AI and machine learning can influence motorsport performance by turning telemetry, video and other race information into forecasts, comparisons and decision support. The strongest documented examples here are in Formula One: they range from experimental tyre and strategy models to FIA tools that help officials review incidents. That is not the same as an AI system driving a car or making final race decisions.
How can AI and machine learning affect motorsport performance?
Artificial intelligence is a broad label for systems that perform tasks associated with human judgment. Machine learning is one approach: a model learns patterns from data and uses them to make predictions or classifications. In racing, the practical question is not whether a system is “AI,” but what job it does, what information it uses and how people act on its output.
Forecasting from telemetry
Race telemetry records how a car and its components behave. A model can use historical data to estimate a quantity such as tyre energy or degradation, giving engineers and strategists another input when they assess tyre choice or pit-stop timing. The output is a forecast, not a guarantee: its usefulness depends on whether the data and conditions resemble the situation at hand.
A 2025 study by Jamie Todd and colleagues, Explainable Time Series Prediction of Tyre Energy in Formula One Race Strategy, trained deep-learning models on historic Mercedes-AMG PETRONAS F1 telemetry and compared them with XGBoost. The authors also used feature-importance and counterfactual methods to make predictions more interpretable. The paper presents an automated approach that could assist strategy optimization; it does not establish that the models were deployed live by the team.
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Testing race strategies
Reinforcement-learning research tests how a model might choose actions over the course of a race, such as a strategy decision, in response to changing conditions. A 2025 paper, Explainable Reinforcement Learning for Formula One Race Strategy, reports the following result for a test race based on the 2023 Bahrain Grand Prix:
| Measure | Reported result | What it describes |
|---|---|---|
| Explainable reinforcement-learning model | Average finishing position P5.33 | Authors’ test of a race scenario based on the 2023 Bahrain Grand Prix |
| Best baseline | Average finishing position P5.63 | Comparison result in the same reported test |
These are model results, not the finishing positions of cars in the actual 2023 Grand Prix and not proof of a real-world gain. They show what the authors report under a defined test setup; they do not establish that the difference would persist with another track, season, car or set of race conditions.
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Supporting race officials
AI-assisted computer vision and workflow automation can also support sporting operations. The FIA’s Remote Operations Centre (ROC) uses shape-recognition capabilities to help identify incidents and route information to Race Control. FIA reporting describes tools that assist the review of track-limit reports; officials, rather than the software, make the sporting judgments.
For the 2023 Qatar Grand Prix track-limit workflow, the FIA reported that eight people monitored 820 corner passes. The process produced 141 reports sent to Race Control and 51 deleted laps. Those figures describe the review workload and its outcomes, not an AI accuracy rate or a measured improvement in car performance.
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What does the evidence show—and what does it not show?
The examples demonstrate different kinds of value: a forecast may inform engineering or strategy, a learned strategy can be evaluated in a test, and computer vision can help organize officials’ review work. Only the first two relate directly to competitive strategy; the ROC example is operational support, not a way to make a car faster.
The FIA’s Safety and Technological Development – FIA Activity Report 2024 records work on F1 performance analysis, improved pit-stop performance and safety monitoring, a survey of requirements for AI tools, and a roadmap for implementing AI tools in FIA championships. This is evidence of governing-body activity and planning. It does not identify a particular AI system as the cause of improved pit stops or show that AI changed race results.
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A November 2022 Rescale industry report, Formula One Future Technology Series, Season 3: Optimising F1 Performance Off-Track, discusses F1 interest in AI/ML, simulation and digital twins. It offers an industry perspective, not a regulator’s measurement of current adoption or a census establishing what every team uses.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge an AI performance claim
A result is meaningful only in relation to its task and evidence setting. When evaluating a claim, look for the following:
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- Task: Is the system forecasting a condition, optimizing a strategy, analyzing imagery, running a simulation or triaging workflow?
- Evidence setting: Was it tested on historical data, in a simulation, in a controlled test or during live operations?
- Data and transfer: What telemetry or imagery went into the model, and has it been shown to work across different tracks, cars, seasons, weather or rules?
- Explainability: Can engineers or officials inspect why the system produced a forecast, recommendation or flag? The tyre-energy study explicitly applies feature-importance and counterfactual techniques.
- Human oversight: Does the system advise a strategist, engineer or official, and who makes the final decision?
There is no standardized cross-team benchmark in the cited studies that establishes an average AI-driven performance improvement across motorsport. A result from one experiment should therefore be reported with its test conditions, not generalized into a sport-wide gain.
Does AI control race cars or determine the rules?
The documented examples here concern analysis, prediction, strategy research and operational support. They do not establish that AI autonomously controls race cars, that every team or series uses it, or that an automated system makes final sporting decisions.
Regulatory changes should not be mistaken for AI requirements. In its April 24, 2025 Formula 1 Commission statement, the FIA discussed refinements to the 2026 energy-management strategy, with further specialist work and regulatory approvals to follow. The statement did not say that the regulations mandate AI. Energy-management rules, algorithms used in a power unit and AI as a broad category are separate matters.
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