Qualifying teammate battles seem to be of great interest to the current F1 community. While I’m a firm believer that Sundays matter far more than Saturdays, I can’t deny the growing importance of qualifying—especially with how tough overtaking has become with the latest generation of F1 cars. Track position is king, and starting ahead often means staying ahead. So while race day is where the points are handed out, Saturdays are playing a bigger role than ever in shaping the final results.
Analysis
Click to expand methodology
Methodology!
As with race pace, we can’t directly compare qualifying pace between races. Different tracks, lengths, and deltas make it tricky. To handle this, I standardized the data using a metric called symmetric percent difference. Without getting too technical, it’s a more robust way of calculating percent differences — hence why I chose it.
I calculated the symmetric percent difference for all qualifying sessions between teammates, keeping only the maximum session where both drivers participated. For example, if George Russell made it to Q3 but his teammate only reached Q2, I used the Q2 data for the comparison. If a driver couldn’t set a lap time in Q1 while their teammate did, I removed that session entirely. While this isn’t ideal, using equally comparable data points is crucial for a fair performance comparison. Negative symmetric percent difference values mean that a driver was faster than his teammate, while positive values mean that the driver was slower than his teammate. A difference of 0% means that both drivers were just as fast.
I calculated the values for each race for each team and plotted them as individual data points in the chart. I then calculated the median of these values for the season (so far) and displayed it the left side of the plot, next to the team logo. Smaller overall values represent that both teammates were more evenly matched during quali, while larger overall values show a greater gap between teammates.
Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.
Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.
Issues!
One of the main issues when gathering data from multiple races is that the deltas will change depending on the length of each track. A delta of 0.1 seconds in a short track (say, 1:05 per lap) will be greater than a delta of 0.1 seconds in a long track such as Spa (~ 1:45).
One way we can standardize the data is by converting the deltas to percentages, but there is one big issue with this. The traditional way of calculating a percent difference is with the following formula:
$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$
The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.
$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$
You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.
One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:
$$ Symmetric\ percent\ difference = 100\times\frac{value1-value2}{(value1+value2)/2} $$ This formula is reversible, meaning that regardless of the order of the values, we will get the same result. Because of this, I decided to use the symmetric percent difference formula as the basis for the analysis.
Quali delta between teammates
We’re already one quarter into the season. With 6 races, and 2 sprints, we now have more representative results. Just as a reminder, this season, we have the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a little trickier. Normally, I’d just use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.
To counter that, I’ve decided to include both the median and the mean qualifying deltas in this article. This approach might change in future posts, but for now, I think it gives us a more complete picture.
The results can change dramatically by switching between the mean and median as the key metric of interest. These numbers will eventually stabilize and converge as the season goes on.
Symmetric percent difference
Looking at the median symmetric percent difference, the biggest gap between teammates is at Red Bull Racing. Right now, Max Verstappen is beating Yuki Tsunoda by 0.968%. That’s now a larger difference than the one between Max and his previous teammate, Liam Lawson.
If we look at the mean symmetric percent difference instead, the largest gap shifts to Verstappen vs Lawson, with Max ahead by an average of 1.034%. I originally assumed that comparison would stay the biggest all season, but now I’m not so sure. Verstappen is currently outpacing Tsunoda by an average of 0.977%, which isn’t far off the gap to Lawson.
At the other end of the spectrum, the smallest delta is at McLaren—just 0.013% based on the median, or 0.109% based on the mean.
Overall, most teammate pairings have been fairly competitive so far, with a few exceptions—like Russell vs Antonelli, Hadjar vs Lawson, Gasly vs Doohan, and of course, Verstappen vs Lawson and Verstappen vs Tsunoda.
Delta in seconds
As a new addition, I’ve included an analysis using seconds instead of the symmetric percent difference. I’m still firmly in the “symmetric percent difference is more representative” camp, but the difference between the two metrics is smaller than most people think—and time in seconds is a lot easier to interpret for most people.
The results are pretty similar to the percent-based version, with just a few small changes. Looking at the median delta, then the results are very similar as the percent-based ones, with the smallest gap found at McLaren, at just 0.012 seconds. If we, instead, look at the mean delta in seconds, the smallest gap is at Williams, with Alex Albon leading Carlos Sainz by just 0.1 seconds on average.
The biggest deltas are again at Red Bull. Right now, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.88 seconds—just over half a tenth more than the gap between Max and Lawson, and by a mean of 0.864 seconds.
Qualifying stage appearances
This chart was exported in very high resolution. If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.
Click to expand explanation
I’ve often seen tables showing how many times drivers reached Q1, Q2, and Q3, but I’ve never been fully satisfied with them. Tables tend to have too much text while still missing key insights. To address this, I created a bubble chart.
Each bubble represents a driver’s qualifying or sprint qualifying appearance throughout the 2025 F1 season. The bubble’s size reflects the driver’s final qualifying position, with larger bubbles indicating better results. The actual position is displayed as a big number inside the bubble, and the colors indicate the qualifying session reached.
As of the sixth race of the 2025 season, the biggest gap in qualifying performance is still at Alpine. Pierre Gasly has reached Q3 three times, while Jack Doohan hasn’t made it past Q2 yet.
Most of the other teammate battles have been pretty even so far. Oliver Bearman continues to hold his own against Esteban Ocon—a driver known for his strong one-lap pace. Matching Ocon in qualifying is no easy feat, which makes Bearman’s performance all the more impressive.