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Formula 1 machine-learning systems do not know the exact lap on which a tire will fail. They estimate how tire-related pace is likely to change, then combine that estimate with pit-lane loss, traffic, rival behavior, car spacing and race disruptions to identify a potentially advantageous stop window. The result is a conditional, continuously updated strategy aid—not an automatic order to pit.
What “tire degradation” means in a race strategy model
Tire degradation is the loss of performance as a tire completes laps. In practical terms, the car may become slower over a stint even though the driver is maintaining an appropriate pace. A new set can restore performance, but taking it requires a pit-lane stop, costs time, and can put the car behind traffic. Strategy software therefore treats degradation as a trade-off rather than a single failure point.
The public Formula 1 and AWS descriptions frame the problem as a prediction of performance and opportunity. Historical races provide reference patterns, while live timing and race events change the value of each possible stop. A displayed “pit window” is best read as the period in which stopping could produce a strategic advantage under the current assumptions.
How does F1 predict tire degradation?
The public material supports a four-stage loop. It does not disclose every feature, model architecture or private team parameter, so the steps below describe the externally documented process without implying that every team uses an identical implementation.
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1. Build a baseline from past races
Past race information establishes patterns for tire use, pace and strategy. Formula 1’s 2018 announcement about its partnership with AWS said data scientists were training deep-learning models with more than 65 years of historical race data. That was the historical dataset described at the time of the announcement; it should not be treated as a verified current dataset size or as proof that every modern model uses the same archive.
Historical data can show how pace changed through comparable stints, how gaps evolved after stops and how strategies performed in different race circumstances. It supplies a prior expectation, not a guarantee: a new circuit temperature, unusual safety-car timing or a different traffic pattern can make a past example less relevant.
2. Observe the current race
Live information updates the baseline. Public F1 Insights descriptions identify tire compound, lap times and the spread of cars as inputs to the Pit Window estimate. Car positions and gaps matter because a theoretically faster tire is less valuable if the driver rejoins behind a slower train.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsAWS’s technical account, published circa 2020, reported that each car had 300 sensors transmitting 1.1 million data points per second from the car to the pit. Those figures are historical claims from that account, not a current, independently verified sensor specification. The public article also does not list every sensor channel used by each broadcast insight. The important point is the existence of a high-rate stream that can describe the car and its operating conditions, while strategy models select the signals relevant to the decision.
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3. Estimate the pace-versus-stop trade-off
The model estimates what may happen if the car remains on its current tires and what may happen after changing to a fresher set. Continuing may avoid pit-lane time but accept declining stint pace. Stopping may recover pace, yet the car must spend time in the pit lane and could rejoin in traffic or behind a rival that has not stopped.
This calculation is not the same as predicting a tire’s exact remaining life. Public descriptions support estimates of strategic possibilities: how a tire compound and observed lap times relate to likely pace, and whether the gaps between cars make a stop attractive. Teams can then apply engineering knowledge, driver feedback and tactical information that is not visible in the public graphic.
4. Recalculate when the race changes
A pit window moves because the race moves. AWS identifies safety cars, yellow flags and other race dynamics as events that can alter strategy. A neutralization can reduce the relative cost of entering the pit lane; a restart can create or destroy clean air; a rival’s stop can change the gap that a driver must cover. The system therefore updates its estimate instead of treating the first projection as fixed.
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| Input or context | How it affects the estimate | Public evidence and qualification |
|---|---|---|
| Tire compound | Different compounds provide different pace and durability expectations, changing the value of staying out or fitting a fresh set. | Listed by AWS as an input to the Pit Window insight. |
| Lap times | Observed pace indicates whether the current stint is losing performance and how quickly a rival may be approached or escape. | Listed by AWS as an input; the public description does not publish a universal degradation rate. |
| Spread of cars and gaps | Determines whether a stop will rejoin in clean air, behind traffic or near a rival. | Listed by AWS; public material does not provide a circuit-by-circuit pit-loss table. |
| Car sensor stream | High-rate telemetry can describe changing operating conditions used by engineering and analytics systems. | AWS reported 300 sensors and 1.1 million data points per second circa 2020; the figures are not verified current counts. |
| Safety cars and yellow flags | Neutralizations can change the time cost of a stop and compress the field. | Identified by AWS as race dynamics that can alter strategy. |
| Rival actions | An opponent’s stop changes gaps, undercut opportunities and the value of track position. | Explained in AWS’s description of the Pit Strategy Battle graphic. |
The complete list of features and weighting is not public. Broadcast graphics are designed to explain strategy to viewers, not to disclose a team’s confidential race model.
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How do F1 teams know when to pit?
A team compares the expected time gained on fresher tires with the time and position sacrificed in the pit lane. Engineers also ask where the car will emerge, whether it will have clean air, and what nearby rivals can do in response. A mathematically attractive stop can be poor in practice if the driver rejoins behind a slower car or loses track position that cannot be recovered.
Undercut: stopping before a rival
An undercut attempts to use the first car’s fresher tires to gain enough pace while the rival remains on older tires. The stopping car may complete a fast out-lap and build an advantage before the rival makes its own stop. AWS explains that the result depends on the fresher tire’s pace, the gap, traffic and the timing of both stops. The undercut is not automatically effective: a cold tire, traffic at pit exit or a circuit where overtaking is difficult can erase its benefit.
Overcut: staying out longer
An overcut keeps the car on track while a rival pits, seeking clean air, lower traffic or a favorable later tire phase. It can work when the driver’s current pace remains competitive and the rival rejoins behind slower cars. It can fail if the existing tires degrade rapidly or if the rival’s fresh-tire pace creates too large a gap before the stop.
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| Decision factor | Why it matters to an undercut or overcut |
|---|---|
| Expected pace gain from fresher tires | A larger gain makes an early stop more capable of covering the time lost in the pit lane. |
| Pit-lane time loss | The stop must recover this cost through faster laps or a favorable race interruption. |
| Compound and stint pace | Observed behavior can support staying out or switching sooner. |
| Track position and traffic | Rejoining behind cars can prevent the fresh tires from delivering their theoretical advantage. |
| Rival timing and clean air | The order in which cars stop determines who gets unobstructed laps and who responds under pressure. |
| Safety-car or yellow-flag timing | A neutralization can change both the effective pit cost and the gaps that a strategy must manage. |
These factors explain why there is no universally optimal one-stop, two-stop, undercut or overcut plan. The best choice depends on the circuit, compounds, current stint pace, gaps and the unfolding race.
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What are the public F1 strategy insights?
Pit Window
Formula 1’s AWS insight describes a Pit Window estimate using tire compound, lap times and the spread of cars. It is intended to show when an opportunity may be opening or closing, while safety cars, yellow flags and other dynamics can move that estimate. Read it as a live range of plausible advantage, not as a promise that a car will stop during that lap.
Predicted Pit Stop Strategy
About Amazon’s 2023 explanation 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. A first-lap prediction gives viewers a baseline for the race, but subsequent tire performance, traffic and incidents can make the eventual plan diverge from it.
Pit Strategy Battle
The Pit Strategy Battle graphic is designed to help viewers follow changing strategy in real time. AWS uses undercut and overcut examples to show how fresher tires, pace, gaps, traffic and rival timing interact. Its purpose is interpretive: it makes the tactical contest legible without revealing every private calculation made by a team.
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What machine-learning methods are publicly documented?
Formula 1’s 2018 announcement referred to deep-learning models trained on historical race data, but it did not publish a complete architecture or feature list. That means it would be inaccurate to claim that one named algorithm powers every F1 tire or pit-window prediction.
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A 2025 arXiv preprint, “Explainable Time Series Prediction of Tyre Energy in Formula One Race Strategy,” examines tire-energy prediction as a route to estimating degradation and pit decisions. It compares an explainable time-series approach with XGBoost. This is academic work, not evidence that either method is deployed by an F1 team. Its relevance is that explainability can help analysts understand why a forecast changes, while a tree-based model can capture nonlinear relationships; neither fact establishes a production system.
How reliable is a machine-learning pit-stop prediction?
Reliability is conditional. A model can be useful when current conditions resemble the patterns in its historical data and when live measurements accurately describe the car and field. Confidence should fall when an unusual event changes the assumptions: a safety car, yellow flags, an unexpected tire-temperature problem, a damaged car, rapidly changing weather or traffic that blocks the expected pace gain.
- Estimate, not certainty: a pit window expresses a favorable possibility, not an unavoidable instruction.
- Context matters: the same tire and lap time can imply different choices depending on gaps and track position.
- Human interpretation remains essential: teams combine analytics with driver feedback, engineering judgment and confidential information.
- Public graphics are partial: they do not disclose every feature, forecast uncertainty or private team constraint.
Formula One World Championship Limited’s 2018 AWS announcement summarized the intended use this way: “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.” The wording describes an opportunity forecast, not a guaranteed outcome.
Is Ferrari’s machine-learning pit analysis the same as the broadcast model?
No equivalence is established publicly. AWS separately describes machine learning supporting Scuderia Ferrari HP pit-stop analysis. That demonstrates an AWS/Ferrari application, but it is not evidence that Ferrari’s complete race strategy is automated, nor that its confidential systems match the models behind Formula 1’s public broadcast insights. Teams can use different data, objectives and decision rules even when the same cloud provider or analytical technique is involved.
What the sensor and historical-data numbers really mean
| Figure | Source and date context | Correct interpretation |
|---|---|---|
| 300 sensors per car | AWS technical article, circa 2020 | A historical description of the sensor count reported in that post, not a verified current specification. |
| 1.1 million data points per second | AWS technical article, circa 2020 | The reported transmission rate from car to pit in that account, not a guaranteed rate for every current insight. |
| More than 65 years of historical race data | Formula 1/AWS announcement, 2018 | The size of the historical dataset described when the partnership was announced, not a present-day total. |
Can machine learning predict an F1 pit stop?
It can estimate when a stop may offer maximum strategic value under the observed conditions, which is the purpose described for F1’s public insights. It cannot guarantee that a stop will occur or that the result will match the forecast. The final call must account for evolving tire behavior, pit-lane loss, traffic, rivals and race-control events. Treat the prediction as decision support: a probability-informed window that changes as new evidence arrives.
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Sources and further reading
- AWS: Accelerating innovation: How serverless machine learning on AWS powers F1 Insights (technical account, circa 2020).
- AWS: F1 Insights powered by AWS (official insight descriptions).
- Formula One World Championship Limited: Formula 1 selects AWS as its Official Cloud and Machine Learning Provider (2018 announcement).
- arXiv: Explainable Time Series Prediction of Tyre Energy in Formula One Race Strategy (2025 preprint).
- AWS: AWS machine learning supports Scuderia Ferrari HP pit stop analysis (circa 2025).
- About Amazon: How AWS is using AI to bring Formula 1 fans closer to the race (2023 explanation).
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