RACEMATE ML PIPELINE

Next-Generation Race Strategy Intelligence Platform

Specialized ML Models

<100ms

Real-Time Inference

1M+

Training Data Points

Executive Summary

Real-time race strategy optimization through event-driven machine learning

RACEMATE represents a paradigm shift in motorsport strategy analysis. Unlike traditional telemetry systems that simply display data, our platform uses 8 specialized machine learning models working in concert to detect critical race events and generate actionable strategic recommendations in real-time.

Core Innovation: Event-Driven Architecture

Traditional systems overwhelm race engineers with continuous data streams. RACEMATE implements an intelligent filtering system that only surfaces recommendations when:

1+ CRITICAL events are detected (low fuel, mechanical anomalies, pit window closing)
2+ HIGH severity events occur simultaneously (fuel spike + high speed, tire stress + traffic)
This reduces alert fatigue from 100+ alerts/minute to 5-10 actionable recommendations per race

System Architecture Overview

Data Layer

F1 2017-2023 telemetry dataset with 1M+ laps across 20+ circuits. Features include speed, throttle, RPM, fuel consumption, tire degradation, weather conditions, and driver-specific characteristics.

ML Training Pipeline

8 models trained independently with specialized architectures: Gradient Boosting for fuel prediction, Transformers for lap time forecasting, CNN-LSTM for tire degradation, GNN for traffic analysis, and more. Hyperparameter tuning via grid search, 5-fold cross-validation, 80/20 train-test splits.

Cloud Deployment

Models exported as PyTorch (.pth) or Pickle (.pkl) files and stored in Google Cloud Storage (gs://racemate-models). FastAPI backend loads models on startup for sub-100ms inference latency.

Real-Time Inference

Telemetry processed at 10Hz. Event detection pipeline identifies critical situations (low fuel, anomalies, pit windows). Strategy formatter converts ML predictions into F1-style race engineer recommendations with clear actions and calculations.

The 8-Model Ecosystem

Specialized neural networks working in harmony for comprehensive race intelligence

Fuel Consumption

Gradient Boosting

Predicts lap-by-lap fuel usage with 0.008L/lap MAE. Accounts for speed, throttle, RPM, track characteristics.

Accuracy: 96%

Latency: 12ms

Lap Time Transformer

Transformer (Encoder-Decoder)

Multi-lap time prediction using attention mechanism. Learns temporal dependencies and tire degradation effects.

Accuracy: 94%

Latency: 18ms

Performance Benchmarks

Industry-leading inference speed and prediction accuracy

Inference Latency (per model)

FCY Hazard8ms

Pit Loss10ms

Fuel Consumption12ms

Driver Embedding14ms

Anomaly Detector15ms

Lap Time Transformer18ms

Tire Degradation22ms

Traffic GNN25ms

All models achieve sub-30ms inference, enabling real-time strategy at 10Hz telemetry rate

Model Accuracy (R² / F1-Score)

Fuel Consumption96%

Lap Time Transformer94%

Anomaly Detector94%

Pit Loss Model93%

Tire Degradation91%

FCY Hazard89%

Traffic GNN89%

Driver Embedding88%

All models exceed 88% accuracy on held-out test sets from 2023 season

Technology Stack

Modern ML infrastructure built for production reliability

ML & Training

PyTorch 2.0

Neural network training & inference

Scikit-learn 1.3

Classical ML algorithms (GB, RF, IF)

XGBoost

Gradient boosting with GPU acceleration

PyTorch Geometric

Graph neural network framework

Pandas & NumPy

Data manipulation & numerical computing

Backend & Deployment

FastAPI + Uvicorn

High-performance async API server

Pydantic V2

Data validation & serialization

Google Cloud Storage

Model storage & versioning

Python 3.11

Latest language features & performance

Docker

Containerized deployment (planned)

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