
PITLANE
Formula 1 data visualization and Fantasy League
CS 426 Senior Projects Spring 2023
University of Nevada, Reno
Computer Science and Engineering
Team 17: Anthony Ganci, Noah Howren, Colin Martires
Project Manager: Zach Estreito
External Advisor: Dave Talari (Verizon)
Instructors: Dr. David Feil-Seifer, Devrin Lee
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ABOUT PITLANE
PITLANE aims to be the go-to web application for all Formula 1 fans that are interested in the intricacies of the motorsport all while delivering convenient and fun features such as a fantasy league and race notifications during the season. PITLANE processes raw data taken from the Ergast and FastF1 APIs and presents it to the user through easy-to-understand graphs. Users can then use the information they have analyzed from the graphs to form their ideal team in PITLANE's fantasy league feature. In addition to data visualization and fantasy league services, PITLANE offers users the choice to subscribe to receive push notifications about races and results during the season. PITLANE uses Vue.js for its front-end implementation while the back-end is implemented in Flask. A PostgreSQL database is used to store fantasy league and race telemetry data. User authentication and data storage is handled through Firebase. This project was developed as part of the CS 426 Senior Projects in Computer Science course at the University of Nevada, Reno during Spring 2023.

Project Video

Project Poster

Team 17
Project Related Resources
Problem Domain Book
Macrae, Callum. Vue.js: Up and Running Building Accessible and Performant Web Apps. O'Reilly, 2018.
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Useful Websites
1. Ergast
An API providing historical records of the Formula 1 racing series starting from the beginning of the world championships in 1950.
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2. FastF1
An API providing telemetry and race data from the Formula 1 racing series.
https://theoehrly.github.io/Fast-F1/
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Technical Reports, Conference Papers, Journal Articles
1. https://www.tandfonline.com/doi/full/10.1080/23750472.2022.2085619
This article discusses analyzing unpredictable data by using past racing data sets and by coefficient of variation.