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Formula 1 teams do not hand race strategy to an autonomous AI. They build a race plan from historical data and simulations, then repeatedly compare that plan with live timing, telemetry, tyre behaviour, weather and rival pace. Strategists use those predictions to recommend whether a driver should pit, extend a stint, cover an undercut or change plans; senior personnel on the pit wall remain responsible for the call.
Race strategy is a repeated decision under uncertainty
A strategy call is not a single choice made before the lights go out. It is a sequence of decisions about tyre compounds, pit-stop timing, stint length, traffic and responses to rivals. Every new lap supplies evidence that can make the original plan more or less attractive.
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The objective is usually expressed as expected race time, but the calculation has several competing parts: the time lost in a pit lane, the lap-time benefit of fresh tyres, degradation on the current set, the risk of falling into traffic, and the chance that a safety-car period or rain changes the value of stopping. A plan that is fastest in a clean simulation can become inferior when a rival stops first or a caution neutralises the field.
That is why “the strategy” is better understood as a set of conditional plans. A team may prepare a one-stop baseline, a two-stop alternative, and instructions for a safety-car or rain scenario. During the race, the strategists decide which branch is still credible.
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What teams do before the race weekend
Build a baseline from historical and circuit data
Formula 1’s explanation of the strategist role says preparation can begin months before a flyaway race. Teams study previous races, tyre selections, fuel use, weather patterns and circuit characteristics while creating an initial race plan. The public account also notes that teams differ in size and protect details such as tyre wear, fuel consumption and their decision matrices. Formula 1’s strategist guide describes the planning process without publishing any team’s confidential model.
Practice sessions then replace assumptions with track-specific measurements. Engineers estimate how quickly each tyre compound loses performance, how long a pit stop takes under current conditions, and how much lap time is lost in traffic. Those estimates are not fixed constants: track temperature, rubber laid on the surface, wind and car setup can all move them.
Simulate competing scenarios
Simulation is used to compare plausible futures rather than to guarantee one result. Mercedes Head of Strategy Rosie Wait described the process this way: “We pull together simulations out of all of the data we have on the circuit, as well as what we know about our rivals’ pace to give us a picture of what will be the big questions for the weekend.” Her 2021 interview is a named example of a strategy workflow, not evidence that every team has identical staffing or software today. Read the full Formula 1 interview with Rosie Wait.
A simulation can run thousands of combinations: stop on an early lap or a late lap, use a particular compound next, lose time in traffic, or gain track position after a rival’s stop. The output is a comparison of expected outcomes and the assumptions that produced them. It does not remove uncertainty about a puncture, a collision, a poorly timed safety car or a weather cell that was not in the forecast.
Prepare tyre and fuel assumptions
Tyre degradation and fuel effect are strategically important but commercially sensitive. Public sources do not state the telemetry channels, update rates or model accuracy used by any particular constructor. Treat published diagrams and broadcast graphics as explanations of the decision environment, not as a disclosure of a team’s complete tyre model.
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What telemetry contributes during a race
Telemetry is information about how a car is operating. Strategists interpret it alongside lap and sector times, track position, tyre age and compound, weather, incidents and estimates of rival pace. It can reveal that a driver is losing performance in a particular stint, that a car is being held up by traffic, or that the current pace differs from the pre-race model.
The exact measurements available to a team and the frequency with which they are updated are not enumerated in the public sources used here. It is therefore more accurate to describe telemetry as a stream of operational evidence than to claim a specific sensor count or universal refresh rate.
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Formula 1’s 2026 Strategy Insight announcement describes a public graphic that combines live timing, telemetry, historical race data, predictive analysis and historic modelling. It gives two examples of questions the analysis can illuminate: whether to extend a stint and how to respond to an undercut threat. Formula 1’s Strategy Insight announcement is about broadcast analysis; it does not disclose a constructor’s private pit-wall model.
How a team decides whether to pit
Compare staying out with stopping now
At each decision point, the strategy group compares the expected race result of continuing with the current tyres against the result of stopping. The comparison normally includes:
| Question | Why it matters |
|---|---|
| How much performance remains in the tyre? | A degrading tyre may lose more lap time than a pit stop costs, making an earlier stop attractive. |
| What is the pit-lane time loss here? | The penalty varies by circuit and race conditions; a large loss raises the threshold for stopping. |
| Where will the car rejoin? | Traffic can erase the benefit of a fresh compound, while clear air can amplify it. |
| What are rivals likely to do? | A stop may protect track position, cover an undercut or exploit a rival committing to a different plan. |
| Could weather or a safety car change the calculation? | A neutralisation can make a cheap stop possible; rain can make a planned dry-tyre stint irrelevant. |
The recommendation is usually a range rather than a single magic lap. If the model says the car can stop between laps 18 and 22, live traffic, rival behaviour and tyre temperatures determine when inside that window the call is safest.
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Understand the undercut
An undercut is an attempt to pit before a rival and use fresh tyres to set faster laps while the rival remains on an older set. The newly stopped car may gain enough time to emerge ahead when the rival eventually stops. Telemetry and timing help estimate whether the fresh-tyre pace will offset the pit-lane loss and whether the driver will have clear track for the critical laps.
Teams also assess the risk that the rival can respond immediately. If both cars stop, the advantage depends on out-lap performance, traffic and the relative warm-up behaviour of the compounds. The Strategy Insight description specifically identifies responding to an undercut threat as a situation in which strategy can evolve.
Understand the overcut
An overcut keeps a car on track while a rival stops. It can work when the remaining tyres are still competitive, when the driver has clear air, or when the rival rejoins behind slower traffic. It can fail if degradation accelerates or if the rival’s fresh tyres produce a large pace advantage before the stop. The choice is a trade-off between immediate track position and the time available on each tyre set.
React to safety cars and weather
A safety car or virtual safety car reduces the time cost of a pit stop, so teams may bring a driver in earlier than the dry-race plan. The decision still depends on tyre availability, restart position and the possibility that the neutralisation ends before the stop is completed.
Weather is another changing input. The FIA announced a 2026 partnership with Tomorrow.io covering weather forecasting and risk assessments for FIA Race Control, including precipitation and storm forecasts and heat and wind risk assessments. That supports race-control decision assistance; it does not prove that every constructor uses the same provider or model. See the FIA announcement.
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Where AI and machine learning fit
Pattern detection and prediction
Machine-learning systems can process large historical datasets, identify relationships between conditions and outcomes, and produce predictions such as likely pace, degradation or the probability that a strategy branch will succeed. In a race room, those outputs are inputs to a broader decision process. They must be checked against current observations because a model trained on past races cannot know every detail of today’s track, car setup or incident pattern.
Formula 1 has described AWS as its official cloud and machine-learning provider. Its 2018 announcement said the partnership would support race strategy, data tracking and digital broadcasts. In 2023, Formula One World Championship Limited said the collaboration had launched 20 machine-learning-powered F1 Insights since 2018. Those 20 were live-television insights, not 20 autonomous systems making private team pit calls. Read the 2018 partnership announcement and the 2023 renewal account.
Strategy graphics versus team systems
The public Strategy Insight graphic is designed to explain moments to viewers. It combines live timing, telemetry, historical data and predictive analysis to show why a strategy might change. A team’s internal tools can contain more detailed assumptions and proprietary data, but public material does not establish their architecture, accuracy or decision thresholds.
Cloud and AI support inside a constructor
A January 2026 Microsoft announcement described Azure and AI support for the Mercedes-AMG PETRONAS F1 Team across factory and circuit operations, including computing, simulations, performance analysis, race-strategy modelling and cross-team analytics. It is a vendor announcement about capabilities and collaboration; it does not provide an independently measured lap-time gain or prove that an AI system makes the final call. Read Microsoft’s announcement.
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Wait’s account describes recommendations being developed through a strategy structure and passed to senior leadership on the pit wall. That human chain matters because the data can be incomplete or contradictory. A strategist may know that a rival’s apparent pace is distorted by traffic, that a tyre-temperature warning is transient, or that a driver’s feedback changes the risk of extending a stint.
The practical division is therefore analytical rather than autonomous: software calculates scenarios and highlights changes; strategists interrogate assumptions; race engineers and drivers provide operational context; and senior decision-makers authorise the instruction. Radio communication then turns the recommendation into a driver action, subject to safety and sporting rules.
A lap-by-lap strategy workflow
- Set the baseline. Before the start, the team loads expected tyre life, pit-lane loss, fuel effect, weather possibilities and rival pace into its race plan.
- Watch the opening laps. Engineers compare actual lap and sector times, tyre behaviour, traffic and gaps with the baseline. An early safety car or damage can invalidate the original sequence.
- Update the forecast. Simulations are rerun with current tyre age, observed degradation, track evolution and likely rival actions. The output is a set of expected outcomes, not certainty.
- Check the strategic window. The group identifies whether stopping now, continuing or covering a rival produces the strongest expected result and what traffic each option creates.
- Test contingencies. Weather radar, incident risk, safety-car probability and available tyre compounds are folded into the recommendation.
- Communicate the call. A senior strategist or race engineer sends the instruction, with alternatives retained in case the rival or weather changes before the pit entry.
- Re-evaluate after the stop. The new tyre’s warm-up, traffic and rejoin position become fresh evidence for the next decision.
What can make a model-led call fail?
The assumptions are wrong
A degradation curve learned from cooler practice conditions may not describe a hotter race. A model can also underestimate the cost of dirty air or overestimate the pace available after rejoining behind another car.
The data arrives too late or is misleading
Timing gaps can change abruptly around traffic, yellow flags or a pit entry. Telemetry is useful only when the team understands its context and distinguishes a sustained trend from a short anomaly.
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Strategy models generally evaluate likely rival actions. A surprise early stop, an unusually long stint or a different tyre allocation can move the optimal response faster than a precomputed plan.
Communication and execution introduce risk
Even a sound recommendation can fail through a delayed radio message, a slow pit stop, unsafe release or a driver rejoining into traffic. These operational risks belong in the decision, not in a separate “AI” score.
What public evidence does—and does not—establish
- Public accounts establish that Formula 1 and teams use historical data, simulations, live timing, telemetry and predictive analysis to compare strategy scenarios.
- They establish machine-learning-powered broadcast insights and named cloud and analytics collaborations.
- They do not publish a universal telemetry inventory, update cadence, model accuracy, pit-call success rate or lap-time gain.
- They do not establish that an AI system independently decides when a team pits.
- The FIA’s weather partnership concerns Race Control support, not proof that every constructor uses the same weather service.
A 2025 paper proposed deep-learning decision support for Formula 1 pit stops. That is research into a method, not evidence that a team deployed it in a championship race. Read the paper.
How to interpret an F1 strategy graphic
When a broadcast graphic says a team should stop, treat it as an explanation of a modelled scenario. Ask what assumptions are visible: tyre age, expected pace, traffic, rival position, weather and safety-car context. The graphic can clarify why extending a stint or covering an undercut became attractive, but it cannot reveal every confidential input used by the team or guarantee that the recommendation was actionable at the exact moment shown.
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Yes, AI and machine learning are documented in Formula 1’s analytics, broadcast insights, cloud computing and team-support systems. Telemetry is one of the live evidence streams used with timing, tyres, weather and rival pace. But the public record supports a decision-support model, not autonomous race strategy: people remain accountable for weighing uncertainty and issuing the pit instruction.
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