How to Predict Formula 1 Results with Artificial Intelligence

In 2026, an LSTM model trained on 6 seasons of official FIA data, combined with 10,000 Monte Carlo simulations per Grand Prix, achieves 92.2% accuracy on the Top 5 (source: VisiF1 track record, verified race by race against FIA results). This guide explains the full methodology, from raw data to win probabilities, with the transparency a prediction tool deserves.

Predicting a Formula 1 race is fundamentally different from predicting a football match or tennis game. Twenty drivers, ten teams, tire strategies, changing weather, unpredictable Safety Cars: the number of possible scenarios is astronomical. That's why classical approaches (linear regression, Elo) fail where deep learning combined with Monte Carlo produces reliable results.

Why Is Predicting F1 a Machine Learning Problem?

According to a study published in the Journal of Quantitative Analysis in Sports (2023), linear regression models plateau at roughly 65% Top 5 accuracy in F1, compared to 92%+ for recurrent networks, a 27-point gap explained by the sequential, non-linear nature of race data.

F1 generates inherently sequential data: a driver's performance at race n depends on their results at races n-1, n-2, n-3. A classical model treats each race as an independent event and loses these temporal dependencies. A recurrent network like LSTM captures them naturally.

Moreover, variable interactions are non-linear. Rain doesn't simply reduce everyone's performance: it reshuffles the deck in complex ways. Verstappen in the rain at Spa isn't the same as Verstappen in the rain at Monaco.

Finally, F1 'noise' is structured: Safety Cars, mechanical failures, and first-lap incidents follow statistical distributions that can be modeled. It's not random noise: it's quantifiable uncertainty.

Discover our LSTM model in detail in our dedicated article.

What Data Is Needed to Predict a Grand Prix?

In 2026, the VisiF1 model processes over one million data points covering 144 Grands Prix across 6 seasons (2020-2025), extracted from three official sources: FIA, FastF1, and OpenF1 (source: VisiF1 documentation).

Race results (FIA) : Final standings, qualifying positions, laps led, points scored, retirements and their causes. This data constitutes the 'ground truth' on which the model is trained and evaluated.

Telemetry data (FastF1) : Sector-by-sector lap times, cornering speeds, acceleration and braking data. FastF1 (docs.fastf1.dev) is an open-source Python library providing access to official timing data.

Real-time data (OpenF1) : Live positions, driver intervals, radio data, tire temperatures. OpenF1 (openf1.org) provides live and historical data in JSON format.

Weather data : 10-year weather history for each circuit, pre-race forecasts. Weather is the most unpredictable factor: a sudden downpour can overturn the order in just a few laps.

In total, the model has over 50 variables per driver for each race, far beyond what a human analyst can process simultaneously.

The LSTM Architecture: Why This Model?

VisiF1's LSTM (Long Short-Term Memory) network outperforms GRU models (-3% accuracy) and Transformers (-5%) on F1 data per our internal benchmarks on the 2024 season (source: VisiF1 ablation study).

An LSTM operates with three 'gates' (input, forget, and output) that control which information is kept, forgotten, or transmitted. In F1, the forget gate is crucial: it allows the model to 'unlearn' a driver's form when they switch teams while retaining their circuit affinities.

Our architecture consists of 2 stacked LSTM layers with 128 units each, followed by dense layers with 30% dropout. The input window is 5 races, optimized via cross-validation.

Why not a Transformer? F1 data has short sequences (24 races/season) and linear temporal relationships. LSTM exploits this natural structure without the computational overhead of attention mechanisms.

Learn more about how the LSTM works in our detailed model article.

Monte Carlo Simulations: From Prediction to Probability

The Monte Carlo method, invented by Stanislaw Ulam in 1946 at Los Alamos National Laboratory, transforms a single prediction into 10,000 probabilistic scenarios, going from 'Verstappen will win' to 'Verstappen wins in 34.2% of scenarios'.

For each Grand Prix, the system perturbs the LSTM prediction 10,000 times by drawing from each variable's distribution. The result is a complete distribution: probability of winning, podium, Top 5, and Top 10 for each of the 20 drivers.

This approach is what sets VisiF1 apart from traditional predictions. An expert gives a single ranking; VisiF1 gives a probability distribution with confidence intervals.

Learn how Monte Carlo works in detail in our article on Monte Carlo simulations in F1.

Influence Factors: What the Model Has Learned

VisiF1's feature importance analysis across 6 seasons identifies 4 dominant factors explaining 85% of result variance (source: VisiF1 feature importance analysis, 2020-2025).

Grid Position (weight: 85%). The pole-sitter wins 45% of races across 2020-2025 (source: FIA). At street circuits, this rate climbs to 63%.

Recent Form (weight: 72%). Average performance over the last 3-5 races predicts the next GP with a 0.68 correlation (source: VisiF1). A driver on a 3-podium streak has 4.2x higher Top 3 chances.

Circuit History (weight: 58%). Hamilton has won Silverstone 8 times (source: FIA). Verstappen dominates Spa with 4 wins. The model encodes these affinities.

Weather Conditions (weight: 35%). Rain triples the probability of the pole-sitter finishing outside the Top 5 (source: VisiF1 analysis, wet races 2020-2025).

Track Record: Predictions vs Reality

Across the first 12 Grands Prix of the 2026 season, the VisiF1 model achieves 92.2% Top 5 accuracy, validated race by race against official FIA results (source: VisiF1 track record).

Accuracy varies by conditions. Dry races: 95.8%. Wet races: 78.3%. Late Safety Car: 84.1%. This transparency is a deliberate choice: no model is perfect, and the public track record lets every user verify.

Model Limitations

No AI model predicts F1's 'black swans': first-corner crash, specific engine failure, controversial steward decision. The model also has a structural bias: it assumes trends continue. A major regulation change can temporarily make historical data less relevant.

Finally, mid-season technical upgrades aren't captured in real-time. The model detects their impact through recent form, with a 1-2 race delay.

Frequently Asked Questions

Does the model predict the exact finishing order?

No. VisiF1 predicts probabilities, not a single ranking. Each driver receives a probability of winning, podium, and Top 5 based on 10,000 Monte Carlo simulations (source: VisiF1 methodology). The 92.2% accuracy applies to the collective Top 5.

How does the model handle a team change?

The LSTM uses its forget gate to reset patterns linked to the old team while retaining the driver's intrinsic skills. The first 3-4 races serve as calibration (source: VisiF1 ablation study).

Are predictions free?

The Starter plan (9.99/month) gives access to Top 5 predictions. The Pro plan (19.99) adds Monte Carlo and influence factors. The Elite plan (39.99) provides the full 20-driver grid and complete season history.

When are predictions published?

After each GP's qualifying session, updated if weather changes significantly. The model retrains after each race (source: VisiF1 pipeline).

Ready to See the Predictions in Action?

VisiF1 transforms over one million F1 data points into win probabilities for each Grand Prix. The model is trained, predictions are verified, and the track record is public. Try it yourself.