Racing with Data: Formula 1 and NASCAR Analytics with R is a practical, professional handbook for turning raw racing data into actionable insight. Written for intermediate R users, the book walks through end-to-end workflows used in modern motorsport analytics: ingesting real datasets (e.g., historical results, lap times, basic telemetry), cleaning and transforming them with the tidyverse, visualizing performance with ggplot2, and building predictive models with caret, randomForest, and xgboost. You’ll analyze lap-time evolution and tire degradation in Formula 1, explore pit-stop strategy and position gains in NASCAR, compare driver styles through telemetry traces, cluster tracks by characteristics, and simulate “what-if” race scenarios with Monte Carlo methods.
Beyond the code, you’ll learn how to structure repeatable pipelines, validate models under class imbalance, communicate uncertainty, and package results in reproducible R Markdown reports and Shiny dashboards. Each chapter includes annotated scripts and guidance on interpreting outputs in the context of real race decisions: undercut vs overcut, two-stop vs one-stop, safety-car impact, and qualifying effects on podium odds.
Whether you’re a data scientist looking to apply R to high-octane problems, an engineer who wants a rigorous template for analyzing performance, or a fan eager to quantify strategy, this book gives you the tools to move from curiosity to robust, decision-ready analysis—lap by lap, stint by stint, race by race.
Pages: 33
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