A compilation of resources for sport scientist building Athlete Management Tools in Shiny
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Updated
Aug 21, 2022
A compilation of resources for sport scientist building Athlete Management Tools in Shiny
Provides a pseudo API for TFRRS to query Athletes or Teams
Web Scraping from Asian Games 2018 (EN) Website
The source code of the Django app that is running my Athlete website.
Citation-grounded AI for athletes — performance nutrition, supplementation, recovery. RAG over USDA, NIH, CDC, WHO, and PubMed.
R script for athlete-level Force–Velocity profiling from ForceDecks exports. Implements Samozino’s method, dynamic slope optimization, and athlete-specific feedback. Includes regression plots, FVI classification, and batch analysis across multiple athletes.
AthleteType 運動人格測驗:28 題運動情境找出你的運動人格,附個性化運動項目建議、訓練方式與教練溝通指南
Production-grade TypeScript library for athlete & gym community platforms — pull-ups, workouts, exercises, XP, goals, analytics, nutrition, AI coaching, and rewards.
📊 Intelligent athlete performance analysis platform • Physical assessment management • Maturation-adjusted analytics • Position-specific scoring engine • Interactive dashboards • Built with Next.js, NestJS & PostgreSQL
Daily health metrics & composite athlete readiness score from Garmin Connect — CLI + Python API
This data analysis project utilizes Python for processing, with SQL for data extraction and libraries like NumPy and Matplotlib for data visualization.
A Flask application allowing the user to upload a ECG to a webserver and get a prediction back
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