Turn match data into winning strategy.
Scouting & Strategy is the intelligence branch of an FRC team: the people and systems that observe every robot at an event, turn those observations into reliable data, and use that data to make decisions that win matches. This department designs scouting systems (paper, apps, or QR-based tools like QRScout), tracks the right game-specific metrics, blends collected data with public analytics from The Blue Alliance and Statbotics, builds picklists for alliance selection, and writes the pre-match and in-match plans the drive team executes. Strong scouting routinely separates a good robot from a winning alliance.
nobody reads these in order, that is fine
modules
8
lessons
32
reading time
21hours
mastered
0per cent
reading is free, no account needed
the path
A start-here primer that teaches the spreadsheet and basic-statistics skills every scout needs before collecting or analyzing FRC match data. You will learn how a spreadsheet actually works, how to summarize numbers with averages and spread, and how to turn those summaries into smart alliance and pick-list decisions. No prior experience required.
What scouting is, why it wins matches, and the two pillars: pit scouting and match scouting. You will learn what data is worth collecting and how scouting connects to every strategic decision your team makes at an event.
How to turn metrics into a working data pipeline: paper systems, scouting apps, and QR-based tools like QRScout. You will learn the three parts of any system, how to move data reliably, and how to keep it accurate.
How to read the public analytics that complement your own scouting: OPR, DPR, and CCWM on The Blue Alliance, EPA on Statbotics, and FRC rankings. You will learn what each metric means, its limits, and how to combine it with scouting.
How to convert all your data into decisions: building a defensible picklist, navigating the live alliance-selection draft, writing pre-match plans, and communicating during a match.
Five buildable, end-to-end projects that turn scouting theory into working artifacts. You will configure a real QRScout form for a season game, compute OPR by hand on a tiny example and then in a spreadsheet, pull live data from The Blue Alliance and Statbotics with real code, build an aggregation sheet that ranks robots by EPA components, and assemble a one-page pre-match prep sheet. Every example uses real REEFSCAPE (2025) numbers from the official game manual, real part/tool names, and runnable snippets so a sub-team can reproduce each result before its next event.
The failure modes that quietly sink scouting operations, and the debugging workflows that fix them. You will learn to diagnose and prevent mislabeled and missing data, measure and improve scout accuracy, recognize when OPR/EPA mislead you (defense, small samples, blowouts), keep the data pipeline alive when wifi and batteries fail at a venue, and avoid the human and analytical traps that turn good data into bad picks. Grounded in real REEFSCAPE scenarios and tools teams actually use.
Deep dives into the techniques top scouting programs actually use, anchored in real systems and seasons. You will study Team 1678 Citrus Circuits' multi-app pipeline, learn predictive match modeling and how Statbotics estimates win probability, design custom game-specific metrics that out-resolve generic OPR/EPA, build a data-driven defense-evaluation framework (the thing OPR cannot see), and engineer a full automated analysis pipeline that joins your scouting with public APIs. Every lesson ties an advanced concept to a concrete, reproducible practice.
Know something this department is missing?
Log in to contribute a lesson8 modules and 32 lessons, free to read without an account, written against the real Game Manual and the WPILib docs. Sign in when you want the ticks to stick and the certificate at the end.