How F1’s Red Bull Racing Uses Simulations to Make Mid-Race Calls

Formula 1 fans love to talk about speed, bravery, and tire smoke. Fair enough. That is the glamorous part. But many Grands Prix are actually decided by a less cinematic hero: the laptop. Or, to be more precise, a brutally sophisticated stack of simulations, live telemetry, predictive models, and human judgment that turns chaos into a race-winning call.

Few teams have become more closely associated with that approach than Oracle Red Bull Racing. When a Virtual Safety Car appears, when tire wear suddenly looks friendlier than expected, or when a rival dives into the pits two laps earlier than planned, Red Bull does not just guess and hope for the best. The team updates simulations in real time, compares strategic branches, weighs risk against track position, and then makes the kind of mid-race decision that leaves rivals muttering into their radios.

In other words, while fans are yelling, “Pit now!” from the couch, Red Bull is busy running enough virtual races to make a supercomputer ask for coffee.

What “Simulation” Really Means in Modern F1

In Formula 1, race strategy is not a single plan written on a whiteboard Sunday morning. It is a living model. Before lights out, teams build a primary strategy, several backup plans, and a long menu of contingency options for weather changes, tire degradation, traffic, yellow flags, Safety Cars, Virtual Safety Cars, slow pit stops, or rivals doing something wonderfully inconvenient.

For Red Bull, simulations are central to that process. The basic goal is simple: create a realistic model of how the race could unfold, then rerun that model again and again while tweaking variables. If tire degradation is worse than expected, what happens? If the leading rival stops on Lap 16 instead of Lap 19, what happens? If a Virtual Safety Car appears at exactly the wrong moment, who gains? If your driver loses three places at the start, can a one-stop still work?

That is where Monte Carlo simulation comes in. In plain English, it means running a giant number of plausible race scenarios with slight differences in assumptions, then studying the range of outcomes instead of pretending motorsport behaves like a neat school math problem. Racing does not. Racing behaves like a caffeinated squirrel with aero sensitivity.

Red Bull’s technology partnership with Oracle has pushed this process to an extreme. The team has said it can run around one million Monte Carlo simulations per second during races, while broader reporting and interviews around the program have described billions of simulations across a race weekend. Max Verstappen even said Red Bull runs more than eight billion simulations before a race begins. That does not mean the team knows the future. It means the team gives itself a much better map of possible futures.

The Data Red Bull Feeds Into the Model

A simulation is only as useful as the data it eats. In F1, that buffet is enormous.

Tire Behavior

Tire performance is the heart of race strategy. Teams model degradation, warm-up speed, graining risk, stint length, and how quickly a new set will switch on after a stop. They also factor in the sporting rule that, in a dry race, drivers normally must use at least two different dry-weather compounds. That means strategy is not just about whether to stop, but when to stop, on which compound, and against which rival.

Pit Stop Time Loss

A pit stop is never just a 2-second tire change. Strategy models care about total pit-lane time loss: slowing for entry, driving through the lane, stopping, accelerating out, and then rejoining in traffic. A brilliant tire call can become a terrible race call if the car rejoins behind a train of slower traffic.

Traffic and Track Position

This is where Red Bull’s models become especially valuable. A pit stop may look good on paper, but not if the driver rejoins behind three cars that are slower overall yet impossible to pass without cooking the tires. Track position still matters enormously in F1, especially on circuits where overtaking is awkward, narrow, or about as enjoyable as assembling furniture without instructions.

Safety Cars and Virtual Safety Cars

These are the strategy grenade pins. Under neutralized conditions, the relative cost of a pit stop falls because the field is traveling more slowly. That can transform the value of stopping right now versus staying out. The simulation engine updates instantly to test the new race shape.

Rival Pace

Strategy is never built in a vacuum. Red Bull’s race models account for how quickly Ferrari, Mercedes, McLaren, or any nearby competitor is likely to run on each compound and at each stage of a stint. The question is not “What is our fastest race?” It is “What is our fastest race against them?” Those are very different questions.

How Red Bull Makes the Mid-Race Call

One of the most interesting parts of Red Bull’s operation is that the strategy process is split between the pit wall and the operations room back at the factory. Hannah Schmitz, Red Bull’s Principal Strategy Engineer, has described a setup in which strategists running live calculations and simulations feed actionable information to the person making the pit-wall call. The flow is designed to be fast enough that it feels almost like one room, even when it is not.

That matters because race strategy is both technical and brutally time-sensitive. Will Courtenay has explained that when the Safety Car comes out, you need the answer immediately. Not in 30 seconds. Not after a philosophical debate. Immediately.

So the workflow looks something like this: the team receives live telemetry, timing loops, weather trends, tire signals, and competitor movement. The simulations are updated. Possible branches are ranked. Engineers compare what the math likes, what traffic allows, what the driver can realistically execute, and what the rival is likely to do next. Then a strategist gives a recommendation. The pit wall decides. The driver hears the call. And millions of fans decide they knew the answer all along.

Why Undercuts, Overcuts, and Pit Windows Matter So Much

Formula1.com’s own strategy explainers have long shown how tiny timing differences can change a race. The undercut happens when a driver pits first and uses fresh tires to gain time against a rival still circulating on older rubber. The overcut flips that idea by staying out longer and making time while the rival tries to bring fresh tires up to temperature.

Red Bull’s simulations help determine whether an undercut threat is real or fake. Sometimes a rival pits and the computer essentially says, “Relax, their out-lap won’t be strong enough.” Other times the model is screaming, “Pit now or enjoy watching the replay later.”

That is also why modern F1 strategy graphics from AWS focus so heavily on pit windows, predicted pit stop strategy, undercut threat, and alternative strategy paths. Those are not just TV toys for fans. They are public-facing versions of the deeper logic teams use internally: race pace plus tire life plus traffic plus probability.

Case Study: Canada 2022 and the VSC Gamble

One of the clearest examples of Red Bull using live simulation to make a decisive call came at the 2022 Canadian Grand Prix. A Virtual Safety Car appeared early, and Red Bull had to decide whether pitting Max Verstappen would sacrifice valuable track position or create a long-term tire advantage worth the trade.

According to Will Courtenay’s description of the moment, the simulations showed that if Verstappen pitted under the VSC, Red Bull would likely give up the lead temporarily but recover it later because Carlos Sainz would struggle more on tire life. Red Bull made the stop. The prediction held. Verstappen reclaimed control and won.

That is a textbook example of simulation doing what great strategy tools are supposed to do: not eliminate uncertainty, but give the team enough confidence to choose the higher-value branch of the race tree.

Case Study: Miami 2022 and the One-Stop Pivot

Miami in 2022 offered a different kind of challenge. As a new venue on the calendar, tire behavior contained more uncertainty than usual before the race began. That matters because pre-race models are only as good as the assumptions beneath them. If the track surface surprises you, the plan can age badly in a hurry.

Once the race started, Red Bull saw that the tires were holding on better than expected. Instead of stubbornly clinging to an outdated plan, the team fed the new information back into its model and quickly converged on a one-stop approach. Courtenay later described that process as crucial because, in the middle of uncertainty, the simulation gave Red Bull confidence not to panic.

That is a revealing phrase. Strategy is often sold as intelligence. In practice, its real value is composure. A good model stops a team from making an emotional call just because the situation feels tense.

Case Study: Zandvoort 2022 and Matching, Then Beating

The 2022 Dutch Grand Prix showed another layer of strategic strength: not simply inventing something clever, but reacting faster and cleaner than everyone else when the race twists. In broad terms, Red Bull matched Ferrari’s earlier stop and then handled later neutralizations more effectively as the race opened up through a VSC and then a full Safety Car.

This is where simulations are especially useful. They allow a team to compare “copy the rival,” “cover the rival,” “attack the rival,” and “ignore the rival” in near real time. That sounds simple when written down. It is not simple at 190 miles per hour with a championship at stake.

The Human Factor Still Runs the Show

Here is the part some people miss: Red Bull is not winning races because a machine presses the pit button. Simulations do not replace strategists. They make strategists sharper.

Hannah Schmitz has described strategy as an exercise in adaptability and fast reaction to live scenarios. That should tell you everything. The models supply probabilities, but humans interpret context. A strategist still has to ask the hard questions. How trustworthy is the tire data after three strange laps in dirty air? Is the rival bluffing with an early stop? Will the driver be able to pass the car ahead quickly enough to justify this plan? Is track position king today, or is tire delta king?

Sports remain gloriously resistant to full automation. Fortune put it well when describing F1’s cloud-heavy world: data does most of the heavy lifting, but the human factor still matters. In Red Bull’s case, that human factor is not cosmetic. It is the final filter between insight and action.

Why This Matters Beyond Red Bull

Red Bull’s simulation-driven strategy is not just an interesting quirk of one successful team. It is a snapshot of where elite motorsport has gone. Cars now generate huge streams of telemetry from hundreds of sensors. That information reaches engineers in real time. Teams model not just their own race, but the probable race of everyone around them. Strategy rooms work like mission control centers. And cloud infrastructure matters because speed of computation matters.

In old-school racing mythology, the winning call came from a grizzled strategist staring at the sky and making a gut decision. Modern F1 is less romantic and more ruthless. It is gut plus models. Experience plus live data. Instinct plus simulation. The teams that combine those elements best make fewer bad calls and capitalize faster when the race shape changes.

Red Bull has become one of the best examples of that balance. The team’s edge is not that it magically knows everything. The edge is that it can test more possibilities, react faster to new information, and commit to a decision with greater confidence than many of its rivals.

Experiences From the Strategy Pressure Cooker

If you want to understand why Red Bull’s simulation-driven approach feels so powerful, imagine the race from three different viewpoints at once.

First, picture the driver. He is not seeing a spreadsheet. He is managing brake temperatures, battery deployment, tire grip, dirty air, mirrors, radio chatter, and the possibility that someone is about to lunge into Turn 1 like they are auditioning for chaos. He does not have time to think in ten strategic branches. He needs one clear instruction: push, box, extend, defend, switch tires, target this lap time, or save the tire. For the driver, the genius of the strategy team is not complexity. It is clarity.

Next, picture the pit wall. This is the public face of the decision-making process, the part TV cameras love to capture. Headsets on. Screens glowing. Expressions serious enough to suggest someone has just announced the moon is leaving. But the pit wall is only effective if the information arriving there is filtered, ranked, and trustworthy. A strategist cannot dump twenty options on the race engineer and say, “Good luck, bestie.” In the heat of a Grand Prix, usefulness beats volume every single time.

Then picture the operations room back at the factory. This is where modern F1 starts to resemble aerospace mission control. Strategists are scanning live numbers, rival radios, timing sectors, onboards, weather signals, and model outputs while the race keeps moving without sympathy. A call that is brilliant on Lap 21 can be outdated by Lap 22. That is why Red Bull’s approach feels less like drawing a map and more like redrawing the map while the car is still driving over it.

For fans, this creates a strange and thrilling experience. You can watch a Red Bull race and feel, in real time, when the team is preparing something. The radio goes calm. The pit wall goes still. The broadcast starts talking about an undercut threat. And then, suddenly, the car dives in at exactly the moment that looks either genius or insane. Sometimes both. A few laps later, the picture sharpens: the rival rejoins in traffic, or the new tires switch on instantly, or the Safety Car falls at the perfect moment. That is when strategy stops being invisible.

There is also something oddly relatable about it. Red Bull’s simulations are extreme, but the principle is familiar. Every high-pressure decision gets better when you prepare for multiple outcomes before the pressure arrives. That is really what these race tools do. They reduce panic. They let the team say, “We have seen versions of this before. We know the likely consequences. Now act.”

And yet, even with all the data in the world, F1 remains wonderfully human. A driver can lock up. A pit stop can go half a second long. A yellow flag can appear in the worst possible corner. A rival can do something nobody modeled because, occasionally, sport still enjoys laughing at math. That is exactly why Red Bull’s style is so fascinating. The simulations are not there to create certainty. They are there to help the team stay smarter than the chaos for just long enough to reach the checkered flag first.

Conclusion

Red Bull Racing’s mid-race strategy is not magic, and it is not guesswork dressed up in corporate jargon. It is a fast, disciplined system that combines live telemetry, predictive simulation, race-rule awareness, tire modeling, and experienced human decision-makers. The team runs enormous numbers of virtual race scenarios, updates them as conditions change, and turns those outputs into real-world calls under real-world pressure.

That is why a Red Bull pit decision can look uncannily calm in the middle of bedlam. While the rest of the paddock is reacting to the race, Red Bull is often reacting to a thousand possible versions of the race at once. And in modern Formula 1, that can be the difference between finishing second with a polite shrug and winning with everybody else asking, “How did they know?”