When:At every event. Fill this sheet in real time during matches you are not playing. Also use it 1–2 days before each tournament to research teams.
Match Scouting
Fill out a scouting report for every team you observe. Your records save automatically and are available for alliance selection and strategy planning.
📊
This is the Strategist’s primary competition tool. Fill out a scouting report for every team you observe during matches you aren’t playing. Your records save automatically and build your alliance selection list by end of quals.
📊 What to Watch For — Every Scouted Match
⚡ Autonomous
Does it run? Full pts or partial? Which side? Does it win the auton period?
⏱ Cycle Time
Count cycles in 30 seconds. Consistent? Fast intake? Reliable scoring?
🔧 Mechanism
Any jams? Mechanism failures? Code errors? Things that reset mid-match?
📊 End Game
Do they hang? Climb? AWP attempt? Score late or play defense?
✅ Before You Start Scouting — Confirm
✓ You know which team numbers to watch
✓ You have a match schedule in hand
✓ Timer ready to count cycles
✓ Notes saved after every scouted match
Team Information
Autonomous Performance
Estimated autonomous points:−0+
Driver Control
Game pieces scored:−0+
Driver control pts:−0+
★★★★★
★★★★★
Robot Observations
Saved Teams: 0
✅
Export tip: to share scouting data with your team, have each member scout different teams on their phone, then compare notes during lunch breaks. The data is saved to this device — not synced between devices.
ℹ️
Alliance selection strategy: look for teams with complementary strengths — not teams identical to yours. If you score well in driver control, pick a team with a reliable autonomous. If your autonomous is weak, find a partner whose autonomous is strong so your alliance covers both phases.
Top Teams by Category
Save scouting reports to see analysis here.
Alliance Notes
⚙ STEM HighlightMathematics: Statistics, Ranking Systems & Alliance Selection
Alliance selection is a multi-criteria decision problem with incomplete information. Scouting gathers data to reduce uncertainty. Key metrics: average autonomous score (expected value), average driver score (performance level), consistency (standard deviation — a low-σ partner is more predictable). Alliance selection strategy is also a game theory problem: the 1st pick wants to maximize alliance strength; later picks balance need vs availability. Tracking opponent scores lets you calculate strength of schedule — one of the official tiebreaker metrics.
🎤 Interview line: “Our scouting system collects data to support alliance selection as a multi-criteria decision problem. We track both mean performance (expected value) and standard deviation (consistency) for each team, because a consistent 12-point autonomous partner can be more valuable than an inconsistent 18-point one.”
▶ Next Step
Scouting data collected. Now plan your alliance selection and autonomous strategy for elimination rounds.
Team A averages 14 pts autonomous with σ=6. Team B averages 11 pts with σ=1.5. As the 1st seed picking first, which is the better alliance partner in a 12-match qualifier?
Team A — higher average always wins
Team B — their consistency (low σ) means you can reliably predict their contribution; Team A’s high variance is risky across 12 matches
Team A — you should maximize your expected score ceiling
Cannot determine without knowing their driver scores