Goal#
A single Google Sheet (or pandas notebook) that ranks every team at your event by capability, using your own scouting as the spine and public analytics as a cross-check. This is the artifact a picklist (covered in the existing strategy module) is built from, so here we focus only on constructing and validating it.
Columns to compute#
Start from your aggregated scouting (averages per team from QRScout output), then add derived metrics that map directly to REEFSCAPE scoring and ranking points. Use the real point values so the number is in actual match points. Because coral is worth more in auto than teleop, weight the two phases separately:
- Avg points contributed = auto coral
(3*avgAutoL1 + 4*avgAutoL2 + 6*avgAutoL3 + 7*avgAutoL4)+ teleop coral(2*avgTelL1 + 3*avgTelL2 + 4*avgTelL3 + 5*avgTelL4)+ algae(6*avgAlgaeProc + 4*avgAlgaeNet)+ auto leave(3*leaveRate)+ endgame average (Park=2, shallow Cage=6, deep Cage=12). - Auto reliability = fraction of matches with Leave + at least 1 coral scored in auto (this maps to the Auto RP, which needs all robots to leave AND at least one coral in auto).
- L4 rate = avg L4 coral per match. High L4 teams drive the Coral RP, which needs 5 coral on each of 4 levels (or on 3 levels if the Coopertition bonus is earned via 2 algae in each alliance's processor) at regular-season events — FIRST raises that bar to 7 coral per level for the FIRST Championship only.
- Endgame value = expected barge points. The Barge RP needs 14 barge points at regular-season events (FIRST raises this to 16 for the FIRST Championship only), so one deep climb (12) plus a park (2) — or two deep climbs (12 each) — clears it.
- Cross-check columns: TBA OPR, Statbotics EPA total, and Statbotics endgame EPA pulled via Project 3.
Spreadsheet recipe#
- Tab
raw: every scanned QRScout row. - Tab
agg:=AVERAGEIF(raw!$C:$C, $A2, raw!D:D)style formulas to average each metric per team (column A = team list). - Tab
rank: the derived columns above, plus aconsistencycolumn =STDEVof total points per match (lower is more reliable). - Sort
rankby Avg points contributed descending.
Validate before you trust it#
The whole sheet is worthless if the inputs are wrong, so run three checks:
- Match-sum check: for a sampled match, your three scouted robots' points should be within roughly 10-15% of the TBA
score_breakdownalliance total. Large gaps mean a scout miscounted or a robot was mislabeled. - OPR/EPA agreement: flag any team whose scouted average is far from both its TBA OPR and Statbotics EPA. That team gets a super-scout next match.
- Sample-size guard: hide ranks for teams with fewer than ~3 scouted matches; one bad partner alliance can wreck a small average.
Why component breakdown beats a single number#
Two robots can have identical total EPA but win you very different matches: one is a pure-offense L4 coral machine, the other a reliable deep-climb + algae robot that secures the Barge RP. A component sheet lets a strategist pick the robot that fills a specific gap (e.g., "we already have offense, we need a guaranteed climb"), which is exactly the decision alliance selection turns on.
the part worth keeping
Key takeaways
- Convert scouted counts into real match points using actual REEFSCAPE values, weighting auto coral (3/4/6/7) higher than teleop coral (2/3/4/5).
- Validate the sheet by comparing your scouted alliance sums against TBA score_breakdown and flagging teams where your data disagrees with OPR/EPA.
- Component breakdowns reveal robots with equal totals but different strengths, which is what alliance selection actually decides on.
Scouting & StrategyWorked Examples & Mini-Projectslesson 4 of 5
Keep going
Take the quiz+10 XP with an accountwhere this came from
Sources and corrections
This lesson is AI-assisted: drafted from primary sources, then reviewed and edited by hand. Errors still get through. When one is reported we fix it and write down what changed, in public, in the corrections log.
sources and further reading
- statbotics.ioThe EPA Model (Statbotics)
- blog.thebluealliance.comOPR and You - Basic FRC Strategy (TBA Blog)
- citruscircuits.orgCitrus Circuits Scouting Resources (Team 1678)
clipped to this lesson
Articles that go further on this
The lesson gets you through the topic. These go wider on it, and they read in one sitting.
- 19 min readWhat Is Statbotics? FRC EPA (Expected Points Added) ExplainedStatbotics explained: what EPA (Expected Points Added) means in FRC, how it's calculated, how EPA compares to OPR, and how to use it for scouting./blogread it
- 6 min readFRC Scouting Sheet Template: Printable Paper Sheet + Google Form to Sheets SetupA ready-to-print FRC scouting sheet plus a Google Form to Sheets setup that auto-averages one row per scout into per-team stats, with a 2026 REBUILT note./blogread it
- 7 min readHow FRC Competitions Work: Matches, Ranking Points, Alliances, and PlayoffsHow FRC competitions work: qualification matches, ranking points, alliance selection, and the double-elimination playoff format, explained for new teams./blogread it
answer sheet
Lesson quiz
All 4 right completes the lesson. Miss one and only that question comes back, anything you already answered correctly stays banked.
0 of 4 answered
01When converting scouted coral counts into real REEFSCAPE match points, how should the two phases be weighted?
02What does the match-sum check confirm when validating your ranking sheet?
03Why does breaking a robot down into component metrics beat ranking on a single total number?
04What does it take to clear the REEFSCAPE Barge RP?
Answer every question to submit.
All 32 lessons in Scouting & Strategyopenclose
01 / prerequisites
02 / scouting-fundamentals
03 / building-a-scouting-system
04 / data-analysis-tba-statbotics
05 / strategy-picklists-and-match-play
06 / worked-examples-and-mini-projects
- Not read yet:Project 1: Configure a QRScout Form for REEFSCAPE
- Not read yet:Project 2: Compute OPR by Hand, Then in a Spreadsheet
- Not read yet:Project 3: Pull Live Data with the TBA and Statbotics APIs
- Not read yet:Project 4: Build an EPA-Component Robot Ranking Sheet
- Not read yet:Project 5: Assemble a One-Page Pre-Match Prep Sheet
07 / common-mistakes-and-troubleshooting
08 / advanced-techniques-and-case-studies