Toyota GR Cup 2025 Racing Analytics Algorithm

Описание: Integrated Racing Analytics Dashboard: From Telemetry-Based Feature Engineering to Pre- and Post-Event Strategy Optimization in the Toyota GR Cup

Abstract
This manuscript describes the design, implementation, and empirical validation of an integrated analytics dashboard tailored for the Toyota GR Cup series, emphasizing telemetry-driven feature engineering and workflow components for pre-event forecasting, post-event analysis, and real-time strategy. We detail a scalable data ingestion and preprocessing architecture that recursively locates multi-source CSV telemetry and race-result files, handles common encoding and format issues, and applies memory-aware loading strategies to accommodate resource‑limited environments. Feature engineering modules convert raw telemetry (e.g., accelerations, speed traces, and sector times) into aggregated indicators: rolling means, consistency metrics, stint-aware lap counters, and pivoted telemetry matrices indexed by vehicle and lap. The analytics stack integrates a suite of predictive models (tree ensembles, CatBoost/XGBoost/LGBM variants, and lightweight deep learners) with visualization engines (Plotly, Bokeh, Dash) to produce five interactive dashboards: Main Analytics, Driver Insights, Pre-Event Predictions, Post-Event Analysis, and Real‑Time Strategy. Each dashboard is designed around actionable decision-making: driver-level coaching cues from sector variance and braking stability analyses; qualifying and race-pace simulations under alternative tire and fuel strategies; pit-stop optimization using simulated undercut/overcut gains; and post-event storytelling that synthesizes position-change timelines, pit analysis, and key race moments. We provide implementation details for integrating model outputs into HTML dashboards, including generating feature‑importance visualizations, prediction vs. actual plots, residual diagnostics, and strategy tables. The system supports serialization of models and preprocessors for consistent reproducible inference and describes operational heuristics for live deployment—incremental telemetry sampling, lightweight model inference, and web-based visualization hosting. Validation on Toyota GR Cup datasets shows that the dashboard enables simpler conversion of model insights into team decisions: pre-event predictions reduce expected race time by informing tire choice and pit scheduling in simulation, while post-event analysis highlights driver-specific training opportunities with sector-targeted recommendations. Beyond performance, we address practical constraints: robust handling of missing and malformed telemetry, strategies to limit memory usage, and guidelines to prioritize features for dashboard real estate. The paper closes with a roadmap toward automated A/B testing of strategy options and a discussion of how integrated analytics can shorten the loop between data collection, insight generation, and on-track execution for racing teams.
Keywords:
Racing analytics, Telemetry processing, Interactive dashboards, Pit strategy, Tire degradation, Driver insights, Pre-event prediction, Post-event analysis, Real-time analytics
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Github: https://github.com/tobimichigan/Integ...
Original Article Publication:
https://handsonlabs.org/integrated-ra...