Data
Every deployment produces more movement data, and every new hour of data makes the model harder to replicate.
Investors
Movement is the last major data modality without a foundation model.
Text has GPT. Images have DINO. Proteins have AlphaFold. WithoutBall is building the intelligence layer for human movement — self-supervised, validated on real data, and bootstrapped to a working proof of concept.
Thesis
Every data modality gets its foundation model. Movement is next.
One self-supervised model, trained on skeletal motion, that learns what skilled, healthy, and deteriorating movement looks like — and transfers to injury signals, scouting, and consumer movement health without task-specific labels.
| Modality | Foundation model | Status |
|---|---|---|
| Text | GPT | Established |
| Images | DINO | Established |
| Proteins | AlphaFold | Established |
| Movement | WithoutBall — Hierarchical Motion Transformer | Open — being built now |
Proof of concept
Validated, not projected.
One training campaign took the core scientific risk out of the company. These numbers describe what already happened — not a plan.
Built8×NVIDIA H100 GPUs in one continuous run
Built14days of uninterrupted distributed training
Built~2,700GPU-hours of compute
Built0labels — fully self-supervised
Built3capabilities validated on held-out real data
Built4co-founders — research, engineering, elite football
De-risked
What the proof of concept already answered.
Deep-tech risk is a queue of open questions. These five are closed.
| Risk | The question going in | Where it stands |
|---|---|---|
| Convergence | Does the architecture converge on real, noisy skeletal data? | Yes — training remained stable across the full 14-day campaign. |
| Objective | Can self-supervision learn movement structure without labels? | Yes — masked-reconstruction and multi-view consistency objectives produced embeddings that encode speed, stability, and joint asymmetries. |
| Missing data | Can one model handle occlusion and gaps natively? | Yes — missing-data handling is part of the training objective, not a preprocessing patch. |
| Execution | Can this team run multi-week distributed training at scale? | Yes — the founding team planned, executed, and monitored the 8-GPU campaign end to end. |
| 2D→3D | Does lifting ordinary video into 3D fit the same architecture? | Yes — lifting is learned as reconstruction from a lossy view, inside one forward pass. |
Market
Two markets, one model.
One R&D investment feeds two revenue channels: club intelligence for professional football (B2B) and movement health for everyone with a phone camera (B2C).
| Signal | Figure | Source |
|---|---|---|
| Sports analytics | $4.5B (2025) → $14B+ (2030), ~25% CAGR | Grand View Research; MarketsandMarkets |
| Injury economics | €3M–€10M — the cost of one major injury to a top club | Industry estimates of squad-value impact |
| Fitness technology | $12B+ | Statista |
| Wearables | $60B+ | IDC |
| Regular runners | 150M+ worldwide | World Athletics; IHRSA |
| Gait analysis today | €500–€2,000 per lab session; accessible to under 1% of athletes | Published laboratory session rates |
For a club, preventing a single major injury pays for years of analytics. For a runner, a phone camera replaces a lab visit that fewer than one in a hundred athletes can access today.
Platform
One model, a platform of products.
Every product below is a lightweight head on the same movement embeddings. Each additional product reuses the shared representation instead of a new stack — the marginal product gets cheaper to build.
Club intelligence — B2B
- InjuryRadar
- SkeletonAPI
- MoveSearch
- MoveID
Consumer movement health — B2C
- Injury prevention
- Running biomechanics
- Rehabilitation monitoring
- Movement readiness
384-dimensional movement embeddings — one shared representation
Hierarchical Motion TransformerSelf-supervised foundation model · zero labels
- InjuryRadarLead productRoadmap
Biomechanical early warning for the whole squad — subtle drift surfaced weeks before it becomes an absence.
- SkeletonAPIRoadmap
Clean 3D movement data from ordinary video, in a single forward pass.
- MoveSearchRoadmap
Semantic search across a season of movement — query behavior, not tags.
- MoveIDRoadmap
Player identification from gait alone.
- Injury preventionRoadmap
Personal biomechanical early warning from a phone camera.
- Running biomechanicsRoadmap
Form analysis and movement-quality insight for every run.
- Rehabilitation monitoringRoadmap
Objective recovery tracking between clinic visits — never a medical diagnosis.
- Movement readinessRoadmap
A daily readiness signal built from how you actually move.
Moat
One position, four compounding loops.
Tracking systems measure. Video tools tag. Labs analyze. Wearables count. Pose estimators detect. Nobody understands.
WithoutBall sits on top of pose estimation and beneath applications — a complement to tracking and video suppliers, not a competitor. The moat is not one barrier but four loops that tighten with every month of operation:
Products
New products are lightweight heads on shared embeddings — each launch widens the surface a rival must match.
Users
Longitudinal per-person baselines grow more valuable with use — switching away means abandoning your own history.
Trust
Clubs and clinicians extend trust slowly and keep it long. Evidence-first answers compound it month after month.
Team
Four founders, one intersection.
Replicating WithoutBall requires research-grade machine learning, elite football domain knowledge, and production engineering in one room. Each is scarce. The intersection is the moat.
CEO · Co-Founder
Kemal İnecik
Machine learning PhD at Helmholtz Munich & TUM, building deep generative models in one of the world’s leading AI-for-science labs. Turns cutting-edge research into real-world products.
Co-Founder · ML & AI
Fabian J. Theis
Professor at TUM and Director at Helmholtz Munich. Leibniz Prize laureate, ERC Advanced Grant holder, and one of Europe’s most cited scientists in machine learning for the life sciences.
Co-Founder · Football
Şeref Çiçek
UEFA Pro licensed coach with twenty-two years at elite level. Led Beşiktaş to back-to-back Süper Lig championships and coached the Turkey National Team. The domain expert who keeps the AI honest.
Co-Founder · Engineering
Özgür Ak
Mechanical engineer bridging hardware sensing, biomechanics, and scalable software. Designs the systems that turn raw motion data into production-grade intelligence.
Funding status
Bootstrapped to a validated foundation model.
100% founder-owned, no external capital, no debt. A seed round is in progress to fund model scaling, first club pilots, and the consumer beta.
| Position | Status |
|---|---|
| Ownership | 100% founder-owned — a clean cap table |
| External capital | None — bootstrapped through the proof of concept |
| Debt | None |
| Stage | Pre-revenue · deep-tech R&D |
| Round | Raising a seed round |
| Partnerships | In active discussions with professional football organizations; active research collaboration with Helmholtz Munich & TUM |
The large majority of new capital goes to R&D and engineering — our most effective sales tool is a working model demonstrated to the right person. Only a small share goes to marketing.
Roadmap
Milestones, not dates.
We sequence the plan to resolve the highest-uncertainty questions first, with the cheapest possible experiments. Phases advance on milestones, not on the calendar.
- 01
Validate Roadmap
Benchmark the model against off-the-shelf pipelines, scale the data pipeline, and open the first pilot conversations.
- 02
First Revenue Roadmap
Convert pilots into the first paying club deployments.
- 03
Consumer Beta Roadmap
Open the consumer app beta while the club base grows.
- 04
Growth Roadmap
Platform API and adjacent verticals beyond football.
Data room
The detail lives behind an NDA.
Detailed financials, cap table, and partnership information are available to qualified investors under NDA — info@withoutball.com.