Friday, 8 April 2016

Half Marathon Analysis Using Python and R

I recently did a half-marathon run and, whilst I did a personal best, I paced it really badly meaning the last 5km were hard and slow.  This, coupled with some interesting results data that was published gave me a Geek idea!

Here's a snippet from the results PDF:

So for all 10,000 runners a whole plethora of data to compare and contrast.  In particular I was interested in how my 5k splits compared with everyone else's.  Mine were:
  • 0-5km = 22m0s
  • 5-10km = 21m56s
  • 10-15km = 22m03s
  • 15-20km = 23m37s
So really consistent across the first 15km then a bad fade over the last 5.  In fact a quick calculation shows I was 7.4% slower for the last 5km than the average for the first 15km.  However before I beat myself up over this I needed to know whether this was typical, better than average or worse than average.

All the results were in a PDF and what a pain that turned out to be to turn into something I could process.  I tried various online services, saving as text in Adobe Acrobat, avoided the paid for Adobe service and tried a Python module called pyPdf but none would allow me to turn the PDF file into a well formed text file for processing. 

In the end I opened the PDF in Adobe Acrobat, copied all the data then pasted it into Windows Notepad.  The data looked like this (abridged):

GunPos RaceNo Name Gender Cat Club GunTime ChipPos ChipTime Chip 5Km Split Chip 10Km Split Chip 15Km Split Chip 20Km Split 

5 Robert Mbithi M 01:03:57 1 01:03:57 00:15:08 00:29:39 00:44:44 01:00:24 
72 Scott Overall M 01:05:13 2 01:05:13 00:15:33 00:30:46 00:46:13 01:01:59 
81 Chris Thompson M ALDERSHOT FARNHAM & DISTRICT AC 01:05:14 3 01:05:14 00:15:33 00:30:46 00:46:13 01:01:59 
6 Paul Martelletti M RUN FAST 01:05:15 4 01:05:15 00:15:33 00:30:46 00:46:13 01:01:59 
82 Gary Murray M 01:06:12 5 01:06:12 00:15:33 00:30:46 00:46:18 01:02:37 

I then had to work pretty hard to turn this into a file that could be read into my favourite analysis package, R.  Looking at the data above you can see:
  • There's spaces between fields.
  • There's spaces within fields.
  • There's missing fields (e.g. age and club).
  • The PDF format means some long fields overlap with each other.
I actually had to write quite a lot of Python script to turn this into a CSV.  I've put this in the bottom of this post so as to know interrupt the flow (baby). In the script I also added some fields where the hh:mm:ss format times were turned into simple seconds to help with the maths.

Eventually I had a CSV file to play with and I read it into R using this command:

> rhm1 <- read.csv(file=file.choose(),head=FALSE,sep=",")

I added some column names with:

> colnames(rhm1) <- c("GunPos","RaceNo","Gender","Name","AgeCat","Club","GunTime","GunTimeSecs","ChipPos","ChipTime","ChipTimeSecs","FiveKSplit","FiveKSplitSecs","TenKSplit","TenKSplitSecs","FifteenKSplit","FifteenKSplitSecs","TwentyKSplit","TwentyKSplitSecs")

I then computed the net 5k splits (from the elapsed time) with:
> rhm1$TenKSplitSecsNet <- rhm1$TenKSplitSecs - rhm1$FiveKSplitSecs
> rhm1$FifteenKSplitSecsNet <- rhm1$FifteenKSplitSecs - rhm1$TenKSplitSecs
> rhm1$TwentyKSplitSecsNet <- rhm1$TwentyKSplitSecs - rhm1$FifteenKSplitSecs

I then computed the mean time for the first 3 splits:
> rhm1$FirstFifteenKMean <- (rhm1$FiveKSplitSecs + rhm1$TenKSplitSecsNet + rhm1$FifteenKSplitSecsNet) / 3

...and used this to compute the percentage difference in the last 5k split from the average of the first 3:
rhm1$Last5KDelta <- (rhm1$TwentyKSplitSecsNet - rhm1$FirstFifteenKMean) / rhm1$FirstFifteenKMean

Finding me in the data:
> rhm1[grep("Geek Dad",rhm1$Name),]
    GunPos RaceNo Gender       Name AgeCat Club  GunTime GunTimeSecs ChipPos 
869    869  13759      M   Geek Dad     40      01:39:12        5952     918 
ChipTime ChipTimeSecs FiveKSplit FiveKSplitSecs TenKSplit TenKSplitSecs 
01:34:54         5694   00:22:00           1320  00:43:56          2636      
    FifteenKSplitSecs TwentyKSplit TwentyKSplitSecs TenKSplitSecsNet 
                 3959     01:29:36             5376             1316             

FifteenKSplitSecsNet TwentyKSplitSecsNet FirstFifteenKMean Last5KDelta
                1323                1417          1319.667    0.073756

I could then plot all the data using ggplot.  I chose a density plot to look at the proportions of each "Last5KDelta" value.  Here's the command to create the plot (and add some formatting and labels).

> library(ggplot2)
qplot(rhm1$Last5KDelta, geom="density", main="Density Plot of Last 5K Delta",xlab="% Delta", ylab="Density", fill=I("blue"),col=I("red"), alpha=I(.2),xlim=c(-1,1))

So nice looking chart and I can see that there's more people who were slower in the final 5k (positive value) than faster.  Looking good!

However this didn't tell me whether I was better or worse than average.  For this I need a cumulative frequency plot.  This uses the stat_ecdf (empirical cumulative distribution function) to create the plot.  The command below does this, tweaks the x axis to make it tighter and puts in an extra a axis tick at 7% so I can see where "I" sit on the graph.

> chart1 <- ggplot(rhm1,aes(Last5KDelta)) + stat_ecdf(geom = "step",colour="red")  + scale_x_continuous(limits=c(-0.3,0.6),breaks=c(-0.3,-0.2,-0.1,0,0.07,0.1,0.2,0.3,0.4,0.5,0.6))
chart1 + ggtitle("Cumulative Frequency of Last 5K Delta") + labs(y="Cumulative Frequency")

So get in!  0.7% sits at less than 50% cumulative frequency!  More people faded more than me over the last 5k.

However somewhere behind me was a man carrying a fridge!  I decided to look at just those who completed the run in under 2 hours by doing:

> rhmSubTwo <- rhm1[rhm1$ChipTimeSecs<7200,]

Which gives this chart:

Darn it.  About 68% of this cohort faded less than me.  Not looking so good now...

What about those equal or better than me?
> rhmSubMe <- rhm1[rhm1$ChipTimeSecs<5694,]

Looking pretty poor now :-(.  I must pace myself better next time.

Here's all the Python code to create the .csv file:

InputFile = "/home/pi/Documents/RHM/VitalityReadingHalfMarathon_v2.txt"
OutputFile = "/home/pi/Documents/RHM/RHM.csv"

#Open the file
InFile = open(InputFile,'r')
OutFile = open(OutputFile,'w')

#This takes a time in h:m:s or similar and turns to seconds.
def TimeStringToSecs(InputString):
  #There are 2 cases
  #1)A proper time string hh:mm:ss
  #2)Something else with letters and numbers munged together
  if len(InputString) == 8:
    #Compute the time in seconds
    SecondsCount = (float(InputString[0:2]) * 3600) + (float(InputString[3:5]) * 60) + float(InputString[6:8])
    return str(SecondsCount)
    print "Got this weird string: " + InputString
    return "-1"

for i in range(1,10981):
  #Initialise variables
  Outstring = ""
  EndString = ""
  MidString = ""
  GenderFound = False

  #Read a line
  InString = InFile.readline().rstrip()

  print InString

  #Split the line based upon a space
  SplitStr1 = InString.split(" ")

  #We can rely on the first field which is gun position and second field which is race number.  But don't put Gun position as R ignores it!
  OutString = SplitStr1[1] + ","

  #We can also rely on the last 7 fields of the line which respectively are GunTime,ChipPos,ChipTime,5KSplit,10KSplit,15KSplit,20KSplit
  NumFields = len(SplitStr1)
  #Compute the end of the output string
  for z in range(7,0,-1):
    #print "z=" + str(z) + ".  Equates to" + SplitStr1[NumFields - z]
    EndString = EndString + SplitStr1[NumFields - z] + ","
    #Look up the time in seconds.  Not for case 6 which is the gun position
    if (z != 6):
      EndString = EndString + TimeStringToSecs(SplitStr1[NumFields - z]) + ","
  #Hardest bit last.  Name, Gender, Age and Club.  Gender is reliably there, except for long names where it gets mangled.
  #Hence find it and you know everything before is the name
  for a in range(0,len(SplitStr1)):
    if (SplitStr1[a] == "M") or (SplitStr1[a] == "F"):
      #THis is the position of the gender which is the "anchor" for everything else
      GenderPos = a
      #Add it to the middle part of the string.  No worries it's in different order to file.
      MidString = SplitStr1[a] + ","
      #Say we found the gender.
      GenderFound = True

  #Process for the case where gender was found
  if GenderFound:
    #Now we know everything before (exclusing first two numbers was the name.  Add the parts of the name together.  The below code should handle
    #complex names
    for b in range(2,GenderPos):
      MidString = MidString + SplitStr1[b]
      #See if it's not the last part of the name.  If not add a space
      if (b < (GenderPos - 1)):
        MidString = MidString + " "
        MidString = MidString + ","

    #Now test the part after the gender position.  If it's "U23" or a number(but not 100 as cllubs start with this!) then this is the age category
    if (SplitStr1[GenderPos + 1] == "U23"):
      MidString = MidString + SplitStr1[GenderPos + 1] + ","
      #Log where the club might start
      ClubStartPos = GenderPos + 2
    elif SplitStr1[GenderPos + 1].isdigit():
      if SplitStr1[GenderPos + 1] != "100":
        MidString = MidString + SplitStr1[GenderPos + 1] + ","
        #Log where the club might start
        ClubStartPos = GenderPos + 2
        MidString = MidString + ","
        #Log where the club might start
        ClubStartPos = GenderPos + 1
      MidString = MidString + ","
      #Log where the club might start
      ClubStartPos = GenderPos + 1

    #So now everything from ClubStartPos "might" be a club.  We can test this by seeing if what might be the club is actually gun
    #time which is 7th  from the end
    if (SplitStr1[ClubStartPos] == SplitStr1[NumFields - 7]):
      MidString = MidString + ","
      #Loop adding elements of the club
      for c in range(ClubStartPos,NumFields - 7):
        MidString = MidString + SplitStr1[c]
        #See whether to add a space
        if (c < (NumFields - 8)):
          MidString = MidString + " "
          MidString = MidString + ","
  else: #Where there is no gender.  Add commas to represent Name,Age and Club and somethign to say it was a long name!!!
    MidString = ",Long Name,,,"

  #print OutString
  #print MidString
  #print EndString

  print OutString + MidString + EndString
  OutFile.write(OutString + MidString + EndString + '\r\n')


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