Data Driver
METHODOLOGY

How we predict

We do not pick winners. We assign probabilities. When we say a driver has a 28% chance of winning, we mean that across many similar situations, that outcome would occur roughly 28% of the time.

model status LIVE API

Our system connects directly to primary F1 data sources, engineers 52 predictive features, fuses four distinct signals (Elo, ensemble model, recent form, team performance), and runs 10,000 Monte Carlo simulations. Every prediction is published before the race and scored after.

Read the complete guide →See our track record
01 / THE PIPELINE

↓Tap any stage to explore the detail

Each feature is normalised, validated against outliers, and weighted by recency. The feature store regenerates completely before each race weekend.

  • Qualifying pace delta vs teammate
  • Race pace & tyre degradation curve
  • Circuit-specific historical performance
  • Weather conditions & forecast accuracy
  • Safety Car & VSC probability
  • Constructor development trajectory (R-squared slope)
feature_engineering.py
# 52 features per driver-race combination
def build_features(driver, race):
  features = {}
  features['quali_pace_delta']   = quali_delta(driver, race)
  features['tyre_degradation'] = deg_curve(driver, race, compound)
  features['circuit_history'] = track_score(driver, race.circuit)
  features['dev_slope'         = constructor_trend(driver.team, n=5)
  return normalise(features)  # 52 keys
02 / THE APPROACH
01

Primary Data

We connect directly to primary F1 data sources. No third-party API dependencies. Every data point is traceable to its origin.

  • F1 Live Timing (real-time telemetry)
  • formula1.com (official results)
  • FIA (stewards decisions, regulations)
  • Historical archive (1950 to present)
02

Feature Engineering

Raw session data is transformed into 52 predictive features for each driver-race combination.

  • Qualifying pace
  • Race pace & tyre degradation
  • Circuit history
  • Weather conditions
  • Safety Car probability
  • Constructor development trajectory
03

Model Inference

An Elo rating system combined with recent form and team performance generates calibrated probabilities, validated by 10,000 Monte Carlo simulations.

  • Elo ratings with regulation resets
  • Recent form + team car performance signals
  • Monte Carlo simulation (10,000 paths)
  • Dynamic weighting as season data grows
03 / OUR MODELS

Race Winner

Win probability for each driver, combining Elo ratings with recent form and car performance

Elo ratingRecent resultsTeam performanceCircuit type
—live top-1 accuracy

Podium

P(podium) for each driver via Monte Carlo position distribution

Force scoreGrid positionRace paceSafety car rate
—live top-3 accuracy

Safety Car

Probability of Safety Car, VSC, or red flag during a race

Circuit incidentsWeatherGrid spreadHistorical SC rate
—not scored here

Monte Carlo

10,000 race simulations producing full position distributions and expected points

Driver forceDNF ratesSafety carGrid order
10Ksims/race

Championship

Season-long points simulation and title probability

Race predictionsReliabilityCalendarRegulation impact
0.3822live skill vs baseline

Calibration

Probability calibration and scoring against standard baselines

Brier scoreLog lossSkill scoreDecomposition
0.0371live overall Brier
04 / TRACK RECORD

Every prediction we publish is scored against reality. No cherry-picking, no retroactive adjustments. The full history is public.

What is a Brier score?

The Brier score measures the accuracy of probabilistic predictions. It is the mean squared difference between predicted probabilities and actual outcomes. A perfect score is 0.000. A coin flip on a 20-driver field scores approximately 0.090.

We also compute a skill score: how much better (or worse) our predictions are compared to naive baselines like grid position or championship standings. A positive skill score means our model adds value over simpler approaches.

The same scoring system is used in meteorology, epidemiology, and quantitative finance. It penalises overconfidence and rewards honest uncertainty.

View our full track record

154

Predictions scored

—

Accuracy

0.037

Brier score

05 / DATA INDEPENDENCE

Our data infrastructure is fully independent. Zero third-party API dependencies in production. We connect directly to primary sources, ensuring reliability, speed, and complete data traceability.

F1 Live Timing

Real-time telemetry, lap times, positions, race control via SignalR

formula1.com

Official race results, driver profiles, team data, calendar

FIA

Stewards decisions, technical directives, regulations, penalty points

Historical archive

Complete race data from 1950 to present, digitised and normalised

06 / MULTI-SERIES

The same methodology, adapted per series. Our architecture is designed to support multiple motorsport championships from a single platform.

Formula 1

Active

Since 1950

Formula E

Coming soon

Since 2014

WEC

Coming soon

Since 1953

IndyCar

Coming soon

Since 1996

WRC

Coming soon

Since 1973