NASCAR teams use simulation to prepare drivers, compare car setups and make more use of limited track time. AI is part of some motorsports preparation—General Motors says it uses AI alongside real-time data and driver-in-the-loop simulation—but the available account of one team’s setup workflow describes simulation and data correlation without identifying an AI model. The useful distinction is that simulation is the broader engineering process; AI is one possible tool within it, not a substitute for accurate data, engineering judgment or driver feedback.
Why teams simulate before race weekend
Real track time is limited, and a team cannot practically test every setup idea on the track before a race. A simulator lets engineers and drivers examine alternatives in a repeatable virtual environment, prepare for expected conditions and give the driver practice. The point is not to predict every detail of a race perfectly. It is to create a model close enough to real behavior that comparisons between options can inform decisions.
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In an account of 23XI Racing’s process published by NASCAR.com, simulation driver Keegan Leahy described using a previous race’s data and car in the simulator, checking that the virtual laps and driving behavior aligned with real data, and then exploring setup options. Once the model is correlated, teams can use it to test variations before arriving at the track. The article describes one team’s workflow, not a universal procedure used identically across NASCAR.
This matters because a virtual result is only useful to the extent that the model represents the real car and conditions relevant to the question. A detailed track environment by itself does not establish that the simulated car behaves realistically. Teams have to compare the simulator against measured data and adjust the model when it does not line up.
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How the simulation workflow works
1. Build a representative virtual track and conditions
The starting point is an environment that reflects the circuit and the conditions the team wants to examine. General Motors describes using scanned tracks and simulations that replicate NASCAR vehicles and tracks. The company says a track scan can contain “in the order of 800 million points.” That is GM’s description of scan detail, not a claim that every NASCAR track scan contains exactly that number.
Historical race data can also help set expectations. For a new or repaved circuit, 23XI’s Marcos Telles said Goodyear tire tests can give manufacturers data to work from before race weekend. That helps teams prepare when there is less directly comparable race history for the surface.
2. Check the model against real performance
Before using a virtual car to judge setup changes, engineers need to establish that the simulator produces behavior resembling the real car. Leahy called this process “correlation.” He described taking data from the previous race and checking whether lap times, racing lines, throttle, braking and steering in the simulator were similar to the real data.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThe comparison is important in two ways. First, it tests whether the modeled car is producing plausible results in a known situation. Second, it checks whether the simulated driver inputs resemble the inputs recorded in that situation. If those elements do not align, the simulator may still be useful for practice or exploration, but the team has less reason to trust a small predicted difference between two setups.
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Data collection can be extensive. The 23XI account says a test car records at least a thousand channels per wheel, including brake pressure, speed, wheel temperature and speed, surface and bolt temperature, and tire pressure. This is the figure reported for the described test-car data; it should not be read as a guaranteed specification for every team, car or session.
3. Compare setup alternatives virtually
After the model has been checked against real-world data, engineers can run setup options and compare them before track arrival. The 23XI account does not publish a fixed list of adjustable parameters, so there is no basis for treating one list as a standard NASCAR simulator menu. The general purpose is to explore alternatives in the modeled car and conditions, then identify options worth evaluating further.
Simulation expands the number of iterations a team can consider without using a separate real-track run for every idea. It can help narrow the field of possible choices, but a simulated advantage is not itself proof that a change will work in the race. The model’s assumptions and its agreement with relevant measurements govern how much confidence to place in a result.
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Drivers use the simulator for practice and to describe how the car behaves. Leahy said drivers can “get their feedback about how the car drives,” suggest potential setup changes and complete practice laps. Engineers and crew chiefs then interpret that feedback alongside the data and the purpose of the simulation.
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That human role is not incidental. A simulator provides modeled outputs and a controlled way to explore options; the driver supplies a report of the car’s behavior from behind the wheel. The driver’s impression does not replace measured comparison, and measured outputs do not eliminate the need to decide whether a change is practical. The workflow combines both kinds of information rather than making the simulator an autonomous decision-maker.
Where AI fits—and what is not established
General Motors says its motorsports preparation uses technology, real-time data and “even AI.” It also describes driver-in-the-loop simulators for NASCAR-capable vehicles and lists aeromaps, tire force and moment data, and car measurements among simulation inputs. This is evidence that GM includes AI among the technologies used in motorsports preparation. Its public description does not identify a particular AI model or say that AI independently recommends weekly car setups.
By contrast, the published 23XI workflow is described in terms of simulation, data correlation, setup trials, engineering collaboration and driver feedback. It does not call that process AI. A mathematical or physical model can simulate a vehicle without being an AI system. As a result, it is more accurate to say AI is one tool used in some motorsports preparation than to claim that all NASCAR setup simulators are AI-driven.
It is also useful to separate an AI-supported workflow from a claim that “AI picks the setup.” The evidence here supports the former in GM’s broad description, but not the latter. The sources do not specify which calculations AI performs, how its outputs are validated, or how much weight a team gives them relative to conventional models and human judgment.
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Not every NASCAR simulation answers the same question
“Simulation” covers distinct jobs. A weekly setup simulator, a crash-safety model, an engine-manufacturing digital twin and a broadcast fuel estimate all work with modeled or measured data, but they are not interchangeable. Their objectives, inputs and validation needs differ.
| Use | Question it addresses | What the cited evidence establishes |
|---|---|---|
| Race preparation and setup | How does a modeled car behave in a track scenario, and how do alternatives compare? | 23XI describes correlating a simulator to prior race data, trying setup options and incorporating driver feedback. |
| Safety modeling | How might the vehicle and safety systems respond in different crash scenarios? | NASCAR vice president of safety engineering Dr. John Patalak said computer modeling of varied track crash scenarios informed safety changes. This is not the same as weekly setup testing. |
| Engine-part manufacturing | How can manufacturing processes and machine programs be modeled or improved? | Siemens describes Hendrick Motorsports using NX, VNCK and a digital twin in engine-part manufacturing. It is a manufacturing workflow, not weekly car-setup simulation. |
| Broadcast fuel analytics | How much fuel might a car have consumed during a race? | The Associated Press reports that Prime Video’s Burn Bar estimates consumption using telemetry, RPM, throttle and optical tracking of car positions. It is a viewer-facing calculation, not evidence that AI chooses team setups. |
The Burn Bar is a particularly clear example of why labels matter. NASCAR on Prime analyst and former crew chief Steve Letarte said there is no sensor on the car giving the broadcast miles per gallon; the figure is a mathematical calculation based on other cars’ performance. AP attributed thousands of performance data per second to the Burn Bar’s AI model in 2025. That number describes the broadcast feature, not the data rate or capabilities of a team’s setup simulator.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the reported performance numbers do—and do not—mean
Published numbers can be useful, but only when their scope stays attached. The available figures come from different years and applications, and they do not constitute a current, across-the-grid benchmark of race setup simulation.
| Reported figure | Source and context | How to interpret it |
|---|---|---|
| In the order of 800 million points in a track scan | General Motors motorsports page, accessed in 2026; publication date not stated. | GM’s description of scan detail. It is not an exact count for every track scan. |
| At least a thousand channels per wheel | NASCAR.com’s 2024 account of test-car data at 23XI. | Describes the data collection in that account, including measures such as pressure, temperature and speed; it is not a universal specification for every team. |
| Thousands of performance data per second | Associated Press report from 2025 on Prime Video’s Burn Bar. | Attributed to the broadcast feature’s AI model, not a NASCAR team setup simulator. |
| “Zero-scrap rate,” tenfold improved accuracy and setup time reduced by 50 percent | Siemens case study on Hendrick Motorsports engine manufacturing; publication date not stated. | Vendor-reported manufacturing outcomes involving NX, VNCK and a digital twin, not general race-performance results. |
| Simulation reduced from 24 hours to about 30 minutes | Microsoft’s 2008 account of a Chip Ganassi Racing high-performance computing example. | A historical example, not a current benchmark. Microsoft said the simulations modeled track characteristics, expected temperature, camber, tire pressure, spring rates and suspension geometry. |
The Siemens results can illustrate how simulation and digital-twin tools may support manufacturing, but they should not be used to claim that weekly setup work became 50 percent faster or that cars became more competitive by a stated amount. Likewise, the 2008 Microsoft example is useful history, not a present-day measure of how quickly NASCAR teams run simulations.
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What simulation can and cannot tell a team
It can help make comparisons before spending track time
A correlated simulator gives engineers a way to examine multiple options and drivers a way to practice before arriving at the circuit. Where useful prior data exists, it can also anchor the virtual environment in experience from previous races. The strongest practical benefit supported by the sources is more preparation and more setup iterations from limited real-world track time.
It cannot guarantee a result simply because the virtual world is detailed
A high-resolution track scan describes the environment; it does not, by itself, validate the car model or ensure that its behavior matches the real car. Correlation against actual measurements is a separate step. New or repaved surfaces also make relevant testing data valuable, as the 23XI account’s reference to Goodyear tire tests illustrates.
It does not remove the need for people to assess the answer
Drivers report handling and complete practice laps; engineers and crew chiefs assess the results and decide what to do with them. GM’s description of driver-in-the-loop simulation aligns with that broader pattern. Nothing in the cited account establishes a fully autonomous setup process in which an AI system makes the final call.
It should not be judged by unrelated metrics
A model built to investigate crash scenarios should be judged by its safety purpose, not by whether it recommends a fast setup. Manufacturing simulation metrics concern production, while broadcast fuel estimates communicate an analytical calculation to viewers. Treating those outcomes as proof of race-week setup performance confuses the question each tool was built to answer.
How to evaluate a claim about AI-assisted NASCAR simulation
When a team, manufacturer or software vendor says AI or simulation improves racing preparation, the useful follow-up is not just “How advanced is the model?” Ask what task it supports and how its output is checked.
- Identify the purpose. Is the system supporting driver practice, setup comparison, safety validation, manufacturing or broadcast analysis?
- Ask what data goes in. The cited examples include prior race data, track scans, car measurements, tire data and driving inputs, but a particular source may document only some of these.
- Look for validation. For setup preparation, 23XI’s described correlation checks virtual behavior against real data. A claim of detailed simulation without a validation account leaves an important question unanswered.
- Separate AI from simulation. A simulator can use mathematical or physical models without AI. If AI is claimed, check whether the source explains its specific role rather than using the term as a broad label.
- Keep metrics in scope. A vendor case-study result, a historic computing example and a broadcast-data figure each describe different contexts; none should be presented as a general measure of current NASCAR setup performance.
- Account for human feedback. Driver impressions and engineering judgment are explicit parts of the 23XI workflow, so a complete description should say how people use the output.
The best-supported picture is therefore a layered one: measured data and models create a virtual setting; correlation checks whether it resembles a real case; teams use the model to explore options; drivers and engineers interpret the results. AI may contribute to motorsports preparation, as GM says it does, but the public evidence here does not establish AI as the universal engine of NASCAR setup decisions.
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