BlawkOps is built on open data. Six independent BJJ graph datasets, the Gracie Barra GB1 curriculum, and 120,279 labelled video frames from the University of Ljubljana. This page documents every source, what we extracted, and the people whose work made it possible.
We parsed six open-source BJJ position/transition datasets into a common schema: positions (nodes), edges (transitions, submissions, sweeps, escapes, passes), and metadata. Cross-source matching uses a 31-entry alias table to normalize position names.
96 techniques across 8 fundamental positions, mapped to BLISP notation with phase classification (define, control, attack/defend, transition) and perspective tagging (Me/Op). 30 cross-position transitions explicitly link positions into a directed graph.
| Code | Position | Techniques | Teaching Level |
|---|---|---|---|
| STND | Standing | 23 | Level 4 (takedowns, self-defense) |
| CGRD | Closed Guard | 22 | Level 1 (first position taught) |
| OGRD | Open Guard | 14 | Level 3 (sweeps, spider, guard pulling) |
| HGRD | Half Guard | 3 | Level 2 (underhook sweeps) |
| SCTR | Side Control | 12 | Level 0 (escapes, submissions, advancing) |
| MNT | Mount | 11 | Level 0 (chokes, armbars, escapes) |
| TRTL | Turtle | 6 | Level 0 (guard recovery, back take defense) |
| BCTR | Back Control | 5 | Level 0 (chokes, escapes) |
| Total | 96 | + 30 cross-position transitions |
Curriculum structure reconstructed from the Gracie Barra Fundamentals Program (GB1). Each technique is tagged with perspective (Me = playing the position, Op = opposing) and phase (define, control, attack/defend, transition). Teaching order follows the GB1 progression: Closed Guard first, Standing last.
120,279 labelled video frames with COCO 17-keypoint pose annotations from the University of Ljubljana's Visual Cognitive Systems Lab.
Hudovernik & Skocaj (2022). Video-Based Detection of Combat Positions and Automatic Scoring in Jiu-jitsu. MMSports'22, Lisbon.
CC BY-NC-SA 4.0 — University of Ljubljana, Faculty of Computer and Information Science.
BlawkOps would not exist without the open-source BJJ community. Every dataset listed here represents someone's decision to share their work freely.
Eelis van der Weegen built GrappleMap over years of solo work — 596 positions with full 3D skeletal data, released to the public domain. It remains the largest open BJJ position database.
Diogo Seca created BJJ Graph, the most edge-rich dataset we use. Its per-perspective modeling (top vs. bottom) and outcome branching (success/failure/counter) directly informed our duality and failure topology design.
Dave Yarwood mapped the Gracie University curriculum into a Clojure graph, giving us an independent Gracie-lineage reference to cross-validate against GB1.
iphoenix227 built Flow-State with belt-level skill tagging, gi/no-gi markers, and chain families — the only dataset that explicitly models pedagogical progression.
Felipe Cavani and Ian Wessen built minimal but structurally clean graphs (RDF and FSM respectively) that helped validate our fundamental position set.
Hudovernik & Skocaj at the University of Ljubljana created the ViCoS BJJ dataset — 120K labelled frames that made empirical validation of our algebraic position recognition possible.
Gracie Barra — the GB1 Fundamentals curriculum provides the pedagogical backbone for our teaching order and the 96-technique reference set.