Two Castles Run 2011

I did the Two Castles Run today; it’s a 10km race between Warwick and Kenilworth castles. The organizers were very quick to put the results online and even went the extra mile of offering them as a CSV file. It was therefore very tempting to launch R and see what the distribution looked like (and how I fared compared to the rest of the runners).

After a quick R script to read and parse the data:

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library(ggplot2)
results<-read.csv("2011TwoCastlesRun.csv")
results$Minutes<-sapply(as.character(results$ChipTime),
  FUN=function(s) sum(as.integer(strsplit(s,':')[[1]])*c(60,1,1/60)))
summary(results$Minutes[results$M.F=="M"])
p<-ggplot(results,aes(Minutes,colour=M.F))+geom_density()
print(p)
print(results[results$Bib==2474,])

the distribution of the results (in minutes) looks like this:

As expected, men are faster on average than women but it’s funny to see how similar the two curves are; they even have the same small bump after the median. I wonder what makes those bumps.

My time today was 48’29 (or 48.4833 minutes), which places me at the 740th position. How good is that? Well,

summary(results$Minutes)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  32.08   48.72   55.13   55.86   61.46   99.50

So I’m in the first quartile!
But wait, looking at men only:

summary(results$Minutes[results$M.F=="M"])
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  32.08   46.68   51.85   52.84   57.62   99.50

I’m not any more. Still closer to the quartile than the median though!

UPDATE:
A reader sent me the distribution for the 2010 results:

They both look very similar year on year. Actually, putting them on top of each other for direct comparison, the similarity is even more obvious:

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results<-rbind(results2010,results2011)
results$year<-c(rep(2010,dim(results2010)[1]),rep(2011,dim(results2011)[1]))
p<-ggplot(results)+geom_density(aes(Minutes,linetype=factor(year)))+facet_grid(.~M.F)
print(p)

Did people do better this year? A simple merge shows us the improvement (or lack thereof) of the runners who ran both races (assuming no homonyms):

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m<-merge(results2011,results2010,by=c("Forename","Surname"),suffixes=c(".2011",".2010"))
p<-ggplot(m,aes(Minutes.2011-Minutes.2010))+geom_density()
print(p)
count(m$Minutes.2011<m$Minutes.2010)$freq*100/dim(m)[1]

[1] 27.98077 72.01923
Indeed! Most people (72%) improved their time, some by a 10min margin. That’s encouraging!