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/
Note: Read-only access is sufficient. The Fabric only consumes data (match results, EPA ratings, event data) — no writes needed.