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Collect, analyze, and leverage data to make informed strategic decisions. Data-driven teams win championships.
Scouting is the process of collecting and analyzing data about teams at a competition. It informs your match strategy, alliance selection, and gameplay decisions.
📊 The Scouting Pipeline
Collect Data (objective, subjective, pit) → Process & Calculate (OPR, EPA, pickability) → View & Analyze (viewer tools) → Create Outputs (picklist, match strategy, playbook)
Hard numbers and countable facts. This is the foundation of data-driven strategy.
What to Track:
How to Collect:
Observations and judgments that can't be easily quantified. Provides context to the numbers.
What to Track:
Rating Scales:
Information gathered by visiting teams in the pit area. Reveals robot capabilities before they compete.
What to Collect:
Pit Scouting Tips:
Raw data is useful, but calculated metrics provide deeper insights by normalizing performance across different alliance partners and opponents.
Estimates how many points a team contributes to their alliance's score on average.
Formula: Solve a system of linear equations using alliance scores and team compositions across all matches.
Estimates how many points a team prevents the opposing alliance from scoring.
Formula: Similar to OPR, but calculated from opponent alliance scores.
Predicts a team's contribution to alliance score, accounting for opponent strength. More accurate than OPR.
Used by: Statbotics for rankings and match predictions.
Relative skill level score based on wins and losses. Higher ELO = stronger team.
Note: Borrowed from chess; adjusts based on opponent strength.
Calculated as OPR - DPR. Gives credit to defensive play.
Why it matters: Recognizes that defense contributes to winning.
Custom metric combining objective data, subjective ratings, and calculated stats to rank teams for alliance selection.
Custom formula: Each team defines their own based on strategy.
📈 Which Metric to Use?
EPA is generally the most accurate for predictions. OPR is simpler and widely used. Pickability is best for alliance selection because it incorporates your specific strategy needs.
Scouting data flows through a pipeline from collection to actionable outputs:
Scouts gather objective, subjective, and pit data during matches and pit visits.
Data is entered into a database or spreadsheet. Calculate OPR, EPA, CCWM, and pickability scores.
Use a viewer tool (custom app, Tableau dashboard, or spreadsheet) to visualize team performance.
Identify top performers, complementary robots, and strategic matchups.
Generate picklist (ranked teams for alliance selection) and match playbooks (strategy for each match).
A picklist is a ranked list of teams you'd want to pick (or be picked by) for alliance selection. It's created by combining:
💡 Picklist Pro Tips
Before each qualification match, create a simple playbook:
🤝 Communication is Key
Share your playbook with alliance partners before the match. A coordinated alliance beats three individual robots every time.