Monte Carlo. Game Theory. Machine Learning. The Race Strategy Triangle for Formula 1.

Formula One race strategy is one of the most fascinating challenges in all of sport. During every race, teams face high-stakes situations, requiring them to make decisions under extreme uncertainty, anticipate competitor behaviour in real time, and respond to constantly changing track and system conditions.

In other industries engineers face similar uncertainties; fluctuating system dynamics, unexpected anomalies, and tight time constraints. But in Formula One, the “disturbances” are not just the system itself; they are the competitors, who are also trying to out-optimise you. That human element makes the challenge uniquely complex.

So, what tools are available to tackle this?

Monte Carlo Simulation: Exploring Uncertainty

Monte Carlo simulations have long been a cornerstone of F1 strategy. By randomising variables such as tyre life, pit loss times, and safety car probability, teams can simulate thousands (or millions) of race futures.

✅ Strength: Monte Carlo provides robust probability distributions, making it excellent for pre-race scenario planning. It helps teams stress-test strategies against uncertainty.

❌ Limitation: Competitors are treated as part of the random environment. Monte Carlo doesn’t inherently capture the way rivals adapt in response to your choices.

Monte Carlo is the strategist’s insurance policy: a wide lens on what might happen if the race plays out in a variety of ways.

Game Theory: Anticipating Rivals

Game theory frames strategy as an interactive problem. It models competitors as rational actors, trying to optimise their own outcomes in response to your moves.

✅ Strength: Game theory is powerful for anticipating rival reactions, for example whether another team will cover an undercut or extend a stint to block.

❌ Limitation: It relies on assumptions of rationality and complete information. In practice, teams sometimes act conservatively, irrationally, or simply have different objectives.

Game theory helps strategists think one step ahead. But in the chaos of a race, humans don’t always behave like rational agents.

Machine Learning: Capturing Empirical System Behaviour

Machine learning brings a new dimension to race strategy. Rather than relying on assumptions or randomisation, ML models learn directly from empirical system behaviour based on past race decisions and outcomes.

✅ Strength: ML can capture non-linear dynamics, adapt to live race conditions, and be customised for specific objectives – from minimising lap time to maximising team points.

❌ Limitation: ML models can become a “black box.” Without transparency tools – feature attribution, decision frameworks, and optimisation solvers – they can be difficult to interpret and trust.

The real power of ML is in its flexibility; teams can build targeted models trained on the systems and objectives they care about most, and update them in real time.

Putting It Together - The Strategy Modelling Triangle

Each of these approaches has a role to play:

• Monte Carlo is ideal for pre-race robustness – exploring uncertainty across thousands of futures.

• Game Theory adds a competitive overlay – anticipating how rivals may react.

• Machine Learning provides live adaptability – refining predictions and optimisation as conditions evolve.

Together, they form what we call the Strategy Modelling Triangle.

From Models to Decision Frameworks

At the end of the race, nobody needs to know about distributions or single-point predictions. What matters is outcomes:

Did the strategy maximise points gained or positions relative to rivals?

If not, was the model wrong – or was it used in the wrong way?

This is why a traceability framework is essential. Strategists need to see what each model was “saying” at the time, and why. For example:

• Monte Carlo: “Based on millions of scenarios, this is the probability distribution.”

• Game Theory: “If we make this move, this is how rivals are likely to respond.”

• Machine Learning: “Based on actual past races and current conditions, this is the optimal move right now.”

By combining these perspectives, strategy teams can make decisions that are robust, adaptive, and explainable.

Our Perspective at SIG Machine Learning

At SIG Machine Learning, we focus on the machine learning component of this triangle.

Our Nexgineer™ platform is designed to:

• Build and deploy targeted ML models trained directly on race dynamics.

• Integrate these models with Monte Carlo and game-theory frameworks.

• Deliver optimisation and decision-support tools that are transparent, adaptive, and tailored to each team’s workflow.

Because in the end, strategy isn’t about choosing one model. It’s about turning every tool — from simulations to ML – into an advantage.

And when domain expertise, intuition and innovation work together, strategy becomes stronger than ever.

Curious how machine learning can be integrated into your own race strategy environment to give your team the winning edge? Let’s talk,