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Scripts

You probably don't need this

If you have fewer than 20 teams or fewer than 5 judges, use the MLH Judging Example Sheet instead. It's a Google Sheet you can copy in one click and requires no technical setup.

This script is useful if you have many teams and want to automate score aggregation from a CSV export.

score_calc.py

Aggregates judge scores from a CSV file and produces a ranked list of teams.

Requirements: Python 3.6+ (no external dependencies — uses only the standard library)

Usage:

# macOS / Linux
python3 scripts/score_calc.py scores.csv

# Windows
python scripts/score_calc.py scores.csv

Input format: A CSV file where each row is one judge's scores for one team. See schemas/score-input.json for the full schema.

Minimum required columns: 

team_name,judge_name,technical,innovation,impact,presentation

Example scores.csv: 

team_name,judge_name,technical,innovation,impact,presentation
Team Rocket,Alice,8,7,6,8
Team Rocket,Bob,7,8,7,7
Byte Bandits,Alice,9,9,8,9
Byte Bandits,Bob,8,8,9,8

Output: 

=== HACKATHON RESULTS ===
1. Byte Bandits     avg: 35.3 / 40   (judges: 2)
2. Team Rocket      avg: 32.5 / 40   (judges: 2)

Edge cases handled: - Tied scores: teams with equal averages are listed alphabetically - Missing judge scores: averages are computed from judges who did submit scores; a warning is printed - Zero scores: treated as valid scores (not skipped) - Single judge: works fine

Adding the required field check

If you customize the rubric (see equity-pack/judging/rubric.md) and remove a required category, update the REQUIRED_CATEGORIES constant at the top of score_calc.py to match your rubric.