Your staff already collect data. The problem is that GPS files, wellness responses, video, medical notes, and training plans do not form one reliable record. We make the software layer that joins those inputs and turns them into decisions your coaches and performance staff can inspect.
One athlete profile with source, timestamp, session, and consent attached to every record
Device and video integrations assessed against actual API access and data rights
Role-specific dashboards for coaches, analysts, medical staff, and athletes
Explainable risk flags tested in shadow mode before they affect a training decision
The problem
Athlete data spread across wearables, spreadsheets, wellness forms, and video tools?
Performance reports arrive after the coaching decision they were meant to inform?
Risk scores appear without enough context for medical and coaching staff to trust them?
Short answer
Sports analytics software gives performance staff one governed view of athlete workload, wellness, video, and training data. RaftLabs makes custom platforms for clubs, academies, and sportstech companies whose existing tools do not share data or match their decision process. Risk models remain decision support and require validation before operational use.
Sports analytics software turns athlete, session, and competition data into a shared decision record for coaches, analysts, and medical staff. A custom platform becomes useful when the team's wearables, wellness tools, video systems, and internal methods cannot produce one timely and traceable view.
The first goal is not prediction. It is trustworthy data. Every reading needs an athlete ID, source, timestamp, unit, session, and quality status before a dashboard or model can use it safely.
01 Diagnosis
The same athlete has a different record in every tool
A GPS system identifies the athlete one way. The wellness form uses another name. Video tags use a shirt number. Medical staff keep a separate history. Joining those records by hand creates duplicates and makes trend lines hard to trust.The fix starts with identity and provenance. A unified athlete profile should retain the original source, measurement unit, collection time, and consent status. It should also flag missing or late records instead of silently filling gaps. The outcome is trend lines the medical team can actually trust.
Reports explain last week after today's session is planned
Raw exports often reach an analyst before they reach a coach. The analyst cleans the file, joins it to a squad list, and rebuilds the same report. By the time the report is ready, the next training plan may already be set.A useful dashboard starts from a named decision. It might show which athletes need a load review before the next session or whether a return-to-play plan stayed within its agreed limits. The view should show the evidence and its age, not only a coloured score. The outcome is reports that arrive before the decision, not after it.
A risk score looks precise before the model is proven
Sports-injury prediction remains a hard research problem. A 2026 systematic review indexed by PubMed found that only one of 18 reviewed studies had a low overall risk of bias. Most models relied on internal validation, which limits confidence that they will work for a different team or athlete group.That changes the product brief. A risk model should begin as decision support in shadow mode. Staff see the signal, the contributing factors, and the uncertainty, while the team compares forecasts with real outcomes. A person remains responsible for the training or medical decision. The outcome is risk flags staff act on instead of risk scores staff ignore.
Sensitive data moves further than the athlete expects
Fitness and health integrations can expose heart rate, recovery, sleep, location, and injury information. Apple HealthKit requires user permission for apps to read or share health and fitness data. The European Commission's GDPR guidance also treats health and certain biometric data as sensitive.The platform therefore needs purpose-based access, data minimisation, consent records, retention rules, and audit logs. Medical notes should not appear in a general coaching view by default. The outcome is data handling that survives an athlete complaint or a regulator's question.
02 What we ship
The foundation maps athletes, teams, sessions, competitions, and measurement sources into one data model. Incoming records keep their original unit and source. Validation rules catch duplicate athletes, impossible values, clock drift, missing sessions, and device changes before those issues reach a dashboard.
This layer also records who can see each data type and why. That makes later reporting, export, and deletion requests manageable instead of turning privacy work into a manual search across tools. The outcome is a foundation that doesn't need rebuilding for every new question.
Integration scope starts with documentation and account access. The WHOOP API exposes permitted workout and recovery data through OAuth scopes. Hudl lists video, tracking, and performance products that can work together, while its product documentation also shows that access differs by product.
We verify the exact endpoint, export, licence, refresh rate, and athlete identifier for every source. If a vendor does not expose the required data, the product plan says so and uses an approved import or leaves that source out of the first release. The outcome is integration scope you can defend, not promises you can't.
A head coach, analyst, performance lead, and clinician should not receive the same screen. Each view should answer a small set of role-specific questions and provide a path back to the underlying records.
A training review might show recent load, wellness completion, flagged gaps, and the sessions included in the calculation. A medical view can apply stricter access and show rehabilitation data without exposing it to the wider squad staff. The outcome is one platform every role actually opens.
Video becomes more useful when a metric can lead to the event that produced it. The platform can connect session timestamps, athlete identities, and tagged clips so an analyst can inspect the context behind a movement or workload change.
Automated tagging should be evaluated on the event types and footage the team actually uses. Low-confidence results go to review. The first release should avoid promising full automation across sports, camera angles, and lighting conditions before a representative test set exists. The outcome is video that answers questions instead of video that needs watching.
Risk work begins with a specific outcome, a defined prediction window, and a baseline. The team then measures sensitivity, false alerts, calibration, and performance across relevant athlete groups. Staff need to see why a flag appeared and record what they did about it.
The model must be easy to disable or roll back. It should never present a risk estimate as a diagnosis or a guarantee that an injury will or will not occur. The outcome is models that earn trust instead of demanding it.
The buyer should retain usable records if a device, analyst tool, or software provider changes. Documented exports, stable internal IDs, and a source-level audit trail reduce lock-in and support later analysis.
This is also what makes a platform ready for a sportstech company's customers. Each club can receive separate data boundaries, configuration, roles, and exports without sharing another club's information. The outcome is a platform you can sell, not just a platform you can use.
The first question is whether you need custom software at all. Most clubs don't need another dashboard. They need the data layer that makes dashboards trustworthy.
Keep or configure
Your wearable vendor's own analytics
When one source answers the question and the coach trusts it, keep it.
The spreadsheet that works
A workflow the analyst runs reliably every week isn't broken; it's just manual.
Vendor add-on dashboards
When the vendor's reporting covers your decision and you don't sell the platform onward.
Build custom
When data won't join
GPS, wellness, video, and medical records identify the athlete differently and no vendor connects them all.
When models need discipline
Risk flags that must run in shadow mode, show their reasoning, and switch off cleanly.
When you sell the platform
Sportstech companies shipping to clubs need multi-tenant data boundaries, not a shared spreadsheet.
Bottom line
Don't build a dashboard. Build the data layer that makes dashboards trustworthy.
A focused first release should prove one operational decision for one squad. It usually contains:
Video automation and injury-risk modelling come later unless validated data already exists. That order lowers delivery risk and gives future models a cleaner foundation.
04 How we work
RaftLabs built PDC, a remote patient monitoring platform, in 14 weeks. It connected more than four wearable device types to role-based portals and enrolled more than 25 clinics within 60 days. This is healthcare work, not a sports case study. It is relevant evidence for the adjacent engineering problem: reliable wearable ingestion, alerts, role boundaries, and sensitive-data handling.
05 Track record
08 Why us
09 Questions
Often, but we confirm access before promising an integration. We review each vendor's current API, export format, licence terms, identifiers, rate limits, and historical-data access. The first scope names which sources can sync automatically, which require approved exports, and which need a manual fallback.
No. A model should surface a risk signal and the factors behind it for qualified staff to review. It should not clear, restrict, diagnose, or treat an athlete on its own. We recommend shadow testing against real outcomes, subgroup checks, calibration review, and a documented human decision path before operational use.
Start with one decision, one squad, and the few data sources needed for it. A sensible first release usually includes identity matching, one or two reliable data connectors, a unified athlete timeline, a role-specific dashboard, data-quality alerts, and an export. Video automation and predictive models follow after the data foundation proves reliable.
A focused first release with one or two integrations, a unified athlete profile, and one decision dashboard usually fits a 10 to 14 week plan. Video analysis, custom device work, and validated risk modelling add separate phases. Final scope depends on data access and validation needs. We scope every project before pricing. Fixed cost, agreed before work starts.
Treat the data as sensitive from the start. Use the minimum data needed, record consent and purpose, separate coaching and medical access, encrypt data in transit and at rest, log access, define retention rules, and support deletion or export requests. The exact legal basis and safeguards require review for each country and competition.
Data analytics services
data models, reporting, and governed decision views
IoT application development
device ingestion, connectivity, and telemetry workflows
AI development
model evaluation, guardrails, and production AI systems
Sports and fitness software
consumer fitness, training, membership, and coaching products
Bring the systems, users, and decision you need to connect. We will identify the smallest release that can prove the workflow.
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