“If you torture the data long enough, it will confess.”
~ Ronald Coase
This is the second blog post related to Data Visualization and this time it’s about Formula 1. I am going to comment on datasets related to Formula-1 racing . You can download the dataset from here . The DataSet and code can be found here too.
“Race cars are neither ugly nor beautiful. They become beautiful when they win.”
– Enzo Ferrari, Founder of Scuderia Ferrari
Some of the stacks used are : Matplotlib, Seaborn , Plotly, Pandas, Numpy,
Formula One cars are the fastest regulated road-course racing cars in the world and many famous constructors like Ferrari, Mercedes, Red-Bull, McLaren , Renault participate in this with their highly engineered cars.
The success in Formula-1 depends upon cars and drivers. But ,Recent years have seen total domination by one team in any given season. It seems that if a team manages to find an engineering edge for their cars, they will win the drivers’ and the constructors’ championships regardless of how good the opposition drivers are. Ferrari, Brawn, Red Bull, now Mercedes – same story. There have been years where Ferrari reigned and then in more recent years Red Bull and then Mercedes have won Championships .
Let’s see which constructor has won most percentage and absolute count of championships.
The upper plot shows the number of Constructor’s championship won by the constructors. The following Plot will show the winners with respective year , so that the plot would be more interesting.
As you can see last 6 years is all Mercedes, from 2010-13 all Red Bull. And this is a proof that machine and constructors are most important factors when it comes to Championship win.Let’s see one more graph
Let’s see this Boxplot , this boxplot is a trivial one and generally it’s not a good idea to use boxplot in this condition. But one thing can be well explained by this boxplot .If we see from 1980s to mid 2000s , there has been three major constructors(Ferrari, McLaren, Williams) who won large number of championships, and they won alternately , i.e there was a three sided wars and all three won their fair share. But after mid-2000s , we can see it has just been a short period of consecutive wins by various constructors as Renault , Red Bull and now Mercedes.when we see after 2005 period it has been all one sided for a period of time, and then a new winner emerges.
Before going to next topic I will also add the plot for most number of championship wins for drivers.
We can see there are a very limited number of driver who has won the championship that started from 1950s , and constructors play a major role in this.
How the speed of cars have changed over time
It will be interesting to see how cars have evolved in all these years, how their speed have changed , how different tracks affect speed.
Hmm, from graph, it doesn’t seem there has been much of improvement in the speed.
We can also see a 3 year span of short lived Indian GP. Italian GP recorded the fastest lap speed , while Singapore GP the least.
Let’s see top 10 fastest Lap and along with their drivers and Constructors.
Now , instead of championship we will see most number of “Races” won by each Driver and Constructor. so,
Well , we can see It is lead by Ferrari and McLaren , and Mercedes are chasing them very well , and it would become more evident if we were to include 2018,2019 data. The below graph is for drivers,
Is there a British superiority in F1 ?
In this plot the color is coded as per nationality (you can see by hovering over graph). Well it may be not completely dominated by british constructors but a well portion of it has been. This will become more evident by following graph:
It is slightly more evident from this plot, that british constructors have been more dominant than anyone else.
I am going to analyze two density plots:
This plot shows the distribution of race wins over 1950s-2017.We can see the winners of race comes from a very narrow distribution of constructors. This distribution expands as we go from ‘Top 1’ to ‘top 3’ to ‘top 10’ finish.Now let’s see this density plot for drivers:
so , this has been my analysis for plot for the F1 championship, I would like to add one more plot for Most number of wins by Driver encoded with their nationality:
So, I stumbled across this dataset and it was way more intresting and huge than my previous movie dataset.So , after a day and half of continuous tinkering this is the result I got. Hope you guys will like it.
In next post, Probably I would be coming with an another interesting dataset.
Till then 🙂
Black as My Coffee 