2021 Season quali battles: Rounds 1 to 8

We already have 8 races in the bag, so let’s take a look at how the intrateam quali battles are developing so far in this 2021 Formula 1 season. I will make this article every one or two races so that you can stay updated on how the drivers are faring against their teammates so hopefully, you will enjoy it.

As a side note, I am not feeling very motivated right now. Sometimes it’s just hard to keep time-consuming projects like this going. Because of this, I’m not doing an analysis this week. The post still has all the charts that you like so hopefully you still enjoy this article.

Methodology

This analysis is quite similar to the one about Perez that I did a while ago, but comparing performance between teammates for all the teams. As before, the main challenge is comparing performance between drivers in different tracks. Some tracks will have higher dispersion in quali times, while in some tracks you will see very similar lap times all across the grid. To overcome this issue I decided to standardize the data.

The process is quite simple. First, I obtained the average time done by all the drivers in every quali session analyzed. Eg, for the 2021 Emilia Romagna GP, I obtained the average lap time done in Q1, Q2 and Q3. This includes the lap times done by every driver who participated in each session. Second, I subtracted this average time from all the lap times done in that session. Finally, I divided that resulting number by the standard deviation to end up with the standardized lap time.

An example of the standardization process is shown in the table below. If you like formulas, this one should help you to understand a bit better how this process works.

The second challenge with this analysis is how to compare performance when not all drivers qualify to all quali sessions. For example, I can’t compare the lap time of a driver in Q3 vs the lap time of his teammate in Q1. These numbers are just not comparable due to track evolution. The reality is that there is no proper way to do this. Due to this problem, I only compared quali sessions that both teammates had in common. Eg, I only compared the Q1 and Q2 sessions of Lando Norris and Daniel Ricciardo from the Monaco GP. This means I compared Lando’s best time in Q1 with Daniel’s best time in Q1 and got the delta between them. I did the same for Q2. Since Danny Ric didn’t qualify for Q3, I didn’t utilize Lando’s lap time in Q3 for this analysis.

An analysis like this has the main limitation of not telling you if a driver is consistently missing qualifying to Q2 or Q3. I decided to represent this data with a simple bubble chart that I will show in a while.

Finally, if you want to take a look at the detailed data, I will add a table with all the information at the end of the article.

Standardized lap times

Interpretation

How do we interpret this chart? Some important pointers must be considered. First, the x-axis has no traditional units. The units shown here are not seconds or milliseconds, but standard deviations above or below the mean. Second, a standardized lap time of 0 means that it was exactly the average time done during that particular quali session (Q1, Q2 or Q3). Negative times—dots shown to the left of 0—represent faster lap times than the mean (this is good!), while positive times represent slower lap times than the average.

The gradient bands shown stand for high or low-density areas. Darker areas show where most of the laps were recorded, while lighter areas show the opposite.

The horizontal coloured lines represent the 66% and 95% quartiles. The thick line around the middle contains the middle 66% of the laps, while the thinner line spans 95% of the lap times done by each driver. Laps outside any of these two intervals could be considered as anomalies, that is laps that were perhaps a bit faster or slower than you would normally expect. 

Standardized lap times: Session by session

The following table presents the information that has been shown before, but on a quali by quali session basis. Every quali session was considered to be a battle, with a driver crowned the winner by a certain margin (delta).

How is this different from the previous chart? The previous analysis considered the whole season to be a battle, while this chart considers each session as an individual competition. I am a fan of the season-long analysis, but the more granular race by race, session-by-session analysis provides some interesting information about the performance of the drivers.

Just as before, there is no way to compare the performance of the drivers if one of them classified to Q2 or Q3 while the other one didn’t. Because of this, you may not see a driver in any of these two columns even though he advanced to that particular session. I can’t declare a winner between for example Perez and Verstappen at the Q3 session of the 2021 Emilia Romagna GP since Perez failed to advance from Q2 to Q3.

Remember that the units that are being used here are NOT seconds, but standard deviations. These units are harder to interpret than pure seconds, but allow you to compare performance between different tracks. Seconds are easier to interpret, but the comparison between different tracks is misleading at best, so I decided not to use them for this analysis.

Appearances in quali sessions

Interpretation

This is a quite simple chart to interpret. Each big bubble represents a team. The bubbles inside each big bubble represent each driver in each team. The little, medium and slightly larger bubbles inside show the appearances in each quali session of the season. Take a look at the size of Daniel’s bubble compared to Lando’s bubble. As you can see, it is smaller due to him having only three Q3 appearances, compared to Lando’s five.

If you want a hi-res png image of this chart just click here.

Detailed data

This is a simple table that should be pretty much self-explanatory. The table shows the number of appearances in each quali session, the average position in each particular session, and a tiny sparkline chart with the range of the previously mentioned average position.

Final remarks

 I hope you have enjoyed this post. Working on articles like this one is no easy task, and takes quite a bit of time and effort. If you enjoy visiting my site, please consider sharing my posts on social media or with friends and family. If you want to support me, I would also greatly appreciate a donation.

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