Tinkering with Competitive Supertimes

I’m back on the R thang with F1 data from ergast.com, and started having a look at how drivers and teams compare at a circuit.

One metric I came across for comparing teams over a season is the supertime, typically calculated for each manufacturer as the average of their fastest single lap recorded by the team at each race weekend expressed as a percentage of the fastest single lap overall.

It struck me that we can also derive a reduced competitive supertime by basing the calculation on best laptime recorded across the qualifying and race sessions, omitting laptimes recorded in the practice sessions.

We can draw on the notion of supertimes to derive two simple measures for comparing team performances based on laptime:

  • evolution of manufacturer competitive supertime for a circuit over the years;
  • evolution of manufacturer competitive supertime for each circuit over the course of a season.

We can also produce driver performance metrics based on the competitive supertime of each driver.

So here are some notes on doing just that… I’m pulling the data from a MySQL database built from a datadump published via ergast.com.

q=paste0('SELECT circuitRef, c.name AS circuit, r.name as race, location, country FROM races r JOIN circuits c WHERE r.circuitId=c.circuitId AND year=',year,' AND round=',round)
cct = as.list(dbGetQuery(ergastdb, q))

[1] "bahrain"

[1] "Bahrain International Circuit"

[1] "Bahrain Grand Prix"

[1] "Sakhir"

[1] "Bahrain"

Calculate some competitive supertimes for manufacturers:


q=paste0('SELECT q1, q2, q3, fastestLapTime, cn.name, d.code, year FROM races r JOIN circuits c JOIN results rs JOIN constructors cn JOIN qualifying q JOIN drivers d WHERE r.circuitId=c.circuitId AND d.driverId=rs.driverId AND r.raceId=rs.raceId AND cn.constructorId=rs.constructorId AND q.raceId=r.raceId AND q.driverId=rs.driverId AND cn.name IN (',teamsThisYear_str,') AND circuitref="',cct$circuitRef,'" ORDER BY year')
st = dbGetQuery(ergastdb, q)

st = melt(st,
          id.vars = c("name","code", "year"),
          measure.vars = c("q1", "q2", "q3", "fastestLapTime"))

st['time'] = as.numeric(apply(st['value'], 1, timeInS))

# Normalise the time
st = ddply(st, .(year), transform, ntime = time/min(time, na.rm=TRUE))

#Find the best time for each manufacturer per race weekend (quali+race)
stt = ddply(st,
            stime = min(ntime, na.rm=TRUE))

stt$label = as.factor(stt$name)

Here’s a chart theme I’ve used elsewhere:


  g = g + theme_minimal(base_family="Arial Narrow")
  #g = g + theme(panel.grid.major.y=element_blank())
  g = g + theme(panel.grid.minor=element_blank())
  g = g + theme(axis.line.y=element_line(color="#2b2b2b", size=0.15))
  g = g + theme(axis.text.y=element_text(margin=margin(r=0, l=0)))
  g = g + theme(plot.margin=unit(rep(30, 4), "pt"))
  g = g + theme(plot.title=element_text(face="bold"))
  g = g + theme(plot.subtitle=element_text(margin=margin(b=10)))
  g = g + theme(plot.caption=element_text(size=8, margin=margin(t=10)))

Create a supertime chart plotting function:

compSupertime = function(df, cct, labelSize=0.7, smooth=FALSE){
  if (smooth){g=g+stat_smooth(aes(colour=label),method = "lm", formula= y ~ x + I(x^2), se=FALSE, size=0.7)}
  else { g=g+geom_line(aes(colour=label))}
  g=g+ guides(colour=FALSE)+xlim(min(df$year),max(df$year)+1)
  #cex is label size
  g=g+geom_dl(aes(label = label, colour=label),method = list(cex = labelSize, dl.trans(x = x + .3), "last.bumpup"))
  g=g+labs(x=NULL,y='Competitive Supertime (% of best)',
           title=paste0('F1 ', cct$race, ' - ','Competitive Supertimes',', ', min(stt$year), ' to ', max(df$year)),
           subtitle = paste0(cct$circuit,', ',cct$location,', ',cct$country),
           caption="Data from Ergast database, ergast.com/mrd")

Let’s have a look…





The best fit / model line obviously leaves something to be desired, eg in the case of Renault. But it’s a start.

I’ve also started working on a workflow to autopublish stuff to Gitbooks via github. For example, here’s the site in progress around the Bahrain F1 2018 Grand Prix. Here’s an earlier example (subject to updates/reflowing!) around the Australia 2018 F1 Grand Prix.

PS As I get back in to this, I’ll probably start updating the Wrangling F1 Data With R with recipes again… Also on the to do list is a second edition based on the tidyverse way of doing things…

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