The Complete FTC Scouting Guide: Data-Driven Strategy for Teams
Scouting is the difference between hoping for a good alliance and engineering one. This guide walks through how to collect match data at FTC competitions and turn it into smart alliance-selection decisions.
Why scouting wins matches
In FTC, alliance selection often decides who reaches the finals. The teams that pick the best partners are the ones with the best data — not the loudest opinions. Good scouting turns gut feeling into evidence.
What to track for every robot
Record objective, repeatable metrics each match: average points scored, autonomous reliability, cycle time, endgame consistency, and how often the robot breaks down or gets penalized. Consistency matters more than peak performance.
Build a simple scouting system
Assign one scout per robot per match and use a shared sheet or app with the same fields every time. Standardized inputs are what make data comparable. Capture a quick note for context — a great robot having an off match is still a great robot.
Turn data into alliance picks
Before selection, rank teams by the metrics that complement your robot. If you score well but struggle in endgame, prioritize a reliable endgame partner. Have a ranked backup list ready — picks move fast.
Practice the mindset on MECH
The same data-driven thinking that wins alliance selection powers MECH's Arena and Leaderboard — every match sharpens your ranking. Build the habit of competing and measuring your progress.