📄️ Cycling Data Description
At this stage, this document only explores that data available in the “new Cyclist data.csv” file available from the T2 GitHub repository as we have not yet received Google Big Query access and therefore cannot yet explore the entire available dataset.
📄️ Cycling duration prediction models
A number of experiments were performed to test prediction models for duration of a workout based upon previous workout details. These experiments can been seen in the Python Notebook in the Project GitHub repository.
📄️ Cycling FTP prediction models
A number of experiments were performed to test prediction models for duration of a workout based upon previous workout details. These experiments can been seen in the Python Notebook in the Project GitHub repository. FTP is a critical performance metric in cycling, indicating the highest power a rider can sustain for an hour.
📄️ Developing ML Models for Football Prediction
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📄️ Functional Threshold Power
If you're interested in developing tools to assist cyclists in improving their performance, you've likely come across the term "FTP" or Functional Threshold Power. FTP is a pivotal metric within the cycling community that allows you to assess a cyclist's fitness level, establish accurate training zones, and create tools tailored to enhancing their strength and efficiency. Understanding FTP is crucial when building effective resources for cyclists looking to excel in their sport.
📄️ Heart Rate Zones
This document provides information on heart rate zones to support sports performance analysis.
📄️ Research and documentation
This area contains the research findings and other documentation for the Sports Performance Analysis project for T3 2023.
📄️ Power BI & GitHub Integration
Introduction
📄️ Power BI & Python Integration
Introduction
📄️ Sports Performance Overview
The purpose of this document is to provide a snapshot of all the Sports Performance analyses and capture the key objectives.
📄️ Strava Bulk Export Data Description
This document explains the origin of the Strava data used in the Cycling analysis sub-project which was obtained through a bulk export of a team members workout data. The data contained details for multiple sports but the cycling data can be separated for analysis as part of this project.
📄️ Web Scraping in Python
Introduction