Background

At the beginning of 2020, as soon as the season started, I found myself checking the list of registered competitors and categories and thinking: How can I increase my chances of having a good result? I didn’t know any of the competitors and teams. Basically, all I could do was to conduct a course recon and trust my physical conditioning.

Of course, I didn’t stop thinking about it, and in no time, I was checking each contestant’s name and searching for their information. I then discovered that USA Cycling (The American governing body for bicycle racing) has a history of all races and athletes, including amateurs and professionals. Bingo!

Learn more about the background and how I developed this project on this article.

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Data

The data is gathered from Zwift, including race lists, registered riders, categories, routes, distance, and elevation information.

I used ZwiftPower.com to collect historical data on each rider. Information such as weight, age, FTP, among others, were consolidated in a final list of the field for further analysis.

Route images are collected from ZwiftHacks.com

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Prediction Model

I am currently using machine learning and regression algorithms for the prediction model. To learn more about the prediction model, check my article about this project on this link.

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Privacy

If you do not want your data to be shown in the predictions, please click on feedback and send your Zwift ID or link to your ZwiftPower profile. This way we will anonymize your data.

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The App

This is a non-profit and open-source project. It was developed for academic and research purposes, using R language, Shiny framework, Shinydashboard, Caret, and others packages. Soon the link to the project's source on GitHub will be available. Collaboration for improvement will be more than welcome. License: GNU GPLv3

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Author