F1 2011 Belgium - Lap Time Heat Map

How can we visualise the range of laptimes across a race, and as a result identify when a driver is laying down a set of particularly good times? Here's a first foray into using heat maps to visualise times over a race.

Here's the sort of thing I mean:



This is a visualisation of times recorded during the Belgian grand prix (can you see when the safety car came out?)

The chart is defined as follows: x axis is driver/car number; y axis is lap; the data that is visualised by the "heat" of each of block is the difference between the laptime and the fastest laptime recorded by that driver during the race. Bright red shows a small gap between the current laptime and that driver's fastest lap. (In fact, I use a logarithmic mapping from delta to colour value.)

If we look for the deep reds, those are laps that were close to that driver's fastest lap. Note that there is plenty of scope for visual illusions - car 11 appears to have a red much brighter than any other driver in lap 42... But each car has a lap that red (the driver's fastest lap, when the delta to their fasted lap is 0). For an example of just such an illusion, see Adelson Checker Shadow Illusion (h/t Mr C/@sidepodcast)

Here's the R script I used to generate the heatmap. It pulls the data from the Google spreadsheet I set up to store timing data from the race.

#Grab the data from a Google spreadsheet
library(RCurl)
gsqAPI = function(key,query,gid=0){ return( read.csv( paste( sep="",'http://spreadsheets.google.com/tq?', 'tqx=out:csv','&tq=', curlEscape(query), '&key=', key, '&gid=', curlEscape(gid) ) ) ) }

beldata=gsqAPI('0AmbQbL4Lrd61dDBfNEFqX1BGVDk0Mm1MNXFRUnBLNXc','select C,D,E',gid='9')
l2=with(beldata, data.frame(car=car,lap=lap,laptime=lapTime))

#Now we're going to reshape the data and then plot it
library(graphics)
library(plyr)
library(reshape)

#this function is just to keep things tidy
#It could be refactored to accommodate more parameters, eg col, scale
f1djHeatmap=function(d){
lx=cast(d, lap ~ car, value=c("diff"))
lx=lx[-1]
lm=data.matrix(lx)
lh=heatmap(lm, Rowv=NA, Colv=NA, col = heat.colors(256), scale="column", margins=c(5,10))
}

#reshape the data
dd<- ddply(l2, .(car), summarize, lap=lap, diff=log(1+laptime-min(laptime)))

#plot the heatmap
f1djHeatmap(dd)

Here's a view over the fuel corrected laptimes:



This looks more textured to me...if the fuel penalty model is sound, then maybe it shows some deterioration in times due to tyre wear.....?

Here's how it was generated:

beldata=gsqAPI('0AmbQbL4Lrd61dDBfNEFqX1BGVDk0Mm1MNXFRUnBLNXc','select C,D,F',gid='9')
l2=with(beldata, data.frame(car=car,lap=lap,laptime=fuelCorrectedLaptime))

dd<- ddply(l2, .(car), summarize, lap=lap, diff=log(1+laptime-min(laptime))) f1djHeatmap(dd)

If you can find better ways of doing the colour mapping, or scaling the data, please let me know in the comments.

I also wonder whether a heat map showing position held by each car at the end of each lap in the race would be informative?

PS if you want to try out R, I suggest using RStudio.

F1 2011 Belgium - Race Position Chart

So who had the most "interesting" race at Spa this year?

The following chart tries to provide an overview of key position related information from across the race as a whole. In particular, it identifies:

- grid positions;
- race positions at the end of the first lap;
- final race positions (before any changes by the stewards);
- the range of race positions taken by each driver throughout the race.



Key:

- red dot: grid position;

- black dot: final position

- horizontal tick mark: position at end of first lap

- vertical line: range of race positions

The chart was constructed with a view to providing journalists with a snapshot overview of the race as an aid to intifying which drivers might have an an "interesting" race: blog.ouseful.info/2011/08/03/data-driven-storytelling-wor...

So:

- what sorts of information can you pull out of this chart (please let me know in the comments...;-) For example, we can see Senna had a lousy start, Button's race went all over the place, Schumacher had an amazing start, (and Ricciardo fared quite well at the start too, as did Rosberg), and five cars did not finish (five missing black dots).

- how can the chart be improved?

PS Any ideas on new and "interesting" ways of displaying information about the pit stop activity?