TL;DR

  • A tyre strategy simulator isn’t “guessing the winner” — it’s optimising an objective (usually minimising race time) under constraints like tyre degradation, pit loss, and stint lengths.
  • You’ll learn the four big terms that dominate strategy optimisation: time loss, degradation, track position, and traffic risk.
  • You’ll learn how to interpret outputs as conditional (“if these inputs hold…”) and where uncertainty (Safety Cars, weather, incidents) sits outside the model.
  • Run your own scenarios in the Tyre Strategy Simulator by changing one assumption at a time (pit loss, degradation slope, traffic penalty) to see what actually moves the “optimal” plan.

A good F1 strategy conversation starts with a simple question: what are we optimising for? If you don’t define the objective, “Plan A vs Plan B” becomes vibes — a mix of hindsight, highlight clips, and selective memory. Race strategy is a decision-making problem under uncertainty, and a tyre strategy simulator is a tool for making that decision-making explicit. The point isn’t to output one perfect stop plan; it’s to show which strategy wins given your assumptions, and which assumptions you can’t afford to be wrong about.

The objective function: what “optimal” means in an F1 tyre strategy simulator

Most tyre strategy models start with an objective function that looks like this in plain language: minimise total race time. That single sentence is the anchor for everything else. It’s also why strategies that “feel” conservative can still be optimal — if the model believes they reduce time lost to degradation, traffic, or extra pit stops.

In the Tyre Strategy Simulator, the interesting part isn’t that it can compute stint times. The value is that it forces you to be specific about the components of total race time, and then lets you stress-test them. If two strategies are separated by (say) 1–2 seconds over a full race distance, your next move shouldn’t be to crown a winner — it should be to explore uncertainty: how easily does that gap flip if pit loss increases, degradation steepens, or traffic risk rises?

Those “flip points” are where strategy actually lives.

1) Time loss: pit lane delta, tyre warm-up, and the cost of changing tyres

The most visible term in strategy optimisation is the pit stop cost: the time you lose by driving through the pit lane, stopping in the box, and rejoining. But a simulator typically needs to treat “pit loss” as more than a single number, because it bundles several effects:

Pit lane delta sets the baseline tax for any additional stop. If your pit loss is large, the model will lean toward fewer stops unless degradation is punishing. If it’s small, two-stopping becomes easier to justify — not because the tyres are suddenly better, but because the penalty for resetting them is cheaper.

Then there’s the less glamorous part: the first lap(s) on a new tyre aren’t always instantly fast. Depending on compound, track temperature, and car balance, you may pay a warm-up cost before the tyre reaches its working window. Even if your simulator represents this as a simple “out-lap penalty,” it changes the undercut math: the undercut is only powerful if the new tyre’s early-lap pace compensates for pit loss and for the rival’s tyre state.

This is why the Tyre Strategy Simulator is most useful when you treat pit loss and warm-up as inputs to challenge, not constants to accept. If your “optimal” plan requires an undercut that only works with unrealistically strong warm-up, the model is telling you something important: the strategy is fragile.

2) Degradation: the lap-time curve you’re actually modelling (even if you don’t call it that)

Degradation is where most strategy debates go wrong, because people talk about it as a single number (“the tyres are gone”) when the strategy problem depends on a curve: how lap time changes as a function of tyre age.

A simulator’s degradation model typically describes two things: the starting pace of a tyre (how fast it is when new) and the rate of decay (how quickly it slows). That’s enough to explain the core tradeoff between one-stopping and two-stopping. A one-stop accepts more laps on older tyres, so it needs degradation to be mild enough that the time loss per lap doesn’t exceed the pit loss you avoided. A two-stop “buys” fresher tyres more often, but must earn back the extra pit loss with faster average lap time.

The key is that you’re rarely certain about the shape of the curve. Degradation can be linear-ish, but it can also cliff late in a stint, or vary by compound, fuel load, and traffic. That’s why tool-first strategy analysis focuses on sensitivity: in the Tyre Strategy Simulator, nudge degradation up or down and watch where the optimum changes. If a strategy only wins in a narrow band of degradation assumptions, you shouldn’t treat it as “the correct call” — you should treat it as one branch of a decision tree.

3) Track position: the time value of clean air, passing difficulty, and “not being stuck”

Pure race-time optimisation assumes you can always convert theoretical pace into lap time. In reality, track position can block that conversion. This is the track-position term: the time you lose when you can’t drive the lap time your tyres allow.

In an idealised strategy model, you might assume clean air and let the fastest plan win on paper. But real races have cars. If you rejoin behind a slower car (or a train of cars), your effective pace becomes constrained by traffic and overtaking difficulty. That can make a seemingly aggressive, pace-driven plan underperform — not because the tyre math is wrong, but because the model omitted the time penalty of being stuck.

The practical way to handle this in a simulator is to treat track position as an adjustable penalty or constraint rather than an afterthought. If the “optimal” plan requires rejoining into heavy traffic, test how sensitive it is to a modest traffic cost. In the Tyre Strategy Simulator, that’s the difference between a plan that’s robust (still good if traffic is worse than expected) and one that’s brittle (only good if the rejoin is perfectly clear).

This also helps you interpret why teams sometimes choose a slower theoretical strategy: they’re not ignoring race time — they’re optimising race time under realistic constraints.

4) Traffic risk: undercut windows, overcut attempts, and the probability of losing time

Traffic is not just a deterministic penalty; it’s a risk. You might rejoin into clean air, or you might rejoin behind a car you can’t clear quickly. That uncertainty is why strategy is probabilistic even when the tyre model is deterministic.

An undercut attempt is a classic example. The undercut’s payoff depends on three things happening at once: the new tyre’s early-lap pace being strong, the rival’s old tyre pace dropping enough, and the out-lap/in-lap execution being clean. But it also depends on track position: if you pit and rejoin in traffic, the undercut can fail even if the tyre delta exists.

Similarly, an overcut is often a bet on the opposite: stay out, use clear air (or a rival’s compromised out-lap) to gain time, then pit into a better window. Again, the risk is traffic and timing, not the tyre equation alone.

A useful simulator doesn’t pretend to “know” the exact traffic outcome. Instead, it gives you a structured way to ask: how much traffic time can this strategy tolerate before it stops being optimal? Run that directly in the Tyre Strategy Simulator: add a realistic traffic penalty to the rejoin phase, and see if the plan survives.

What the simulator is not optimising (unless you explicitly model it)

It’s easy to misuse an F1 calculator by assuming it’s more omniscient than it is. A tyre strategy simulator typically does not optimise for things like Safety Car timing, red flags, changing weather, damage, penalties, or team orders — unless you build those scenarios in.

That limitation isn’t a flaw; it’s the boundary between planning and reacting. Strategy tools are strongest when they give you a baseline plan and clear thresholds for when to pivot. If you treat the output as a prediction, you’ll be disappointed. If you treat it as a decision aid — “Plan A is best unless degradation exceeds X or traffic exceeds Y” — you’re using it correctly.

And if your end goal is championship impact, remember the scoring context. From 2025 onwards there is no fastest-lap bonus point, which slightly reduces the incentive to chase late-race tyre flips purely to grab an extra point. That’s where a season-level tool complements a race-level tool: test the race strategy, then translate points implications into the bigger picture with the F1 Championship Calculator or explore downstream variance in the Season Simulator.

How to interpret “optimal strategy” outputs without over-reading them

When you run the Tyre Strategy Simulator, the highest-leverage habit is to treat every output as conditional. “Two-stop is optimal” really means: given these pit losses, these degradation curves, and these traffic assumptions, the lowest expected race time comes from two stops. If you change those inputs, you’re not “moving the goalposts” — you’re doing the job.

Two signals matter most:

First, the margin between strategies. If the gap is large, the plan is robust: you can be a bit wrong about degradation or traffic and still be fine. If the gap is small, the model is telling you the race is strategically sensitive. In that case, your focus should shift from picking a single plan to defining a trigger: what would you need to observe (tyre life, lap-time drop-off, traffic patterns) to commit to Plan B?

Second, the source of the advantage. If a strategy wins mainly because it avoids one pit stop, it’s sensitive to degradation being mild. If it wins mainly because fresh tyres are much faster, it’s sensitive to pit loss and warm-up. If it wins mainly because it keeps you in clean air, it’s sensitive to track position and overtaking difficulty. The simulator helps you identify which term is doing the work — and that’s what you should communicate when you discuss strategy.

Conclusion: use the simulator to find the fragile assumptions, not the “right answer”

An F1 tyre strategy simulator is at its best when it makes tradeoffs explicit: pit loss versus degradation, theoretical pace versus track position, and expected time versus traffic risk. If you want a clean way to explore those tradeoffs without hand-waving, run your next scenario in the Tyre Strategy Simulator. Change one variable, watch what flips, and you’ll get something far more actionable than a prediction: a strategy you understand.