How do F1 teams model real-time tire strategy data beyond lap times?

[James96]

I've been following Formula 1 for a few seasons now, but I'm still trying to fully grasp the strategic nuances behind tire compound choices during a race, especially how teams balance the mandated two-compound rule with unpredictable safety car windows and track evolution. The commentary often mentions "undercut" and "overcut," but the real-time decision-making process seems incredibly complex. For more seasoned fans or those with technical insight, how do teams model these decisions in real-time, and what specific race data—beyond just lap times—do they prioritize when calling a driver in for an unexpected pit stop?

[StephenP]

Teams run a live model that blends pace, tire wear, and traffic. Beyond lap times, they watch pace deltas between compounds, estimate undercut/overcut windows, and factor in safety-car timing to decide when to pit.

[Donald.T]

In practice, the data inputs go well beyond timing: current tire temps and wear rate, projected degradation curves, fuel load, track evolution forecasts (rubbering in, grip changes with heat), pit-stop duration, and how many cars are in the pit lane or behind you. Teams simulate 3–5 laps of stints with each option to see who gains or loses track position, then compare the pit delta to the risk of losing time in traffic.

[Zachary_S]

A typical workflow looks like this: monitor tire state and air/track temps; run a quick forecast of pace with the current tires; simulate a handful of laps on new tires versus staying out; fold in traffic and pit lane flow; apply a decision threshold (e.g., only pit if you gain X seconds on track position or avoid a certain loss). The decision matrix then guides whether to pit now or wait for a window, like a safety-car phase.

[William72]

Two-compound rules and unpredictable safety-car windows add flexibility: sometimes an earlier undercut is preferable if the car behind will be held up in traffic, sometimes a late overcut pays off if the track is evolving fast and your old tires still hold a line. The art is balancing the physics model with the live drama of race conditions and the pit-crew’s reliability in the moment.

[OliviaOM]

Want a deeper dive? I can outline a data-flow diagram: sources (telemetry, tire telemetry, weather), modelling steps (pace deltas, degradation curves, traffic simulation), and decision points (pit timing gates, risk allowances). If you tell me the level you want—layperson overview or technical walkthrough—I’ll tailor it.