2019 Preseason Analysis

With less than a week until the 2019 cars hit the track in Melbourne, let’s take a careful look at the preseason testing data and what we can expect from the latest driver match-ups. Owing to time constraints, you have my apologies for this coming a little later than usual. Hopefully better late than never!

Preseason testing

In past years, I have used a method to analyze long runs from testing that has proven to be quite accurate for predicting the within-in season pecking order. You can find this method described in detail is last year’s preseason analysis post. To briefly summarize the steps:

1. I collected stints that were known to be part of a race simulation. Since these stints were strung together by pit-stops only for tyre changes and typically ran close to a full race distance, the fuel loads are known. These stints can be used to anchor the data for stints run at unknown fuel loads. For comparing stints, I estimated the equivalent pace on a full fuel load (70 laps of fuel).
2. I collected all other long runs for which the tyre compound was known. Stints were cleaned of slow laps (>1 sec/lap slower than laps on either side). I required a minimum of 10 laps with valid times for inclusion in the analysis.
3. I derived degradation-time curves for each team on each tyre compound. These curves show the relationship between lap time and how hard a driver is pushing (i.e., degrading the tyre compound, as evidenced by worsening lap times). This step is critical, as it allows comparison of stints with variable work rates of tyre compounds. This is the step that is missing from all other online analyses I have found; without it, one may be comparing a stint where a driver was pushing very hard to one where a driver was extracting very little life from the tyres.
4. Finally, I attempt to estimate the unknown fuel loads by finding estimates that give the best match to the degradation-time curves for each team.

Of course, even after all this, we may still be comparing cars with differing set-ups, track conditions, and engine modes. But by compiling enough stints, we can hopefully begin to divine the overall trends, and spot outliers. It’s not perfect, but it’s about the best we can do as outsiders, without access the teams’ internal data.

As in previous years, @f1debrief on twitter was a superb resource for collecting key long runs in 2019 preseason testing. This year I also had considerable help from a colleague in collating the data from the live timing, without which I would not have had time to complete the analysis before the first race.

Overall trends

Below, I have plotted the degradation-time curves for the two most used compounds in testing: the C3 compound (softer) and the C2 compound (harder). Note that, given the huge changes in the Mercedes design from the first to second test, I used only their second test data, which was still a reasonably sized dataset.

To read these graphs, the x-axis represents the rate of tyre degradation (in seconds per lap, after adjusting for fuel burn), while the y-axis represents the base lap time (i.e., the predicted lap time on a fresh set of tyres) after adjusting for the estimated fuel load. Note that curves always slope down, meaning that the harder a driver pushes, the lower (faster) the base lap time and the higher the degradation rate. The curves for different teams have different vertical offsets, representing differences in their base lap times on a particular compound. These offsets represent differences in pace between the teams, which are discussed and analyzed in detail for each team below.

 

Degradation-time curves for each team. Colors for each team are defined in the legend. Lines represent best-fitted curves for each team. Individual points represents individual long runs (at least 10 valid lap times after cleaning). Stints from race simulations are plotted as large squares. Stints with unknown, deduced fuel loads are plotted as small circles. All y-axis values correspond to estimated pace on a full fuel load (70 laps of fuel).

Let us now run through the individual teams, their performance estimates, and the potential sources of uncertainty in these estimates.

 

Williams: Clearly struggling

There is unfortunately very little positive to be said about Williams’ performance at this stage. They came into preseason testing with difficulties and delays, before parting ways with their technical director, Paddy Lowe. They are likely also feeling the ramifications of the Stroll family money shifting over to Racing Point. The 2018 season was a tough one for Williams, and 2019 is looking like it might be even tougher.

On the basis of their race simulations and other stints, Williams appear to be currently 3.2 seconds adrift of the leaders and over a second adrift of the next slowest team. Williams will hopefully make some developments in the early season to catch up, but for now it’s looking like it could be a back-row lock-out in Australia.

The lower midfield

Getting into the midfield, we can expect some intense inter-team battles this year. By my estimates, the 5th through to 9th best teams are spanned by ~0.7 seconds. Looking at the slower end of this range, we have Racing Point and Toro Rosso. The differences between these two teams and others are small enough that this hierarchy could be largely track dependent. As I noted last season, what was quickest at Catalunya was not necessarily quickest at other circuits. They are clearly within potential fighting distance of other teams, so I expect these teams to battle their way into the points.

In the case of Toro Rosso, we have a couple of confirmed race stints on the C3 tyre to reliably place them around 1.7 seconds slower than Ferrari. In the case of Racing Point, there is less to go on, but based on plausible fuel loads they appear to be somewhere in the same range; I estimate 1.9 seconds slower than Ferrari with their current package.

The upper midfield

Team ranks 4th through 7th look like they will be fiercely fought in 2019. These four teams in particular show the importance of gathering a large dataset to examine trends. If we base the whole analysis on only one unrepresentative stint, we can potentially come to extreme conclusions, which may explain the diversity of team rankings in the midfield across other websites.

Renault completed impressive mileage, but none of their long runs were confirmed race simulations, making it difficult to anchor their fuel loads. Their longer runs were almost always punctuated by stops in the garage for minutes or longer, meaning they may not have been running at race stint fuel loads. I found that the best fit to most of the stints, adjusting for likely fuel loads, put them consistently 0.8-1.2 seconds behind Ferrari across both the C2 and C3 compounds. But I acknowledge that this is an uncertain estimate for now. In their analysis of preseason testing, AMuS concluded that Renault are currently 2nd best, between Ferrari (1st) and Mercedes (3rd). If this were the case, Ricciardo would actually be looking at an upgrade from Red Bull! Examining the data, however, I find this difficult to reconcile with the slower pace of several of Renault’s longer runs, including one on day 6.

Alfa Romeo are another difficult case to judge. Overall, most of their runs appear to put them around 1.0-1.5 seconds behind Ferrari. However, they had at least one very impressive stint on the C3 compound, which appears on the red Ferrari curve in the C3 graph above. Here are the individual lap times for this particular stint:

We can see that, although most of the stint was indeed very fast, there was a notable three-lap slow period in the middle. These lap times were cleaned out of the stint in my analysis (exclusion denoted by black dots). This slower period would have allowed the driver to regain battery charge and lower temperatures, enabling a subsequent period of faster laps, possibly in a higher engine mode. Rather than a typical race stint, this may be a simulation of the type of running needed to pull a gap in a critical phase of the race, or just testing out system tolerances. To me, it doesn’t look like a representative race stint, explaining why it is such an outlier.

For Haas, we see a considerable variation in lap times between the tyre compounds. Like for Alfa Romeo, we can see some very impressive times, this time on the C2 compound. Let’s take a look at the quickest C2 stint:

Note that, unlike the Alfa Romeo stint, this one is extremely stable and fast, with a base lap time at the beginning of the stint of around 80.9 seconds. This looks much more like a representative race stint, given its consistency and length. Given the stint ran for 25 laps, the least generous interpretation is that the car was fueled for only 25 laps. An equivalent pace on 70 laps of fuel at Catalunya is about 2.4 seconds slower, meaning a fuel-adjusted base lap time of 80.9+2.4=83.3 seconds. The degradation rate (fuel-corrected slope) is only 0.09 seconds per lap, which places this stint only a few tenths slower than Ferrari on equivalent race stints. If we look at this stint alone, Haas are possibly challenging Red Bull.

We must, however, somehow reconcile this with the pace of the C3 stints, which, even under the most generous interpretation for fuel loads to Haas, were 1.4 seconds adrift of Ferrari. The performance of the Haas machine is therefore one of the most intriguing questions going into the season. If they are only able to extract maximum performance on the harder tyre compounds then we may see them shine brilliantly at certain tracks and struggle at others. Haas appear to have an interesting concept, at the very least.

Finally, what to make of McLaren, who have been ranked everywhere from 3rd quickest to 9th quickest. Have they bounced back from last year’s very difficult season, where they were at times slower than Williams? Looking at their race simulations on the C2 tyre, they certainly look more competitive this year than last year. If we use those stints to anchor the other stints of unknown fuel load, we see that McLaren are usually trailing Ferrari by about 0.7 seconds. There are, however, four stints on the C2 tyre that are much slower — over a second slower than this. Which are the more representative stints? The quick ones, which place McLaren at the pointy end of midfield, or the slow ones, which place McLaren down in lower midfield? McLaren’s running on the C3 tyre is consistent with either interpretation, and unfortunately we have no C3 race simulations.

Some important insight is gained by comparing McLaren’s performance in the first test to their performance in the second test.

We can see from this that all of McLaren’s slower stints, and much of their variability, came from the first test. Once they had everything checked and functioning, McLaren’s pace was consistent and more impressive in the second test. These results suggest that McLaren should be capably fighting in the upper midfield this season.

The top three

Last year, Formula 1 was functionally divided into two competitive classes: F1 (Mercedes, Ferrari, and Red Bull), and “F1.5” (everyone else). This year, it looks to me as though the gap between these classes has reduced, blurring the lines between them. Red Bull in particular may be under threat from teams in the upper midfield, although their pace was quite difficult to pin down due to Gasly’s crash early into his race simulation, not to mention their need to experiment with a new powerunit.

At the top of the pile, Ferrari and Mercedes look again to be emerging as the key championship competitors. Last year, I concluded Mercedes had the edge coming out of preseason testing. This year, I have to say that Ferrari look strongest of the pair. On comparable C3 stints, we see Ferrari leading Mercedes by about 0.3 seconds. On the C2 stints, the gap is about half of that.

Below, I have plotted the lap times from the two closest comparison stints between Ferrari and Mercedes. The Mercedes stint was a simulated first race stint started by Bottas around 2pm on day 7. The Ferrari stint was a simulated first race stint started by Vettel around 2:30pm on day 8. The two stints were therefore under near identical conditions.

Across laps 1-16, the Ferrari averaged 0.3 seconds faster than the Mercedes. This is a competitive gap that a driver could potentially bridge, though with difficulty. Mercedes have a development battle ahead of them, but they have proven themselves very capable in this regard in past seasons.

To summarize the above data, here is a graphic showing the estimated gaps to the front for each team, given the best fitted curves and associated degree of uncertainty.

Colored blocks represent ranges of uncertainty for each team.

Intra-team battles

One of the fascinating things about the 2019 season is the number of new driver teammate pairings. Of the ten teams, only Mercedes and Haas are running the same driver line-ups in 2019 as in 2018. This season will therefore be a fantastic opportunity to better understand the driver hierarchy in F1 today.

Before we see the results of those match-ups, let’s ask my driver performance model what it predicts. For each match-up, the model takes into account the drivers current age and prior F1 experience. The model also takes variance into account, generating probabilistic estimates, with the answer representing the percentage likelihood that the driver will have higher points per counting race (excluding mechanical DNFs) than their teammate across the season.

Whereas last year I simply gave no predictions for rookies, this year I’ve used an approach to estimate each rookie’s probable performance. Specifically, I searched for other drivers with similar rookie age (within 5 years), and similar scores on my junior career metrics of achievement (within 30 points) and excitement (within 5 points). I then used their performances to generate a proxy distribution of performances, applying the appropriate corrections for age and experience to match the actual rookie in 2019.

Let’s start with a controversial prediction! Given his meteoric first season, the model sees Leclerc (with the benefit of additional age and experience) potentially being more than a match for Vettel, with an almost 2 in 3 chance of coming out ahead. I’m personally more circumspect, as I think Ericsson’s uncertain rating may have inflated Leclerc’s status, as I noted last year. In any case, this will be a fascinating match-up.

Hamilton has looked firmly in control at Mercedes since Bottas joined the team. The model sees that scenario as being very likely to continue.

Gasly showed serious promise in 2018. However, facing Verstappen is a tall order for any driver, especially given he is clearly the favored driver within the Red Bull team. The model sees Gasly having about a 1 in 5 chance of outperforming Verstappen in 2019.

The Magnussen vs. Grosjean battle will continue into a third consecutive season at Haas. Grosjean still seems the quicker driver when on form, but his erratic performances across a season tend to always cost significant points. The model sees Magnussen as the slight favorite here.

This is the most one-sided prediction on the grid. There are two factors contributing to this: (i) although the model’s ranking of Sainz was downgraded last season, it still remains relatively high; (ii) historically, rookies are very unlikely to outscore their teammates. Norris, who was a very talented junior, surely rates his chances higher than this.

This one is surely the most mouthwatering new pairing on the grid. How does Hulkenberg measure up to Ricciardo, a driver who has a strong claim to being among the  sport’s elite? As the model sees it, this one could easily go either way!

Experience vs. youth should make for an intriguing battle at Alfa Romeo. The model views Raikkonen as the favorite, with a 1 in 4 chance of Giovinazzi prevailing over Raikkonen.

Albon and Kvyat are both receiving recalls from the Red Bull program to drive at Toro Rosso. Kvyat has more F1 experience and undoubtedly the better junior career, but has he mentally recovered from his previous demotion and sacking? If he has, expect Kvyat to take the lead at Toro Rosso.

The experienced and very capable Perez is likely to give Stroll a much sterner test than Sirotkin did last year. The model sees Stroll as having a 1 in 3 chance of outperforming Perez; that number is probably inflated due to Stroll’s large rating gain from the result of Baku 2017, as I noted last year. Perez is surely the favorite in this one and the match-up should help to clarify Stroll’s actual level.

Kubica, on comeback, will face Russell, the reigning F2 champion. Under the model’s assumption of no effect of Kubica’s injury, it sees Kubica’s age and talent counting for enough to come out ahead. In reality, we will have to see if the injury matters.

 

And with that, I wish you an enjoyable 2019 season!

Tags: Alfa Romeo, Charles Leclerc, Daniel Ricciardo, Ferrari, Haas, Lewis Hamilton, Max Verstappen, McLaren, Mercedes, Nico Hulkenberg, Pirelli, Red Bull, Renault, Robert Kubica, Sebastian Vettel