TL;DR

  • An undercut “wins” in a simulator when the time gained on fresh tyres (especially the outlap + first flying lap) beats the opponent’s in-lap + pit loss + warm-up deficit.
  • An overcut “wins” when clean air, tyre life, and degradation slope let the staying-out car produce a faster lap-time profile than the new-tyre car stuck in traffic.
  • You’ll learn the simulator’s practical decision tree: warm-up → traffic → degradation slope → rival reactions, and why each step can flip the call.
  • Run both branches in the Tyre Strategy Simulator by changing only one variable at a time (warm-up, pit loss, traffic penalty, deg) to see what actually drives the result.
  • Interpret outputs as conditional (“if these inputs hold…”) rather than predictions, and stress-test the strategy that’s most robust to uncertainty.

An undercut vs overcut debate is usually framed as “who had the faster car” or “who called it first.” In reality, it’s a timing problem: you’re trading a known pit stop time loss for a probabilistic lap-time gain that depends on tyre warm-up, traffic, and how quickly the current stint is falling away. The most useful takeaway isn’t a single rule (“always undercut at X laps”)—it’s learning what the model is actually comparing, and how sensitive that comparison is to small input changes.

That’s why this post is tools-first. If you want a grounded answer for your scenario, don’t memorize heuristics—run the scenario in the Tyre Strategy Simulator, then perturb the assumptions until you can explain why the recommendation changes.

Undercut vs overcut: what the simulator is really trying to decide

In a tyre strategy model, “undercut vs overcut” is not a philosophical choice; it’s a delta calculation.

  • Undercut: pit earlier, accept pit loss now, and attempt to gain time with fresher tyres while your rival stays out on degrading rubber.
  • Overcut: stay out longer, preserve track position, and attempt to gain time because the pitting car’s outlap is compromised (warm-up, traffic) while you have clean air and your current tyres are still in a decent performance window.

A key point for interpreting any output: the simulator doesn’t “know” what will happen. It evaluates if-then branches using inputs. The recommendation is only as good as the assumptions for pit loss, tyre warm-up, traffic penalties, and the degradation curve.

Also, from 2025 onwards you should assume no fastest lap bonus point, which subtly shifts real-world incentives: the value is in position and total race time, not gambling a late stop for an extra point. In the Tyre Strategy Simulator, that means you should judge strategies primarily by net race time and track position outcomes, not by “can we steal fastest lap.”

The simulator’s decision tree (warm-up → traffic → deg slope → rival behaviour)

Different models implement these steps differently, but the logic is broadly consistent. If you understand the sequence, you’ll know exactly which knob to turn when the sim result “doesn’t feel right.”

1) Tyre warm-up: is the undercut even available?

The undercut only exists if the new tyre produces a meaningful advantage quickly enough. The simulator typically treats this as an outlap penalty (and sometimes a first-flying-lap penalty) compared to a fully “in-window” lap.

What matters:

  • Warm-up time constant: How many corners/laps until peak grip?
  • Outlap shape: Is the outlap just one slow lap, or is the tyre still compromised on lap 2?
  • Compound sensitivity: Some compounds deliver peak quickly but plateau; others take time but then hold.

In decision terms: the simulator asks, “If Car A pits now, what is the lap-time profile for the next 2–4 laps?” If the warm-up penalty is large, the undercut window narrows or disappears—because the rival’s in-lap + your outlap might not create enough net gain.

What to do in the Tyre Strategy Simulator: run two versions of the same scenario where you only change outlap penalty / warm-up. If the strategy flips, you’ve learned the most important thing: your conclusion is warm-up-driven, not “degradation-driven.”

2) Traffic: can the new tyre be used, or will it be wasted?

Even if warm-up is favorable, the undercut fails when the pitting car rejoins into traffic and can’t convert tyre advantage into lap time. Simulators handle this in a few ways: explicit traffic penalties, reduced effective pace, increased overtaking time, or a probability of being “stuck” behind slower cars.

Traffic is not just “slower laps.” It changes the entire cost/benefit of pitting because:

  • The outlap is already slow; adding traffic can make it catastrophically slow.
  • If you rejoin inside DRS range of a slower car, your “fresh tyre” becomes an overtaking problem instead of a lap-time weapon.
  • The rival who stayed out may now have clean air and can manage their degradation while you burn tyre trying to pass.

In decision terms: the simulator asks, “Does the pit rejoin land in clean air, and if not, what is the expected time loss until clear?” That expected time loss can easily exceed the theoretical undercut gain.

What to do in the Tyre Strategy Simulator: treat traffic as a first-class input. If your tool allows a traffic/clean-air adjustment, sweep it across a realistic range (e.g., minimal traffic vs moderate vs heavy) and observe how quickly the undercut becomes non-viable.

3) Degradation slope: does staying out get worse linearly, or fall off a cliff?

Most strategy arguments are really arguments about the shape of degradation.

  • If degradation is shallow and predictable, the undercut needs a very clean execution to work.
  • If degradation steepens late in the stint (a “cliff”), the undercut becomes more powerful because every lap the rival stays out is disproportionately costly.

Simulators often represent this as a per-lap degradation rate, a nonlinear curve, or an “ageing” model with temperature/energy effects. The key is not which formula is used, but whether your inputs imply:

  • Stable pace (low slope): overcut becomes more plausible.
  • Accelerating fall-off (high/curving slope): undercut becomes more valuable.

In decision terms: the simulator asks, “What is the delta between ‘stay out’ laps and ‘fresh tyre’ laps over the next N laps?” The bigger and faster-growing that delta, the more the undercut dominates.

What to do in the Tyre Strategy Simulator: don’t just enter one degradation number and trust it. Create two cases—“optimistic deg” and “pessimistic deg”—and see whether the recommendation stays stable. If it doesn’t, your real conclusion should be framed as a range: “undercut if deg is above X; otherwise hold track position.”

4) Rival behaviour: coverage, bait, and retaliation

Even a perfect lap-time model is incomplete if it assumes the opponent is passive. In real races, your rival’s response changes the payoff.

Common behaviours a simulator may approximate (explicitly or implicitly):

  • Coverage: the rival pits immediately to neutralize the undercut threat.
  • Delay: the rival stays out because they believe they can overcut (clean air, strong tyre).
  • Cross-over: both cars pit in adjacent laps and the race becomes a warm-up + pit-execution contest.

In decision terms: the simulator asks, “If we pit now, what is the rival’s best response, and what is the resulting expected position?” That means the output you’re reading may not be “what happens if they do nothing,” but “what happens if they react optimally (or according to a chosen response model).”

What to do in the Tyre Strategy Simulator: run the same stint with two rival assumptions: (1) rival covers immediately, (2) rival delays 1–2 laps. If your strategy only works when the rival makes the least competitive response, you don’t have a robust undercut—you have a conditional opportunity.

Turning the concept into inputs (so the calculator tells you something useful)

The fastest way to get misleading simulator output is to feed it precise-looking numbers that aren’t anchored to how races actually unfold. The better approach is to set up a baseline, then test sensitivity.

Start with four inputs that map directly to the decision tree:

  1. Pit loss (including pit lane time + any likely in/out loss at that circuit).

  2. Outlap warm-up penalty (and, if available, “lap 2 warm-up penalty”).

  3. Traffic model (either a direct penalty, an expected delay to clear traffic, or a probability of rejoining in dirty air).

  4. Degradation curve for each compound in the relevant fuel/track conditions.

Then do one disciplined thing: keep everything constant and adjust one variable at a time in the Tyre Strategy Simulator. If your undercut only works within a razor-thin band of warm-up or traffic assumptions, that’s a strategy with high execution risk—not a “best” strategy.

If you want more on interpreting “optimal,” it pairs well with Why “Optimal” Tyre Strategy Often Loses Races and What the Tyre Strategy Simulator Optimises For.

How to read the output without over-trusting it

A good tyre strategy calculator output is less “do X on lap 18” and more “here’s where the crossover happens.” Three interpretation habits keep you grounded:

First, look for the crossover window, not the single best lap. If the sim says pit on lap 18 is best by 0.2s vs lap 19, that’s not a real edge once you include operational variance (traffic, execution, minor pace changes). Treat that as “lap 18–19 is broadly the window.”

Second, separate deterministic from uncertain components. Pit loss is relatively deterministic; traffic and Safety Car risk are not. If the strategy only wins because the model assumes clean air, your real question is: “How likely is clean air?”—and you should rerun with moderate traffic.

Third, interpret deltas in racing terms. A “+1.5s net gain” is only valuable if it translates into position (or forces the rival into a worse tyre). If the net gain is inside DRS and the next stint is traffic-limited, the practical value may be close to zero.

Common misunderstandings (and how to stress-test them)

The biggest misunderstanding is thinking “undercut” is a universal truth. It’s a conditional weapon. If you’re unsure which side you’re on, the stress test is simple: in the Tyre Strategy Simulator, widen your uncertainty ranges and see which strategy remains acceptable.

  • If the undercut fails as soon as you add realistic warm-up or traffic, it’s not a plan—it’s an opportunity that requires circumstances.
  • If the overcut only works when degradation is extremely low, it’s a gamble that depends on tyres holding longer than expected.

The goal is not to produce one confident-sounding answer. The goal is to identify the driver of the decision (warm-up, traffic, deg slope, rival response), then build a strategy that is robust to the variables you can’t control.

Conclusion

Undercut vs overcut isn’t decided by slogans; it’s decided by a sequence of comparisons: how quickly the new tyre switches on, whether it can be used in clean air, how sharply the old tyre is falling away, and how the rival responds. If you can name which of those four is doing the work in your scenario, you’re already reading strategy like an analyst.

Now run it: set up your baseline and stress-test it in the Tyre Strategy Simulator. The best strategy isn’t the one that wins by a tenth in a perfect world—it’s the one that still makes sense when the world is messy.