Umpire Calendar

Type: Personal Workflow Automation – Python Web Scraping + Excel Dashboard

Overview

As a baseball umpire, selecting games that fit my schedule requires navigating an online scheduling system with limited sorting and filtering capabilities.

To streamline the process, I built a hybrid Python + Excel workflow that automatically retrieves available games, applies my personal filters, and presents them in a customizable dashboard for efficient decision-making.

The result dramatically reduces the hidden administrative time required to secure assignments.

The Challenge

The scheduling platform used for officiating:

• Has limited filtering and sorting tools
• Updates frequently as coaches add or modify games
• Makes it difficult to quickly identify games that match availability and travel preferences
• Requires manual searching and repeated checking

Finding the right assignments required more effort than it should.

The Solution

I developed a two-part automation workflow:

1. Python Web Scraper - Logs into the scheduling platform
   - Downloads all currently available games
   - Exports structured data to a CSV file
   - Includes direct links back to each game for quick requesting

2. Excel-Based Dashboard - Uses Power Query to ingest the CSV
   - Applies filtering based on personal criteria (distance, age level, etc.)
   - Integrates with a “Not Available” calendar to avoid conflicts
   - Provides slicers for dynamic filtering and prioritization

The system updates quickly and provides a clean, centralized decision interface.

Key Features

• Automated extraction of live scheduling data
• Travel-distance filtering
• Age and level-based filtering
• Conflict detection against personal availability
• Direct request links embedded in the dataset
• Rapid refresh as new games are posted

Impact

• Significantly reduced manual searching time
• Increased efficiency in selecting preferred assignments
• Improved scheduling accuracy
• Transformed a frustrating process into a structured workflow

This project reflects my approach to everyday problem-solving: identify friction, extract the data, structure it properly, and build a lightweight decision system that saves time and improves outcomes.