Wrangling F1 Data With R (The Book + Code and Data Samples)
Last updated on 2016-04-02
About the Book
As a driver of technological and engineering innovation, Formula One motorsport is unsurpassed in its relentless pursuit of improvement on a weekly basis. But whilst sports such as cricket and baseball provide a wealth of geekery for the stats fans, F1 seems to lag behind.
If you're attracted by F1's passion to push engineering and technology to the limit, this book will help you grab a range of Formula One datasets by the scruff of the neck and wrangle a wide variety of insights from them.
Using the latest in open source data analysis and visualisation techniques, you'll learn how to extract the stories that often go unnoticed from whatever Formula One data you can lay your hands on. And maybe, just maybe, you'll be able to use the skills you learn along the way outside of the F1 context...
Let your passion for Formula One drive your data skills to new heights... #f1datajunkie
The current release of the book is an early draft. Many of the chapters are in a raw and incomplete form; others aren't published yet. The preview will change regularly, containing anywhere between 30% and 80% of the the latest version of the paid for version. If you pay for the book, you get any and all updates to it for free. The book is priced as it is so that affiliate links work.
License
Table of Contents
-
Foreword
- A Note on the Data Sources
- The Lean and Live Nature of This Book
-
Introduction
- Preamble
- What are we trying to do with the data?
- Choosing the tools
- The Data Sources
- Additional Data Sources
- Getting the Data into RStudio
- Example F1 Stats Sites
- How to Use This Book
- The Rest of This Book…
-
An Introduction to RStudio and R dataframes
- Getting Started with RStudio
- Getting Started with R
- Summary
-
Getting the data from the ergast Motor Racing Database API
- Accessing Data from the ergast API
- Summary
-
Getting the data from the ergast Motor Racing Database Download
- Accessing the ergast Data via a SQLite Database
- The Virtual Machine Approach
- Getting Started with the ergast Database
- Asking Questions of the ergast Data
- Summary
- Addendum
-
Data Scraped from the Formula One Website (Pre-2015)
- Format of the Original scraperwiki.sqlite Database
- Format of the f1com_results_archive.sqlite Database
- Problems with the Formula One Data
- How to use the Formula1.com Data alongside the ergast data
-
Reviewing the Practice Sessions
- The Weekend Starts Here
- Practice Session Data from the Official Formula One Website Prior up to 2014
- Sector Times (Prior to 2015)
- Summary
-
Practice Session Utilisation
- Session Utilisation Charts
- Finding Purple and Green Times
- Stint Detection
- Revisiting the Session Utilisation Chart - Annotations
- Session Summary Annotations
- Session Utilisation Lap Delta Charts
- Summary
- Useful Functions Derived From This Chapter
-
A Quick Look at Qualifying
- Qualifying Progression Charts
- Improving the Qualifying Session Progression Tables
- Qualifying Session Rank Position Summary Chart - Towards the Slopegraph
- Rank-Real Plots
- Ultimate Laps
- Summary
-
A Further Look at Qualifying
- Clustering Qualifying Laptime by Session
- Purple and Green Laptimes in Qualifying
- How do Session Cut-off Times Evolve Over the Course of Qualifying?
- Summary
-
Lapcharts and the Race Slope Graph
- Creating a Lap Chart
- Lap Trivia
- Lap Position Status Charts
- The Race Summary Chart
- Position Change Counts
- The Race Slope Graph
- Further Riffs on the Lapchart Idea
- Summary
-
Race History Charts
- The Simple Laptime Chart
- Accumulated Laptimes
- Gap to Leader Charts
- The Lapalyzer Session Gap
- Eventually: The Race History Chart
- Summary
-
From Battlemaps to Track Position Maps
- Identifying Track Position From Accumulated Laptimes
- Calculating DIFF and GAP times
- Battles for a particular position
- Generating Track Position Maps
- Summary
-
Pit Stop Analysis
- Pit Stop Data
- Pit Stops Over Time
- From Pitstops to Stints
- Summary
-
Career Trajectory
- The Effect of Age on Performance
- Statistical Models of Career Trajectories
- Modeling the Perfromance of F1 Drivers In General
- The Age-Productivity Gradient
- Summary
-
Streakiness
- Spotting Runs
- Generating Streak Reports
- Streak Maps
- Team Streaks
- Time to N’th Win
- Looking for Streaks Elsewhere
- Summary
-
Keeping an Eye on Competitiveness - Tracking Churn
- Calculating Adjusted Churn - Event Level
- Calculating Adjusted Churn - Across Seasons
- Taking it Further
- Summary
-
Laps Completed and Laps Led
- Calculating Laps Completed and Laps Led Percentages
- Comparing laps led counts over seasons
- Comparing Laps Led Counts for Specified Circuits Across Several Years
- Laps Led From Race Position Start
- Summary
-
Event Detection
- Detecting Position Change Groupings
- Detecting Undercuts
- Summary
-
Comparing Intra-Team Driver Performances
- Intra-Team League Tables
- Race Performance
- Summary
-
Points Performance Charts
- Grid Points Productivity
- Maximising Team Points Hauls
- Intra-Team Support
- Points Performance Charts - One-Way
- Points Performance Charts - Two-Way
- Summary
-
End of Season Showdown
- Modeling the Points Effects of the Final Championship Race
- Visualising the Outcome
- Summary
-
Charting the Championship Race
- Getting the Championship Data
- Charting a Championship Points Race
- Charting the Championship Race Standings
- Summary
- Conclusion
- Bibliography
- Appendix - Converting the ergast Database to SQLite
Packages
The Book + Code and Data Samples
A copy of the book plus some example code files and data files referenced from within the book.
English
PDF
EPUB
MOBI
APP
The Book
English
PDF
EPUB
MOBI
APP
About the Author
Tony Hirst is a Senior Lecturer in Telematics in the Department of Computing and Communications at The Open University, and data storyteller with the Open Knowledge Foundation's School of Data.
The Leanpub 45-day 100% Happiness Guarantee
Within 45 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.
See full terms...
