diff --git a/sources/frc/statbotics.md b/sources/frc/statbotics.md new file mode 100644 index 0000000..09867f8 --- /dev/null +++ b/sources/frc/statbotics.md @@ -0,0 +1,84 @@ +--- +type: data-source +name: Statbotics +url: https://www.statbotics.io/ +status: active +monitoring: continuous +--- + +# Statbotics — FRC Data Analytics Platform + +**URL:** https://www.statbotics.io/ +**Data Source:** Powered by The Blue Alliance +**Purpose:** Advanced FRC analytics with EPA (Expected Points Added) ratings + +--- + +## What It Is + +Statbotics modernizes FRC data analysis with the **EPA (Expected Points Added)** metric — a highly predictive measure of team performance. Better than OPR or Elo, EPA estimates a team's average scoring contribution to a match. + +Open-source, community-built analytics platform. + +## EPA Explained + +**EPA (Expected Points Added)** estimates how much a team scores in an average match using statistical inputs. It's predictive, not just historical. + +Key advantages over older metrics: +- **Predictive** — tells you what a team will likely do, not just what they did +- **Interpretable** — clear what the numbers mean +- **More accurate** — outperforms OPR and Elo for match prediction + +## Comparison to TBA + +| Feature | The Blue Alliance | Statbotics | +|---------|-------------------|------------| +| Raw data | Match results, rankings | Match results, rankings | +| Primary metric | OPR/CCRM | EPA | +| Prediction | Basic | Advanced | +| Visualization | Limited | Rich dashboards | +| API access | Yes | Yes (REST + Python) | + +**Use both together.** TBA for raw data and videos. Statbotics for analysis and prediction. + +## Primary Use for 2890 + +**Team 2890 analytics:** +- EPA rating over time (improvement tracking) +- Match prediction (likely score vs opponents) +- Event analysis (how did we perform vs expected) +- Comparison to other teams + +**Training applications:** +- Scouting data validation (EPA vs observed performance) +- Match strategy (what score is realistic against opponent) +- Team improvement tracking (is 2890 getting better over seasons?) +- Advanced analytics for students interested in data science + +## Key Sections + +| Section | Use | +|---------|-----| +| Teams | 2890 EPA rating, history, event performance | +| Events | Regional/off-season analysis | +| Matches | Predictive match scores | +| Compare | Head-to-head team comparison | +| API Docs | Build custom analytics tools | + +## Why It's in the Fabric + +Statbotics gives 2890 **predictive power** — not just "what happened" but "what will happen." EPA-based predictions help with: +- Match strategy (set realistic goals vs opponents) +- Scouting prioritization (which teams are threats) +- Robot capability benchmarking +- Season performance trends + +**Data-driven decision making** — empirical predictions, not gut feel. + +## APIs + +Statbotics offers REST and Python APIs for custom analytics. Students learning programming can build tools that pull real FRC data. + +--- + +**Source:** https://www.statbotics.io/ \ No newline at end of file