832536
2013LINK016.139.180.667755991285403
2006TSAI036.509.690.670794633642931
2010LANE026.259.250.675675675675676
2012MCNE015.918.670.681660899653979
2011KNOT016.649.700.684536082474227
2013WOJT026.819.930.685800604229607
2011ZALE026.429.310.689581095596133
2010YOUY016.619.550.692146596858639
2011HAMS016.509.370.693703308431163
2010THOM036.218.920.696188340807175
2010ADHI016.849.790.698672114402451
2012WICH016.529.330.69882100750268
2011DERS016.779.620.703742203742204
2010WANG535.818.230.70595382746051
2011GROG026.899.690.711042311661507
2011BARB036.649.330.711682743837085
2011HOFF026.909.680.712809917355372
2010CORM026.418.990.71301446051168
2010PROT016.278.700.720689655172414
2009KONV016.158.500.723529411764706
2009PLES015.817.980.728070175438597

R code:
Code:
raw_data = read.csv(file = "WCA_export_Results.tsv", head = TRUE, sep = "\t")
valid_333 = data.frame(raw_data$personId, raw_data$eventId, raw_data$best,
  raw_data$value1, raw_data$value2, raw_data$value3, raw_data$value4, raw_data$value5, raw_data$average)
names(valid_333) = c("person", "event", "best", "s1", "s2", "s3", "s4", "s5", "average")
valid_333 = subset(valid_333, event == "333")

valid_333$best[valid_333$best <= 0] = 1000000
valid_333$average[valid_333$average <= 0] = 1000000

pb_singles = aggregate(valid_333$best, list(person = valid_333$person), min)
pb_avgs = aggregate(valid_333$average, list(person = valid_333$person), min)
names(pb_singles) = c("person", "single")
names(pb_avgs) = c("person", "average")
pbs = merge(x = pb_singles, y = pb_avgs, by.x = "person", by.y = "person")
pbs = subset(pbs, single != 1000000 & average != 1000000)

pbs$ratio = pbs$single / pbs$average
pbs = pbs[order(pbs$ratio),]

pbs_sub30 = subset(pbs, average < 3000)[1:25,]
pbs_sub20 = subset(pbs, average < 2000)[1:25,]
pbs_sub15 = subset(pbs, average < 1500)[1:25,]
pbs_sub10 = subset(pbs, average < 1000)[1:25,]
pbs = pbs[1:25,]
 
edit: Went through the database and found the biggest difference between an average and best single in that average. Daniel Sheppard got an average of 1:52.86 with a 16.46 single. Full average was 18.43, 20.22, (16.46), (DNF), 4:59.94.

I thought that one would be exceptional and the rest would be lucky singles, but it seems like most of these are the same way--a mostly good average and then a DNF paired with a complete collapse on one solve that skews the whole average. Even after I filtered to only see sub-30 averages, it's mostly stuff like someone who's comfortably sub-20 or sub-15 getting a DNF then a 40.xx or something.
That's pretty common,cubers who screw up two consecutive solves simply don't care about the average anymore. I remember Daniel''s 4:59.94 solve (I witnessed it): when it became evident that he wouldn't get a decent average anyway, he started to do random stuff until the 5-minute time limit was (almost) reached.
 
That's pretty common,cubers who screw up two consecutive solves simply don't care about the average anymore. I remember Daniel''s 4:59.94 solve (I witnessed it): when it became evident that he wouldn't get a decent average anyway, he started to do random stuff until the 5-minute time limit was (almost) reached.
Isn't that technically breaching the regulations?

Sent from my MotoG3 using Tapatalk
 
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