Formula 1 does not use machine learning as a crystal ball that knows exactly how many laps remain in a tire. It combines historical race patterns with live timing, tire, position and race-condition data to estimate how pace may change and when a pit stop could produce a net advantage. The result is a moving probability or window, not an automatic order that a driver must follow.
Public Formula 1 and AWS explanations show the basic loop: establish a historical baseline, measure the current race, estimate the trade-off between worn and fresh tires, then recalculate when rivals stop or a safety car changes the value of track position. Teams add confidential telemetry, simulations and human judgment that are not disclosed in the public broadcast graphics.
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What tire degradation means for an F1 strategy
Tire degradation is the loss of performance as a tire completes laps. A degrading tire can still be safe and usable, yet produce slower lap times because its grip and energy behavior have changed. That matters strategically: staying out preserves track position and avoids pit-lane time, while stopping gives the driver a fresh tire but costs time during the stop and may lead to rejoining behind slower traffic.
A pit window is therefore conditional. It opens when the expected gain from fresher rubber and better pace is large enough to outweigh the pit-lane loss, traffic risk and the possibility that a rival gains position. It can close again after a rival pits, a driver encounters clean air or a safety car changes the time cost of stopping.
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How does F1 predict tire degradation?
The public descriptions support a four-stage prediction loop. They do not disclose every feature, model architecture or proprietary calibration used by an individual team.
1. Build a baseline from past races
Historical races provide examples of how compounds, stint length, pace and strategy interacted in different conditions. In its 2018 announcement naming AWS its Official Cloud and Machine Learning Provider, Formula One said its data scientists were training deep-learning models with more than 65 years of historical race data. That figure describes the dataset referenced in that 2018 announcement, not a verified current total. The announcement says, “For example, Formula 1 data scientists can predict when the window of opportunity is opening and closing for teams to pit their cars for maximum advantage, as well as determine the best timing for changing tires.” Formula One’s 2018 announcement presents this as an example of the intended capability, not a guarantee for every race.
2. Observe the current race
Live data describes what is happening now rather than what usually happened in the past. AWS says the public Pit Window insight uses tire compound, lap times and the spread of cars. Car position, gaps, pit-lane timing and the pace of nearby competitors give the estimate context. AWS also described, in a circa-2020 technical article, a stream of 300 sensors per car producing 1.1 million data points per second sent from the car to the pit. Those are figures from that article and are not a verified current sensor count.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →The public material does not list every sensor channel used for each insight. It is safer to describe the stream as a high-rate source of telemetry and timing information than to claim that a particular broadcast graphic consumes every sensor measurement.
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3. Estimate pace and strategic trade-offs
The system estimates how tire condition may affect future lap pace and compares possible stop timings. A fresh set can improve pace, but the benefit depends on compound, track temperature, traffic and the driver’s ability to use it. A stop can also sacrifice track position or release a rival into clean air. The useful output is not simply “the tire has X laps left”; it is a comparison of possible race outcomes if the driver stops now, stays out, or responds to a rival.
4. Recalculate when the race changes
Every major event can alter the estimate. AWS identifies safety cars, yellow flags and other race dynamics as strategy-changing factors. A neutralisation can reduce the relative cost of pit-lane time, while a restart can make track position more valuable. A rival’s stop can create an undercut threat or make an overcut attractive. The displayed window must therefore be read as a live, conditional estimate.
What data goes into a tire and pit-window estimate?
| Signal or context | Why it matters | What public sources establish |
|---|---|---|
| Tire compound | Different compounds have different expected pace and degradation behavior, so compound choice changes the value of stopping. | AWS lists tire compound as an input to the Pit Window insight. |
| Lap times and stint pace | Observed pace shows whether performance is holding, falling or improving relative to rivals. | AWS lists lap times and describes strategy comparisons using live race data. |
| Car spacing and track position | A fresh-tire gain is less useful if the car rejoins in traffic; a gap can make a stop safer. | AWS identifies the spread of cars, gaps and traffic as part of the strategic picture. |
| Rival stops and clean air | Another car’s timing can create an undercut or overcut opportunity and change the expected finishing order. | AWS explains these interactions in its Pit Strategy Battle description. |
| Safety cars and yellow flags | Neutralisations change pit-lane time loss, gaps and the likelihood of a restart. | AWS explicitly lists safety cars, yellow flags and other race dynamics as strategy factors. |
| Vehicle telemetry | Telemetry can describe the car’s evolving state at a much finer time scale than broadcast timing alone. | AWS reported 300 sensors and 1.1 million data points per second per car in a circa-2020 technical post; the current channel list is not public. |
| Historical race records | Past races provide a baseline against which current pace and strategy can be compared. | Formula One’s 2018 announcement referred to more than 65 years of historical race data at that time. |
How does a model turn tire performance into a pit window?
Think of the calculation as a set of competing timelines. One timeline keeps the current tires on the car and projects lap times as the stint ages. Other timelines insert a pit stop on different laps, add pit-lane time, assign a fresh compound and project the laps after the stop. The system then compares expected positions, gaps and finishing performance rather than looking at tire age in isolation.
The best-looking timeline can change without the tire itself changing. If a driver has a large gap behind, a stop may be less risky. If traffic appears at pit exit, the same stop may be poor. If a safety car is deployed, the time penalty for stopping can shrink. Because these conditions are live inputs, a pit window is better understood as an interval whose boundaries move as the race develops.
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Public graphics simplify this analysis so a viewer can follow it. They are not a disclosure of a team’s complete internal simulator, and they should not be interpreted as a guaranteed instruction to pit on a particular lap.
How do F1 teams know when to pit?
Race engineers combine model outputs with information that may not appear in the broadcast. A practical decision sequence looks like this:
- Check the observed pace. Compare recent laps with the driver’s earlier stint pace and with nearby competitors, allowing for traffic, yellow flags and fuel effects.
- Assess tire condition and compound. Determine whether the current loss of pace is consistent with expected degradation or is caused by traffic, damage, weather or another problem.
- Project stop options. Test several pit laps and compounds, including the likely rejoin position and cars that could be encountered after the stop.
- Track rival intent. A rival stopping early may threaten an undercut; a rival staying out may expose an overcut opportunity.
- Apply race-control context. Safety-car probability, yellow flags, a red flag or changing weather can change the value of every option.
- Choose and keep updating. The engineer and driver decide whether the projected gain justifies the risk. The calculation continues after the call because the race rarely follows the original forecast exactly.
This is decision support, not proof that a team has surrendered strategy to an algorithm. Public evidence about F1’s broadcast insights cannot establish that every team uses the same model or that any team’s complete race plan is automated.
Undercut and overcut: why timing matters
| Approach | How it works | When it can help | Main risk |
|---|---|---|---|
| Undercut | Stop before a rival, use fresher tires for faster laps, and try to gain position when the rival eventually stops. | The new tires deliver a meaningful pace advantage and the car exits into clean air or a favorable gap. | The pit-lane loss, traffic or a slow warm-up can erase the pace gain; the rival may cover the move immediately. |
| Overcut | Stay out while a rival stops, use the existing track position and possibly clear air, then stop later. | The current tires remain competitive, the car can run faster than the rival’s out-lap traffic, or a neutralisation may arrive. | Degradation can become severe, the rival’s fresh tires may be much faster, or the late stop may rejoin behind traffic. |
AWS’s explanation of the Pit Strategy Battle presents both tactics as interactions among fresher tires, pace, gaps, traffic and rival timing. Neither tactic is universally superior. The correct choice depends on the expected pace gain, pit-lane time, compound, track position and conditions at the moment of the decision.
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One stop or two: what the model is really comparing
A one-stop plan usually protects track position by paying the pit-lane cost once, but it asks a tire to cover a longer stint and can become vulnerable if degradation is higher than expected. A two-stop plan pays the pit-lane cost twice in exchange for more laps on fresher tires. Public sources do not provide a universal degradation rate or a current circuit-by-circuit pit-loss table, so no fixed lap number can identify the optimal plan for every event.
- Expected pace gain: How much faster are the fresh tires likely to be over the remaining laps?
- Pit-lane loss: How much time is spent entering, stopping and exiting, including the event’s pit-lane characteristics?
- Track position: Where will the car rejoin, and can it pass the traffic it encounters?
- Compound and stint pace: Is the current tire still producing competitive laps, and how quickly is performance changing?
- Rival behavior: Can another car cover the stop or exploit clean air?
- Race interruptions: Could a safety car or yellow flag change the relative cost of stopping?
A simple race scenario
Suppose two cars are separated by a small gap. The leading car’s lap times begin to fall, while the following car has a fresher set available. A model may first show an undercut opportunity for the follower. If the leader pits, the follower’s projected benefit can disappear because the leader has protected track position. If the leader stays out and a safety car appears, both cars may be able to stop with less relative time loss, changing the preferred sequence again. If the follower would rejoin behind a slower group, the apparent tire advantage may not translate into a position gain.
This example illustrates why the model predicts outcomes under scenarios rather than declaring a tire’s exact remaining life. The strategic value comes from combining tire pace with gaps, traffic and event timing.
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What the public F1 insights show—and what they do not
The AWS F1 Insights descriptions explain the inputs and viewer-facing outputs, including Pit Window and strategy graphics. About Amazon’s 2023 account says the Predicted Pit Stop Strategy insight uses historical data to calculate a strategy during the first lap and compare predicted tire and race strategies. These explanations help fans understand the logic of a race, but they are not a complete technical specification for a team’s private system.
AWS also describes machine-learning support for Scuderia Ferrari HP pit-stop analysis in a separate article. That is evidence of an AWS application with Ferrari, not evidence that the team’s entire race strategy is automated or that its confidential models match the public F1 graphics. The article is available at AWS’s pit-stop analysis account.
What current academic work adds
A 2025 arXiv preprint, Explainable Time Series Prediction of Tyre Energy in Formula One Race Strategy, studies tire-energy prediction as a way to estimate degradation and support pit decisions. It compares an explainable time-series approach with XGBoost. This is academic work, not proof that either method is deployed by an F1 team. Its importance is conceptual: an interpretable forecast can help explain why a predicted degradation trend or pit option changed instead of presenting an unexplained score. Read the preprint at arXiv.
Limits you should keep in mind
- No exact tire countdown: Machine learning estimates likely performance and strategic outcomes; it does not publicly establish the precise number of laps a tire has left.
- Uncertain inputs: Traffic, damage, weather, driver behavior and unusual track evolution can make a historical pattern less reliable.
- Hidden team data: The public sources do not list every telemetry channel, feature, model or confidence calculation used by a team.
- Changing regulations: The material here does not verify a current mandatory compound or pit-stop rule. Such rules are season- and event-sensitive.
- Broadcast simplification: A graphic is designed for viewers and should not be treated as a transparent view of a team’s complete strategy system.
- Model-versus-reality gap: A prediction can be statistically favorable yet lose because of a slow stop, traffic, a rival’s response or an incident.
How to read a pit-window graphic as a viewer
- Identify the tire compound and recent lap trend rather than looking only at the highlighted window.
- Check the gap to the next cars and ask where a stop would rejoin the driver.
- Look for a rival’s stop, a safety car or yellow flags that could move the window.
- Treat the displayed lap as a recommendation under current assumptions, not a guaranteed call.
- After the stop, compare the predicted strategic gain with the actual traffic and out-lap pace.
Used this way, machine-learning graphics make the strategic trade-offs legible without pretending that viewers can see every private calculation made on the pit wall.
Frequently asked questions
Is a displayed pit window the exact lap on which a driver will stop?
No. It is a conditional estimate that can move when pace, gaps, rival actions or race-control conditions change.
Do all F1 teams use the same machine-learning model?
There is no public evidence establishing a single model shared by every team. Public F1 and AWS insights are separate from each team’s confidential strategy and telemetry systems.
Can machine learning predict a puncture or mechanical failure?
The cited public material concerns tire performance and strategy. It does not establish a general puncture or mechanical-failure prediction capability, so those claims should not be inferred from pit-window graphics.
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