Professional teams, academies, and sportstech startups that have outgrown spreadsheets and generic platforms come to us when they need software built around their actual data model: their wearables, their video feeds, their coaching workflows, and their injury prevention logic.
Athlete performance dashboards that pull GPS, heart rate, and load data into a single coaching view, updated in real time
Wearable integrations with Catapult, WHOOP, Zebra Technologies, and custom IoT sensors so no device is an island
Video analysis pipelines with AI tagging, multi-angle sync, and coaching annotation tools built into the platform
Injury prediction models trained on your squad's longitudinal data, not a generic population dataset
The problem
Athlete data scattered across GPS devices, wellness surveys, video clips, and training logs, with none of it connected in one place?
Coaching staff making session and selection decisions from gut feel because the performance data is too slow or too fragmented to use in real time?
Injury prevention built on observation alone, with no predictive model that flags risk before the athlete breaks down?
Short answer
RaftLabs builds custom sports analytics software for professional teams, academies, and sportstech startups. We ship athlete performance tracking, wearable data integration, video analysis, coaching dashboards, and injury prediction AI, each built around your squad's own data model. A focused v1 launches in about 10 to 14 weeks at a fixed cost; the platform grows from there.
Sports analytics software collects, processes, and visualizes performance data from athlete tracking devices, video feeds, wellness surveys, and historical records so coaches, analysts, and medical staff can make evidence-based decisions about training, selection, and injury prevention. In professional sports and high-performance academies, sports analytics software development typically covers wearable data integration, video analysis, coaching dashboards, and AI-based injury prediction.
01 Diagnosis
Athlete data lives in five separate systems and no one sees the full picture
GPS load from the training pitch sits in Catapult. Wellness check-in scores are in a survey tool. Video clips are tagged in Hudl. Strength session data lives in a spreadsheet. Injury records are in a physio note. Each system holds one slice of the athlete's state, and no one in the building sees all five slices at once.The cost is a poor decision made from incomplete information. A coach clears an athlete for full training because the GPS numbers look fine, without seeing the drop in wellness score from the morning check-in or the load spike from the previous week. The injury that follows was predictable from data already sitting in the system.We build the data integration layer that pulls every source into a unified athlete profile, so the coach, the analyst, and the physio all work from the same picture.
Video analysis takes hours of manual tagging before a clip reaches the coaching screen
Post-match video review that requires a full-time analyst to clip, tag, and label every event before anyone else can see it is not sustainable at pace. When the next match is 72 hours away and the analyst is still building the clip library at 2am, the tactical meeting happens on incomplete footage or it gets shortened.The underlying problem is that video tagging is still a manual step. A coach wants to call up every third-phase attacking sequence from the second half. That query should return results in seconds, not hours. AI-assisted tagging that recognizes player positions, game events, and phase transitions removes the manual bottleneck and puts the footage in front of the coaching staff while it is still relevant.
Injury risk is spotted by a physio's intuition rather than a predictive model
According to a 2025 study published in PMC (PubMed Central), AI monitoring systems processing biometric data from wearable devices can detect subtle changes in an athlete's performance up to 48 hours before a potential injury might occur. Most clubs are not using that window.When injury prevention relies on a physio observing an athlete's movement in training and flagging concerns based on experience, the signal arrives late and only covers what one person can see. A load monitoring system that tracks acute-to-chronic workload ratio, flags statistical outliers against each athlete's own baseline, and alerts the medical team to rising risk can intervene before the injury happens rather than after it.
Performance reporting is a weekly presentation, not a live coaching tool
When the head of performance produces a weekly report from raw device exports, the data informing selection decisions is always at least five days old. A coach planning the Thursday session cannot see what the GPS data from Tuesday actually looked like until Friday at the earliest.The gap between data collection and data visibility is the gap between analytics as a coaching tool and analytics as a compliance exercise. Real-time dashboards that update as data arrives from the training pitch close that gap. The coach can call up an individual athlete's load, distance, and heart rate zone breakdown at the end of a session, not at the end of the week.
02 What we ship
We build the central performance database that stores every metric collected about every athlete: GPS and IMU data from the training pitch, session load and distance, heart rate zone distribution, strength and conditioning outputs, wellness and recovery scores, and historical injury records. Every data point is tied to the athlete, the session, and the date so trend analysis can span a full season.
Coaching and medical staff access athlete profiles through a dashboard that shows current form, load trend, and any system-generated flags in one view. The dashboard surfaces the data they need for the decisions they face: selection, load management, return from injury, and pre-match preparation.
Built for professional clubs managing squads of 25 to 200 athletes, high-performance academies tracking development pathways, and national federations managing multi-squad programs across centralized and decentralized training environments.
We integrate with Catapult GPS/IMU devices, WHOOP biometric straps, Zebra Technologies' in-venue motion trackers, Garmin sports watches, Apple HealthKit, Polar heart rate monitors, and custom Bluetooth and RF wearables via their APIs or raw data streams. Where a device does not publish a public API, we build a data ingestion layer from the raw output format.
The integration layer normalizes data from multiple device types into a consistent schema, so load data from a Catapult unit and heart rate data from a WHOOP strap appear in the same athlete timeline without manual reconciliation. New devices are added to the integration layer without rebuilding the platform.
Built for clubs using multiple device vendors across different training environments, sportstech startups building a multi-device performance platform, and organizations replacing manual data exports with an automated ingestion pipeline.
We build video analysis platforms with AI-assisted event detection and tagging, multi-angle video synchronization, and coaching annotation tools. An analyst defines the event types relevant to their sport: defensive set pieces, attacking transitions, one-on-one duels, set restarts. The AI model tags occurrences in new footage without a manual review pass through every frame.
Coaches query the clip library by event type, player, phase, or time window and receive a cut of relevant clips in seconds. Annotation tools allow coaches to draw on the frame, pin a note to a moment in the clip, and share a tagged sequence directly with an individual player or the full squad through the platform.
Built for professional clubs replacing a manual clip-build workflow, high-performance academies doing opposition analysis, and sportstech startups building a video analysis product for the club market.
We build role-specific dashboards for every user in the performance department: a session overview for the head coach, an individual load trend view for the physical performance lead, a readiness and injury risk summary for the head of medical, and an athlete-facing view showing personal metrics and session targets. Each role sees the data relevant to their decisions, not a single generic report.
Dashboards update as data arrives from the training pitch and the wearable devices, so the conversation after a morning session is informed by that morning's data. Configurable alert thresholds send notifications to the relevant staff member when an athlete's metric crosses a defined boundary.
Built for professional clubs that currently run performance reporting through weekly slide decks, high-performance centers replacing multiple disconnected reporting tools, and sportstech startups building a multi-role SaaS platform for the club and academy market.
We build injury prediction models trained on a squad's own longitudinal dataset: historical load, acute-to-chronic workload ratio, wellness scores, training session type, and prior injury records. The model learns each athlete's individual baseline and flags deviations that have historically preceded injury in that athlete's profile, not a generic population average.
The risk monitoring interface surfaces a daily readiness score for each athlete and a ranked risk list for the medical and performance staff. Each flag comes with the contributing factors behind it, so the physio acts on the signal rather than just trusting the output. Models are retrained on a rolling basis as new data accumulates across the season.
Built for professional clubs investing in injury prevention as a competitive advantage, high-performance institutes managing athlete availability across a full season, and sportstech startups building a predictive health product for the elite sport market.
We build multi-tenant SaaS platforms for sportstech founders selling performance, analytics, or coaching tools to clubs, academies, and federations. The architecture handles club-level data isolation, role-based access for coaches, analysts, medical staff, and athletes, subscription billing via Stripe, and per-club configuration of metrics, thresholds, and sport-specific data models.
Platform APIs expose athlete data and event webhooks so clubs can connect the sportstech product to their internal systems without a manual export. Mobile apps for iOS and Android give athletes and coaches access to their data from the training pitch without a laptop.
Built for sportstech startups shipping their first B2B SaaS product to paying clubs, performance tech companies scaling from single-club deployments to a multi-tenant platform, and sports federations building a shared data platform across affiliated clubs.
03 How we work
Trusted by
04 Track record
05 Why us
06 Questions
Yes. Integrating data from Catapult GPS/IMU devices, Hudl video, and other wearables or video tools into a single coaching platform is a specific type of build we take on. We map the APIs and data schemas during discovery, then build the integration layer and unified dashboard so your coaching staff work from one screen rather than switching between five tools.
Yes. Injury prediction accuracy depends on training the model on longitudinal data that reflects your sport, your training load patterns, and your athletes' individual baselines. We build the data pipeline to collect and store your squad's historical load, wellness, and injury records, then train and validate a model against that data. A generic off-the-shelf model applied to your squad will produce false positives and miss real signals. A model trained on your own data will not.
A focused v1, such as wearable integration plus a coaching dashboard, launches in about 10 to 14 weeks and starts around $25,000 to $45,000. From there the platform grows: full tracking, video analysis, coaching dashboards, and injury prediction AI reach roughly $90,000 to $160,000 as you add modules. Cost is fixed and agreed before each phase begins, so there are no scope-creep surprises.
Yes. We integrate with Catapult, WHOOP, Zebra Technologies, Apple HealthKit, Garmin, and custom Bluetooth or RF wearables via their published APIs or raw data exports. Where a device lacks a public API, we build a data ingestion layer from the raw output. Device coverage and integration approach are confirmed during the discovery phase before any cost is committed.
Yes. We work with sportstech founders building their first product for clubs, academies, or federations. The engagement starts with a discovery phase to define the data model, the user roles (coach, analyst, athlete, club director), and the minimum feature set that delivers real value to a paying club. From there we build an MVP that proves the product in a live environment, then iterate based on club feedback.
Off-the-shelf platforms like Catapult's own software or a standard athlete management system work well when your data model, reporting needs, and coaching workflows match what the platform was designed for. Custom software becomes the right choice when your sport, your squad structure, or your performance methodology requires behavior the platform's configuration layer cannot support: a specific load model, a proprietary injury risk algorithm, or a multi-tenant architecture for selling the tool to other clubs. The decision usually arrives at a specific point of product complexity or commercial ambition, not at the start.
Athlete biometric and health data requires careful access control and data handling. We design the data architecture so each athlete's health records are accessible only to authorized medical and coaching staff. The full squad list, the front office, and third parties do not have access by default. Data is encrypted at rest and in transit, access is logged, and the platform is designed with GDPR requirements in mind for clubs operating in regulated markets. Data handling requirements specific to your federation or governing body are confirmed during discovery.
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