Training Data Architecture
Connected the application to PostgreSQL and organized the data required for athletes, exercises, workouts, lifting sets, wellness entries, and performance reporting.
Project File 02
CrossFit Training Intelligence
A Python and PostgreSQL training-data system built to organize CrossFit workout information, monitor athlete wellness, track personal records, and generate useful coaching reports.
Role
Developer & Systems Designer
Technology
Python · PostgreSQL · Flask
Status
Functional Project
Project Identity / 02
Project Overview
Turning training data into useful decisions.
01 / The System
CrossFit Training Intelligence is a Python application for recording workouts, exercises, lifting sets, wellness check-ins, gym activity, and athlete performance data in PostgreSQL.
02 / The Purpose
The project brings information that is often scattered across notebooks, spreadsheets, and separate fitness applications into one organized system for athletes and coaches.
03 / The Approach
I designed the application around practical training workflows. Users can enter workout information through command-line tools or API requests and then review readiness, personal-record, coaching, and training-load reports.
System Components
What I built.
Workout & Wellness Logging
Built tools for recording lifting sets, gym activity, and wellness check-ins so training performance can be evaluated alongside athlete readiness.
Athlete Reporting
Developed reports for personal-record checks, training load, readiness, coaching information, and potential injury-risk indicators.
Flask Dashboard & API
Added a Flask web dashboard and API endpoints for accessing reports, entering training data, completing wellness check-ins, and initializing sample data.
Technical Summary
CF / 02
Project Stack
Python. PostgreSQL. Flask.
The project combines a Python package and command-line interface with PostgreSQL data storage, a Flask web server, JSON API endpoints, sample-data tools, and athlete-focused reporting.