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:

1
2
3
4
5
6
7
8

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

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