F1 Data Analysis: Constructor And Overall Race Results

I’ve been following Formula 1 for 5 years now. Might seem like a lot, but considering Formula 1’s first race was in the 1950s, I barely scraped the surface in terms of know it. This is why I decided to write these series of F1 Data Analysis blogs, to get to know the sports more. And also, because I get to play with plenty of data. Let us begin!

First one in this series, Constructor And Overall Race Results. With recent Formula 1 seasons, it’s easy to lose perspective on a constructor’s race competence. Mercedez-Benz suddenly dominating. Renault, McLaren, and Williams now only competing for midfield. And Sauber being the new backmarker. To not lose sight, let us try to quantify these teams results in terms of their race results.


To perform data analysis on race results, we must have a way of quantifying a race. We know that Formula 1 has a points system, in which race winner receives top points, second gets the next higher points and so on. There are two problems with using the points system for data analysis:

  1. Points system have changed over the years. From 2010 till the time of writing this, given points are much higher and is distributed to more race positions, up to 10th. This puts a bias on race results post-2010 race results.
  2. Due to points being distributed on only up to the 10th race, using points system for data analysis will give us an erroneous midfield results. Almost lumping them back to the backmarker teams.

Due to this, I need a more intrinsic race result metric. I decided to go for race positions. 1st place in 1950 is still 1st place today, fixing the bias introduced by using the points system. And since each driver is assigned a race position during the end of the race, we fix “lumping problem” if we were to use the points system.

The only problem with using race position is that, unlike the points system, we get a lower number (e.g. 1st) for a higher race position. This can easily be amended by inverting or making all race position negative. This way, 1st position, will of a value of -1, the second position will have a value of -2, and so on. To have a decreasing but positive number, I added an arbitrary number, 50, for each position. I chose 50, since its highly unlikely that we have 50 drivers at one race, ensuring a positive value for all race position.

Results by Team

After pulling all race results since 1950 up to 2017 Suzuka GP, the following bar chart is compiled for, Race Position Sum v. Constructor. I omitted the least performing teams, so the chart is a bit readable. I encourage you to click the photo and zoom in on the image.

Suddenly, the myopic perspective of a new fan vanishes. Despite, the recent Mercedez-Benz domination, it’s hard to see that teams like McLaren, Williams, Sauber, Renault, and even the late Lotus, have better overall race results.

Although this data doesn’t exactly show if current Mercedez-Benz domination is a blip, that is acquired in a short period of time, after reading through Mercedez-Benz history in F1, they are recent. Withdrawing after 2 years in the 1950s, they didn’t race in f1 again until 1991. This proves that their accumulative race results are indeed a “blip”. And their championships a proof of their mastery of this hybrid era.

McLaren and Williams, battling backmarkers, get the second and third best overall race results. Plenty of McLaren and Alonso fans yearning for the glory days to come back. And Williams, well, I don’t know what’s their excuse. They have a Mercedes engine and said a said to be good facilities for building a chassis. One thing is for sure if they don’t act, this chart will shift them to the right for each race they underperform.

A final thing to note. This chart is already very right bias. Translating to many F1 teams have fallen short in terms of overall performance. And this is with many more team omitted from this chart.

This concludes my first fiddling with F1 data. In the future, I intend to divulge more. Many questions have already come up while writing this, such as density of Race Results Density (race results in a given time duration), Race Results And Team Budget, and many more! Cya then.

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