Odd WCA stats/ Stats request Thread

Whoever cleaned up the inconsistencies surrounding how states were entered into the database, thank you, you made my life really easy on this statistic.

RankNameNumber of States
1Bob Burton20
2Kit Clement18
3Brandon Mikel17
4Tim Reynolds16
5Takao Hashimoto (橋本貴夫)15
Dan Cohen15
7Jim Mertens14
Shelley Chang14
Adam Zamora14
Tyson Mao (毛台勝)14
Christopher Olson14
Anthony Brooks14
13Ryan DeLine13
Corey Sakowski13
Rowe Hessler13
Bruce Norskog13
Dave Campbell13
Chester Lian13
19AJ Blair12
Walker Welch12
Evan Liu12
Jeremy Fleischman12
Kian Barry12
Felix Lee12
Shaden Smith12
John Brechon12
David Woner12
28James Hildreth11
Mike Hughey11
Ethan Crislip11
Jaclyn Sawler11
James LaChance11
Chris Krueger11
Shane Rowland11
Timothy Sun11
Chris Hardwick11
Waris Ali11
Joshua Feran11
39Tristan Wright10
Bobby D'Angelo10
Kyle Barry10
Ryan Olson10
John Tamanas10
Anthony Hsu10
Marie Hughey10
Rebecca Hughey10
Noah Arthurs10
Kevin Hays10
Aina Hashimoto (橋本愛那)10
Leyan Lo10
Kevin Costello III10
Dan Selzer10
Phil Yu10
Jason Baum10
55Lauren Klemz9
Andrew Hwang9
Quentin McLean9
Sam Boyles9
Gavin Nelson9
Kevin Zhou9
Justin Jaffray9
Carson Penticuff9
Frank Morris9
Mike Kotch9
Justin Mallari9
Eric Zhao9
Sreeram Venkatarao9
Ian Winokur9
Bryan Logan9
Pavan Ravindra9
Henry Savich9
Chris Braiedy9
Kennan LeJeune9
74Weston Mizumoto8
Michael Young8
Nathan Dwyer8
Devin Corr-Robinett8
Clark Cheng8
Joe Epstein8
Daniel Hayes8
Jackson Warley8
Casey Pernsteiner8
Matthew Bahner8
Alex Ozer8
Dan Dzoan8
Lucas Garron8
Chris Dzoan8
Michael Gottlieb8
Shotaro Makisumi (牧角章太郎)8
Ravi Fernando8
Arthur Adams8
Myles Casanas8
Christopher Resendiz8
Collin Burns8
Zach Goldman8
Kate Sosik8
Adomous Wright8
Ilkyoo Choi (최일규)8
Mike Stewart8
Julianna Bennett8
Brandon Delacruz8
Louis Cormier8
Damian Bias8
Alese Devin8
Lucas Etter8
Zachary Nikolaus8
Phillip Lewicki8
Daniel Kottenstette8
Jasmine Lee8
Peter Still8

Note that this will not preserve unicode in Windows, but it will on Mac/Linux. I also removed FMC USA from these results -- no info regarding what state they were actually in, and it's probably safe to assume that FMC USA competitors were competing in a state they had already competed in.
Code: 
results = read.csv("WCA_export_Results.tsv", sep="\t", encoding="UTF-8")
competitions = read.csv("WCA_export_Competitions.tsv", sep="\t", encoding="UTF-8")
competitionsUSA = competitions[competitions$countryId == "USA",]

competitionsUSA_list = competitionsUSA$id

resultsUSA = results[results$competitionId %in% competitionsUSA_list,]

competitionsUSA$State = lapply(strsplit(as.character(competitionsUSA$cityName), ", "), "[", 2)

mergedtable = merge(competitionsUSA, resultsUSA, by.x = 'id', by.y = 'competitionId')
mergedtable = mergedtable[mergedtable$id != "FMCUSA2014",]

countstates = function(name) {
  dummy = mergedtable[mergedtable$personName == as.character(name),]
  number = length(unique(dummy$State))
  return(number)
}

competitors = as.character(unique(mergedtable$personName))
numComps = sapply(competitors, countstates) #really slow
outTable = as.data.frame(cbind(competitors, numComps))
outTable = outTable[order(-numComps),]
outTable$competitors = as.character(outTable$competitors)
write.csv(outTable,"outTable.csv",fileEncoding="UTF-8")

con <- file("outTable.csv", open="w", encoding="UTF-8")
write.table(outTable, con, sep=",",row.names=FALSE)
close(con)

I'll have to do a refresh of this statistic once Utah results are uploaded and I go to my first Oregon competition. :)
 
Last edited:
Kit Clement said:
Whoever cleaned up the inconsistencies surrounding how states were entered into the database, thank you, you made my life really easy on this statistic.

RankNameNumber of States
1Bob Burton20
2Kit Clement18
3Brandon Mikel17
4Tim Reynolds16
5Takao Hashimoto (橋本貴夫)15
Dan Cohen15
7Jim Mertens14
Shelley Chang14
Adam Zamora14
Tyson Mao (毛台勝)14
Christopher Olson14
Anthony Brooks14
13Ryan DeLine13
Corey Sakowski13
Rowe Hessler13
Bruce Norskog13
Dave Campbell13
Chester Lian13
19AJ Blair12
Walker Welch12
Evan Liu12
Jeremy Fleischman12
Kian Barry12
Felix Lee12
Shaden Smith12
John Brechon12
David Woner12
28James Hildreth11
Mike Hughey11
Ethan Crislip11
Jaclyn Sawler11
James LaChance11
Chris Krueger11
Shane Rowland11
Timothy Sun11
Chris Hardwick11
Waris Ali11
Joshua Feran11
39Tristan Wright10
Bobby D'Angelo10
Kyle Barry10
Ryan Olson10
John Tamanas10
Anthony Hsu10
Marie Hughey10
Rebecca Hughey10
Noah Arthurs10
Kevin Hays10
Aina Hashimoto (橋本愛那)10
Leyan Lo10
Kevin Costello III10
Dan Selzer10
Phil Yu10
Jason Baum10
55Lauren Klemz9
Andrew Hwang9
Quentin McLean9
Sam Boyles9
Gavin Nelson9
Kevin Zhou9
Justin Jaffray9
Carson Penticuff9
Frank Morris9
Mike Kotch9
Justin Mallari9
Eric Zhao9
Sreeram Venkatarao9
Ian Winokur9
Bryan Logan9
Pavan Ravindra9
Henry Savich9
Chris Braiedy9
Kennan LeJeune9
74Weston Mizumoto8
Michael Young8
Nathan Dwyer8
Devin Corr-Robinett8
Clark Cheng8
Joe Epstein8
Daniel Hayes8
Jackson Warley8
Casey Pernsteiner8
Matthew Bahner8
Alex Ozer8
Dan Dzoan8
Lucas Garron8
Chris Dzoan8
Michael Gottlieb8
Shotaro Makisumi (牧角章太郎)8
Ravi Fernando8
Arthur Adams8
Myles Casanas8
Christopher Resendiz8
Collin Burns8
Zach Goldman8
Kate Sosik8
Adomous Wright8
Ilkyoo Choi (최일규)8
Mike Stewart8
Julianna Bennett8
Brandon Delacruz8
Louis Cormier8
Damian Bias8
Alese Devin8
Lucas Etter8
Zachary Nikolaus8
Phillip Lewicki8
Daniel Kottenstette8
Jasmine Lee8
Peter Still8

Note that this will not preserve unicode in Windows, but it will on Mac/Linux. I also removed FMC USA from these results -- no info regarding what state they were actually in, and it's probably safe to assume that FMC USA competitors were competing in a state they had already competed in.
Code: 
results = read.csv("WCA_export_Results.tsv", sep="\t", encoding="UTF-8")
competitions = read.csv("WCA_export_Competitions.tsv", sep="\t", encoding="UTF-8")
competitionsUSA = competitions[competitions$countryId == "USA",]

competitionsUSA_list = competitionsUSA$id

resultsUSA = results[results$competitionId %in% competitionsUSA_list,]

competitionsUSA$State = lapply(strsplit(as.character(competitionsUSA$cityName), ", "), "[", 2)

mergedtable = merge(competitionsUSA, resultsUSA, by.x = 'id', by.y = 'competitionId')
mergedtable = mergedtable[mergedtable$id != "FMCUSA2014",]

countstates = function(name) {
  dummy = mergedtable[mergedtable$personName == as.character(name),]
  number = length(unique(dummy$State))
  return(number)
}

competitors = as.character(unique(mergedtable$personName))
numComps = sapply(competitors, countstates) #really slow
outTable = as.data.frame(cbind(competitors, numComps))
outTable = outTable[order(-numComps),]
outTable$competitors = as.character(outTable$competitors)
write.csv(outTable,"outTable.csv",fileEncoding="UTF-8")

con <- file("outTable.csv", open="w", encoding="UTF-8")
write.table(outTable, con, sep=",",row.names=FALSE)
close(con)

I'll have to do a refresh of this statistic once Utah results are uploaded and I go to my first Oregon competition. :)
Click to expand...

Dang, now I really wish I would have went to Utah. I knew I was behind you and Bob, but didn't know I was 3rd. :)

I'd like to point out that I have been to the most out of the non-delegate competitors.
 
Last edited:
AlphaSheep said:
I'm just curious... How many people have competed on all 6 continents? I know Anders Larsson has, but are there any others? And how many have competed on 5 continents?
Click to expand...

Could someone please do this statistics? And, for the competitors who have compete on 5 continents, to sum up their 3x3 avg PB on each continents, what's the top competitors' list?
 
Top Country

I thought of this the other day while I was eating dinner because my Grandma said, "China must be the best in cubing." But, I thought even Canada should be better than China in terms of sub-x solvers per z persons.

I decided to take the top 10 countries with the most cubers (according to https://www.worldcubeassociation.org/results/statistics.php) for this sample calculation.

Equation:

y = number of 3x3 competitors in a country.
n = number of 3x3 competitors with a sub-10 average in a country.

y/n = 1 sub-10 average 3x3 competitor/per z 3x3 competitors

Results:
(I rounded to one decimal point).

1. Germany 1 sub-10 average 3x3 competitor/per 60.8 competitors
2. Japan 1 sub-10 average 3x3 competitor/per 75.1 competitors
3. Poland 1 sub-10 average 3x3 competitor/per 110 competitors
4. France 1 sub-10 average 3x3 competitor/per 125.3 competitors
5. Canada 1 sub-10 average 3x3 competitor/per 134.1 competitors
6. USA 1 sub-10 average 3x3 competitor/per 143.5 competitors
7. Brazil 1 sub-10 average 3x3 competitor/per 176.9 competitors
8. Indonesia 1 sub-10 average 3x3 competitor/per 255.75 competitors
9. China 1 sub-10 average 3x3 competitor/per 306.8 competitors
10. India 1 sub-10 average 3x3 competitor/per 333.9 competitors

I would like to see where you crazy statistic experts can take my idea. Possibly all countries averaged over all events (although some countries and events would need to be removed so the data doesn't get skewed).
 
Genius4Jesus said:
I thought of this the other day while I was eating dinner because my Grandma said, "China must be the best in cubing." But, I thought even Canada should be better than China in terms of sub-x solvers per z persons.

I decided to take the top 10 countries with the most cubers (according to https://www.worldcubeassociation.org/results/statistics.php) for this sample calculation.

Equation:

y = number of 3x3 competitors in a country.
n = number of 3x3 competitors with a sub-10 average in a country.

y/n = 1 sub-10 average 3x3 competitor/per z 3x3 competitors

Results:
(I rounded to one decimal point).

1. Germany 1 sub-10 average 3x3 competitor/per 60.8 competitors
2. Japan 1 sub-10 average 3x3 competitor/per 75.1 competitors
3. Poland 1 sub-10 average 3x3 competitor/per 110 competitors
4. France 1 sub-10 average 3x3 competitor/per 125.3 competitors
5. Canada 1 sub-10 average 3x3 competitor/per 134.1 competitors
6. USA 1 sub-10 average 3x3 competitor/per 143.5 competitors
7. Brazil 1 sub-10 average 3x3 competitor/per 176.9 competitors
8. Indonesia 1 sub-10 average 3x3 competitor/per 255.75 competitors
9. China 1 sub-10 average 3x3 competitor/per 306.8 competitors
10. India 1 sub-10 average 3x3 competitor/per 333.9 competitors

I would like to see where you crazy statistic experts can take my idea. Possibly all countries averaged over all events (although some countries and events would need to be removed so the data doesn't get skewed).
Click to expand...

Interesting. Calculated it for the UK, got 1 in 61.4 (430 competitors, 7 sub-10), very close to Germany.
 
TDM said:
Interesting. Calculated it for the UK, got 1 in 61.4 (430 competitors, 7 sub-10), very close to Germany.
Click to expand...

Australia has 1 in 212.7 (638 competitors, 3 sub-10)

Finland has 1 in 136 (looking at you, Kim)

Sweden has 1 in 109 (436 competitors, 4 sub-10)

Netherlands has 1 in 95 (285 competitors, 3 sub-10)

Ireland probably wins with 1 in 17.5 (35 competitors, 2 sub-10)

The first few countries that popped into my head.

EDIT: Math corrected.
 
Did it in Excel (Any countries not mentioned have no sub-10 cubers):

Ireland 19
Greece 28.5
Slovenia 32
Korea 63.91666667
Germany 64.41176471
United Kingdom 64.57142857
Belarus 70.66666667
Russia 72.7
Thailand 73.4
Japan 89.27272727
Iran 90.4
Italy 93.4
Netherlands 100
Austria 101
Malaysia 105.75
Taiwan 109.75
Sweden 112.75
Switzerland 117
Poland 120.2857143
France 131.375
Norway 135
Finland 141
Canada 144
USA 156.1538462
Philippines 158.8
Ukraine 162.6
Hungary 171
Vietnam 208
Australia 218.3333333
Hong Kong 233
Singapore 259
Peru 294.5
Colombia 297.5
Spain 307
China 332.9444444
Indonesia 365
Brazil 369.8
Mexico 435
India 714.6666667
 
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