The Concept

This tool helps you decide whether it's worth spending time optimising a recurring task. It's based on the principle of return on time investment - if you spend time making a task more efficient, how long will it take to "break even" on that time investment?

The Mathematics

The calculation involves several key components:

1. Time Calculations

We start with three primary variables:

  • T - Current time per task
  • F - Frequency of the task (converted to daily frequency)
  • P - Time period in days

2. Efficiency Improvement

The efficiency improvement E is expressed as a percentage. For example, a 60% improvement means the task will take 40% of its original time.

3. Break-Even Formula

The maximum time you can spend optimising is calculated as:

Mopt = T × F × P × E/100

Where:

  • T × F × P = Total time spent on task over the period
  • E/100 = Proportion of time saved

The Matrix

The efficiency matrix shows the maximum time you can spend optimising based on:

  • Y-axis: How much time you'll save per task
  • X-axis: How frequently you perform the task

Cells are grayed out when the optimisation time exceeds the potential time savings, making it inefficient to optimise.

Tech Stack

Backend (Python/Flask)

  • Built using Flask, a lightweight WSGI web application framework in Python
  • Core application logic handles time calculations and matrix generation
  • RESTful architecture for handling form submissions and page routing

Frontend (HTML/CSS/JavaScript)

  • Modern, responsive interface built with HTML5 and CSS3
  • Interactive elements powered by vanilla JavaScript
  • Dynamic matrix updates using client-side calculations
  • MathJax integration for LaTeX equation rendering

Workflow

When a user adjusts the time parameters or efficiency settings, the application:

  1. Calculates the maximum optimisation time for each frequency/duration combination
  2. Updates the matrix cells in real-time
  3. Generates a narrative explanation of the selected scenario
  4. Highlights inefficient optimisation scenarios

About Me

Hi there! I'm Kip, the creator of "Is It Worth the Time?". In my day job, I work as a Data & Analytics Consultant at NAB, where I help make sense of complex data and turn it into actionable insights.

This project was inspired by the classic XKCD comic about automation efficiency. I wanted to create an interactive tool that helps people make informed decisions about time investment in optimisation tasks. It's a practical application of the "work smarter, not harder" principle.

When I'm not working with data or building web applications, I'm exploring new technologies, reading fantasy books, or tinkering with personal projects. I'm also studying Data Science at RMIT University, diving deep into Machine Learning, AI, and everyone's favorite - statistics.

Want to connect? Feel free to reach out to me on LinkedIn. I'm always happy to chat about technology, data science, or interesting projects!

Kip Jordan