GITNUXSOFTWARE ADVICE
Wellness FitnessTop 10 Best Sports Performance Tracking Software of 2026
Top Sports Performance Tracking Software ranking for teams and analysts. Compare Hudl, Stats Perform, Kinexon and more by features and tradeoffs.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Hudl
Structured video tagging tied to team and player context for repeatable coaching review.
Built for fits when teams need video-centric performance tracking with admin controls, not custom data-model ingestion..
Stats Perform
Editor pickGoverned event ingestion and access control with RBAC plus audit log coverage for dataset and workflow changes.
Built for fits when analytics and ops teams need governed, API-driven event data across matches and downstream tools..
Kinexon
Editor pickConfigurable event workflows that map telemetry streams into structured schemas for downstream automation.
Built for fits when sports teams need automated tracking workflows with an API and strict role-based governance..
Related reading
Comparison Table
The comparison table maps sports performance tracking tools by integration depth, data model design, and the automation and API surface used for provisioning and data ingestion. It also compares admin and governance controls such as RBAC scope, audit log coverage, and configuration patterns that affect extensibility, throughput, and schema consistency across feeds.
Hudl
team performanceSports video, tagging, and team performance workflows with athlete activity context, coach review tools, and administrative controls for organizations and schools.
Structured video tagging tied to team and player context for repeatable coaching review.
Hudl’s data model centers on teams, players, and video-backed activities that coaches can annotate and review. The platform’s automation and governance are driven through administrative configuration, role-based access, and controlled workspace structures for organizations that manage multiple teams.
A concrete tradeoff is that Hudl’s extensibility leans on workflow configuration instead of high-throughput, schema-first event ingestion for external systems. Hudl fits when coaching staff need consistent tagging and review across matches and practices, and when internal users can operate within Hudl’s existing data model.
- +Video tagging and analysis map directly to athlete review workflows
- +Team and player organization reduces fragmentation across seasons
- +Role-based access supports multi-team administrative separation
- +Configuration supports consistent coaching sessions and review patterns
- –Automation depends on built-in workflows, not custom event pipelines
- –Extensibility is limited if an organization needs schema-first ingestion
- –Throughput for high-volume external telemetry is harder to operationalize
Coaching staffs
Annotate and review practice performance
Faster review cycles
Athletic directors
Govern access across multiple teams
Lower data access risk
Show 2 more scenarios
Performance analysts
Compile athlete progress from sessions
More consistent summaries
Aggregate review outcomes from tagged activities into consistent internal reporting views.
Sports operations teams
Coordinate workflows for staff
Lower process variation
Configure repeatable session and review routines without building custom pipelines.
Best for: Fits when teams need video-centric performance tracking with admin controls, not custom data-model ingestion.
More related reading
Stats Perform
sports analyticsSports performance data platform with analytics, integrations, and enterprise governance for organizations that need event and athlete performance tracking in pipelines.
Governed event ingestion and access control with RBAC plus audit log coverage for dataset and workflow changes.
Stats Perform fits organizations that need consistent match event histories across scouting, performance analysis, and downstream reporting. The data model typically centers on entities such as competitions, teams, athletes, matches, and event types with structured attributes that align to replayable match states. Integration depth is measured by breadth of connectors and API coverage for provisioning, ingestion, and retrieval flows. Automation also matters, since repeatable jobs for transformation and publication reduce manual reconciliation between systems.
A tradeoff appears when teams require a fully custom schema, because schema extensibility often requires planning around the event taxonomy and mapping rules. Stats Perform works best when an internal data team can define governance standards and configure workflows for ingestion throughput and change control. It also fits situations where multiple departments share the same source-of-truth datasets while maintaining RBAC boundaries and audit log visibility.
- +Event-centric data model aligned to matches, players, and competitions
- +Documented API surface for ingestion, retrieval, and automation
- +RBAC and audit log support for cross-team dataset governance
- +Schema and configuration controls for extensible event attributes
- –Custom schema mapping can require upfront taxonomy alignment
- –Complex workflow configuration can increase time to operationalize
Performance analytics teams
Automate player event tagging
Repeatable analytics inputs
Data engineering teams
Provision pipelines for match data
Higher throughput updates
Show 2 more scenarios
Broadcast and content ops
Sync match timeline assets
Fewer manual sync tasks
Map structured match events into production outputs for synchronized timeline views.
Sports operations governance
Control access to datasets
Traceable data governance
Apply RBAC and audit logs to manage permissions and track configuration changes.
Best for: Fits when analytics and ops teams need governed, API-driven event data across matches and downstream tools.
Kinexon
tracking telemetryReal time athlete tracking with location and performance telemetry, plus software interfaces for sports operations, data integration, and reporting.
Configurable event workflows that map telemetry streams into structured schemas for downstream automation.
Kinexon’s integration depth shows up in how athlete and equipment data connects to session context so analysts and engineers can query unified records. The data model supports structured telemetry capture and event mapping, which reduces rework when exporting to BI or building custom analytics. Automation and configuration features support repeatable onboarding of teams and devices, instead of per-session setup.
A tradeoff appears with the governance surface. Organizations that need strict RBAC policies and audit-grade traceability must plan how roles and event permissions map to analysts, coaches, and engineering teams. Kinexon fits sports departments that already maintain a sports data pipeline and need predictable provisioning, automation hooks, and API-driven extensibility.
- +Integration-friendly data model linking athlete, session, and telemetry records
- +API surface for event ingestion, export, and external system synchronization
- +Automation supports repeatable device and workflow provisioning
- +Admin governance controls include RBAC and audit log reporting
- –Deep configuration can require dedicated engineering time
- –Event schema design demands upfront governance to avoid rework
- –Throughput tuning may be needed for dense multi-athlete sessions
Sports analytics engineering teams
Telemetry to custom event schema
Consistent analytics and fewer mappings
Team performance administrators
Device and athlete provisioning at scale
Faster setup and fewer errors
Show 2 more scenarios
Governing staff and compliance leads
RBAC and traceable data access
Cleaner governance and accountability
Apply RBAC controls and review audit logs for who accessed data and when.
Coaching operations analysts
Session-ready reports from event pipelines
More consistent post-session review
Generate session outputs by consuming structured events and normalized telemetry records.
Best for: Fits when sports teams need automated tracking workflows with an API and strict role-based governance.
Catapult
wearables platformSports performance measurement software paired with wearable tracking devices, supporting session reporting, athlete monitoring, and team analytics.
Catapult’s sensor performance data model with integration and API support for automated session and event ingestion.
Sports performance tracking in the team-sport stack often hinges on device integration, structured athlete data, and automation across workflows. Catapult focuses on those control points with a defined data model for performance events and built-in configuration for ingesting sensor and video outputs.
Documented integration options and an API surface support data synchronization, provisioning, and downstream analytics. Admin governance centers on access control and audit-ready operational practices for multi-user teams and organizations.
- +Structured performance data schema supports consistent event and session records
- +Integration paths for Catapult hardware and workflows reduce manual data reshaping
- +API and automation options support sync, enrichment, and external analytics pipelines
- +Admin controls include role-based access and operational governance patterns
- –Deep customization can require schema knowledge and careful mapping
- –High-throughput ingest needs capacity planning to avoid latency during busy fixtures
- –Automation workflows may be constrained when non-Catapult data types dominate
- –Cross-team governance depends on consistent provisioning discipline
Best for: Fits when sports organizations need sensor-to-analytics automation with governed access and a structured data model.
S&C Platform
training managementStrength and conditioning training management for athletes with programming, session tracking, and team visibility features built for sports coaches.
API-backed data access for program and athlete provisioning, enabling automated training workflow synchronization.
S&C Platform manages strength and conditioning program data and athlete progress across training cycles. The system supports structured workout logging tied to exercises, sets, reps, and performance metrics.
Configuration controls how programs are scheduled, assigned, and reviewed, with governance features for managing staff workflows. Integration depth and extensibility matter most in this entry, since external systems connect through documented API and automation surfaces for data provisioning and synchronization.
- +Structured data model for exercise, sets, reps, and performance history
- +Program assignment flows support repeatable training cycle workflows
- +API-first automation supports provisioning and cross-system sync
- +Admin controls support staff roles and managed athlete access
- +Audit-style accountability for changes to training content
- –Schema rigidity can slow custom metric definitions
- –Automation complexity increases when mapping data across external tools
- –Reporting configuration can require admin time for repeat use cases
Best for: Fits when staff need controlled program assignment and athlete tracking with API-backed automation across tools.
TeamBuildr
workout trackingWorkout tracking and programming for teams with athlete logs, coach assignments, and workflow features for sports training departments.
TeamBuildr API-backed session and metric ingestion with a configurable performance data model.
TeamBuildr fits sports teams and training staff that track athlete performance and need structured data capture across sessions. It supports a configurable data model for workouts, metrics, and progress views that can be tailored to sport and team workflows.
Automation and integration depth matter here because the system exposes a way to connect training logs to surrounding tools and reporting pipelines through its API and extensions. Governance controls center on role-based access, team membership, and controlled configuration changes so athlete data stays scoped and auditable.
- +Configurable workout and metric schema for sport-specific performance tracking
- +API and automation surface support building reporting and training workflows
- +Role-based access helps scope athlete and staff visibility by team
- +Structured sessions enable consistent progress comparisons over time
- +Extensibility supports mapping external data into the performance model
- +Audit-friendly activity tracking supports traceability for staff changes
- –Schema configuration requires upfront planning to avoid rework
- –API-based integrations may need custom mapping for edge-case metrics
- –Automation logic can feel limited without deeper custom workflows
- –Admin configuration granularity may not match highly complex organizations
- –Throughput for large batch imports can be slower than manual entry
Best for: Fits when sports teams need consistent performance tracking with API-backed automation and controlled access by roles.
Exercises.com
training logsWorkout planning and exercise tracking toolset for coaches and athletes with progression logs and structured training records.
Configurable workout and athlete data model that drives API and automation mappings across sessions and programs.
Exercises.com focuses on sports performance tracking tied to a configurable data model for athletes, programs, and sessions. It supports integration with training workflows through an automation and API surface that can map external systems into the Exercises.com schema.
Admin controls cover user and role governance so organizations can control access to athletes, metrics, and reporting views. Automation features can reduce manual entry by syncing structured workout data and status changes across connected tools.
- +Configurable athlete and program schema supports consistent metric tracking
- +API surface supports schema mapping for external training systems
- +Automation reduces manual session entry via structured sync workflows
- +RBAC-style governance controls access to athletes, programs, and analytics
- +Extensibility through integrations supports adding custom data pipelines
- –Schema customization requires careful planning to avoid broken mappings
- –Automation rules need clear ownership when multiple integrations write data
- –Audit trail visibility can require admin setup to remain actionable
Best for: Fits when sports organizations need deep integration plus governed access to performance data across systems.
TrainingPeaks
endurance analyticsEndurance athlete training and performance tracking with structured workout logs, analytics views, and coach athlete workflows.
TrainingPeaks API for workout and athlete data enables automation of imports, synchronization, and coaching workflow updates.
In sports performance tracking, TrainingPeaks pairs structured training data with coaching workflows and device ingestion. Its data model centers on athlete workouts, plans, and performance metrics, with multiple configuration points for how files map into training history.
Integration depth comes from device and platform connections plus an API surface designed for automation and data synchronization. Admin governance is handled through account-level controls for managing athletes, coaches, and visibility boundaries.
- +Workout and plan data model supports detailed training history
- +Coaching workflows map structured sessions to athlete feedback cycles
- +API and integrations support automation for imports and sync
- +RBAC-style access separation supports athlete and coach visibility controls
- +Extensibility via integrations reduces manual re-entry of activity data
- –Automation depth depends on available endpoints and data mapping rules
- –Data schema variations between sources can require normalization
- –Bulk admin changes are limited compared with full provisioning systems
- –Audit and governance detail can be harder to trace per integration job
- –Throughput for large historical imports can slow batch operations
Best for: Fits when teams need structured training history, coaching workflows, and API-driven automation for multi-source athlete data.
Final Surge
training planningTraining plan creation and athlete training log platform with performance metrics, coaching workflows, and data-driven progression tracking.
Athlete-session schema with plan-to-execution tracking plus API access for workout and performance data synchronization.
Final Surge stores sports performance data in a structured schema tied to training plans, athletes, and sessions. It supports coach workflows for plan creation, session tracking, and analytics across power, pace, and endurance signals.
Integration depth centers on exporting and synchronizing workout and performance records to reduce manual data entry. Automation and extensibility are driven through configuration of templates and data mappings plus an API surface for programmatic reads and writes.
- +Workout tracking centered on a clear athlete-session data model
- +Configurable training plan templates reduce repetitive setup work
- +Exports support data portability for performance review workflows
- +API enables programmatic access for integration and automation
- +Coach workflows keep plan execution and session notes in one place
- –API capabilities can require extra engineering for complex automation
- –Role separation needs careful configuration for larger staff structures
- –Data synchronization can require manual mapping for edge-case metrics
- –Advanced governance features like audit detail are not consistently granular
Best for: Fits when mid-size coaching staffs need controlled athlete performance tracking with automation through API and exports.
Strava
activity analyticsActivity tracking and performance insights for athletes with workout logging, structured activity data, and organization features for sports teams.
Segments and leaderboards with route-aware analytics built from GPS activity data.
Strava fits sports organizations that need high-friction athlete activity tracking plus social performance signals. Activity uploads, GPS routes, segments, and training summaries translate raw movement data into a consistent data model across runs and rides.
Automation relies on predictable event flows like uploads and segment updates, and extensibility hinges on a public API surface for integrations and data access. Governance is mostly handled through user accounts and application access patterns rather than org-level RBAC and provisioning controls.
- +Segments and routes turn raw GPS into structured performance entities
- +API enables activity ingestion, metadata access, and integration building
- +Training analytics like fitness and freshness summarize trends from histories
- +Privacy controls for visibility and sharing reduce data exposure risk
- –Org-wide admin, RBAC, and provisioning controls are limited
- –Automation hooks are less granular than workflow engines
- –Audit-log depth for enterprise governance is not comparable to admin suites
- –Data model standardization across custom schemas is constrained
Best for: Fits when clubs or teams want structured activity and segment data with API-driven integrations, not strict enterprise governance.
How to Choose the Right Sports Performance Tracking Software
This buyer's guide covers sports performance tracking workflows using Hudl for video-centric coaching review, Stats Perform for governed event data pipelines, and Kinexon for real-time athlete telemetry integration.
It also compares Catapult, S&C Platform, TeamBuildr, Exercises.com, TrainingPeaks, Final Surge, and Strava across integration depth, automation and API surface, and admin governance controls.
The goal is to help match an organization’s data model needs and governance requirements to the right tool.
Tools that turn match, training, sensor, and video evidence into governed athlete performance records
Sports performance tracking software stores athlete, team, session, and performance event data, then connects that data to coaching workflows, analytics, and downstream systems. These tools solve fragmentation problems by enforcing a consistent data model for sessions and athletes, then mapping incoming telemetry, workouts, and event metadata into that model.
Hudl represents one common pattern with structured video tagging tied to team and player context, while Stats Perform represents another with an event-centric model aligned to matches and competitions and an API surface for ingestion and automation.
Organizations typically use these platforms to standardize performance records across seasons and automate data flow into reporting and analysis tools.
Evaluation criteria for integration, automation, and governance in sports performance tracking
Integration depth determines whether a tool can fit into existing sports ops systems without manual reshaping of athlete and event records. Tools like Stats Perform and Kinexon emphasize documented APIs and controlled ingestion paths for connecting feeds into structured schemas.
Automation and API surface define whether workflows can run as repeatable jobs for provisioning, exporting, and syncing performance data. Admin and governance controls decide whether multi-team or multi-coach environments can restrict access, track changes, and keep dataset updates auditable.
Documented API surface for event and workout ingestion
Stats Perform provides a documented API surface for ingestion, retrieval, and automation so event and athlete records can flow into downstream systems. TrainingPeaks and Final Surge also provide API access for workout and athlete data synchronization, which supports programmatic automation beyond manual exports.
Data model schema design for matches, telemetry, and sessions
Stats Perform uses an event-centric data model aligned to matches, players, and competitions, which supports consistent analytics and workflow timing. Kinexon ties athlete, team, session, and telemetry into structured schemas, while Catapult provides a sensor performance data model designed for automated session and event ingestion.
RBAC and audit log coverage for dataset and workflow changes
Stats Perform pairs RBAC with audit log support for dataset and workflow changes, which is critical when multiple teams manage shared performance datasets. Kinexon and Catapult include role-based governance patterns and governance reporting, while Strava mainly relies on user account and app access patterns instead of org-level RBAC and audit depth.
Provisioning and workflow automation for repeatable operations
Kinexon supports automation that helps manage device and workflow provisioning, which reduces manual work when telemetry setups change. S&C Platform and Exercises.com focus on program assignment and workout workflows backed by API-first provisioning so training cycles can be synchronized across tools.
Extensibility through schema configuration and controlled mappings
Stats Perform emphasizes configurable schemas and controlled data ingestion, which enables extensible event attributes when taxonomy alignment is planned. TeamBuildr and S&C Platform also use configurable workout or training program data models, but schema configuration planning can be required to avoid rework.
Integration breadth across video, sensor, GPS, and structured coaching artifacts
Hudl centers sports performance tracking on video workflow and structured tagging tied to team and player context, which reduces the gap between footage and coaching feedback. Strava provides segments, routes, and route-aware analytics built from GPS activity data, while Catapult connects sensor outputs and video-related workflows into consistent performance events through integration paths.
Decision framework for selecting a sports performance tracking tool with the right control depth
Start by mapping integration targets to the tool’s data model so athlete, session, and event identifiers remain consistent across systems. Stats Perform is a strong fit when match-based event pipelines need governed API ingestion, while Kinexon is a better match when telemetry streams must map into structured schemas for automation.
Then evaluate automation as an operational surface by checking what can run as repeatable jobs for ingestion, export, provisioning, and sync. Finally, confirm governance fit by checking RBAC and audit log depth for dataset and workflow changes, which is where Stats Perform is particularly explicit.
Match your primary performance signal to the tool’s data model
If performance tracking is driven by video review workflows, Hudl’s structured video tagging tied to team and player context supports repeatable coaching review. If performance tracking is driven by match events, Stats Perform’s event-centric model aligned to matches, players, and competitions fits the data shape of match timelines.
Verify automation and API endpoints for your ingest and sync jobs
Choose Stats Perform when ingestion and downstream automation need a documented API surface for event data access. Choose TrainingPeaks or Final Surge when automation centers on workout and athlete data imports, synchronization, and coaching workflow updates through API access.
Plan schema ownership before enabling extensibility
Select Stats Perform or Exercises.com when custom event or workout attributes require configurable schemas and controlled mappings. Build upfront taxonomy alignment when schema customization is part of the plan, because custom schema mapping can increase time to operationalize in Stats Perform and Exercises.com-style schema customization.
Confirm governance controls for multi-team and staff workflows
Require Stats Perform when RBAC and audit log coverage for dataset and workflow changes must be traceable across teams. Choose Kinexon or Catapult when role-based access and audit-ready operational governance are needed alongside telemetry or sensor ingestion.
Test operational fit for your throughput and workflow patterns
For high-volume telemetry, evaluate Kinexon throughput tuning needs for dense multi-athlete sessions before committing. For sensor-heavy fixtures, check Catapult capacity planning needs to avoid latency during busy periods when ingest volume is high.
Which organizations get the most value from sports performance tracking software
Sports performance tracking tools split into distinct operational needs driven by signal type, workflow workflow ownership, and governance scope. Choosing the wrong operational model often shows up as schema rework, manual mapping, or limited admin controls for multi-team environments.
The best-fit tools below reflect the stated best_for targets for each product.
Teams that track performance through video-tagged coaching review
Hudl fits when athlete performance tracking depends on structured video tagging tied to team and player context with consistent coaching review patterns. The platform’s role-based access supports multi-team administrative separation without schema-first ingestion requirements.
Analytics and operations teams building governed event pipelines across competitions
Stats Perform fits when event and athlete performance tracking must run across match timelines with documented API ingestion and retrieval. RBAC plus audit log support for dataset and workflow changes supports controlled governance across teams.
Sports teams deploying real-time telemetry with strict role governance
Kinexon fits when automated tracking workflows require API-based event ingestion and strict role-based governance. Its configurable event workflows map telemetry streams into structured schemas so downstream automation stays consistent.
Strength and conditioning staff standardizing programs and progress across training cycles
S&C Platform fits when controlled program assignment and athlete progress tracking must be synchronized across systems using an API-backed automation surface. TeamBuildr also fits when teams need consistent workout and metric tracking with API-backed ingestion and role-based scoping.
Clubs that want structured activity and segment analytics with lighter org governance
Strava fits when activity uploads and GPS-derived segments drive route-aware analytics rather than enterprise RBAC and provisioning. Its public API supports integration building for activity ingestion and metadata access with governance handled mainly through user account and app access patterns.
Common failure modes when implementing sports performance tracking software
Many implementations fail when schema extensibility is enabled without a clear taxonomy and ownership model for custom metrics or event attributes. Other failures happen when organizations expect workflow engines to behave like high-throughput telemetry systems or expect enterprise governance from tools designed around user-level access.
The pitfalls below map to concrete constraints found across Hudl, Stats Perform, Kinexon, Catapult, and Strava.
Assuming workflow configuration can replace schema-first ingestion
Hudl can centralize coaching review through built-in workflows and structured video tagging, but automation depends on those built-in patterns rather than custom event pipelines. If the requirement is schema-first ingestion into a custom event model, Stats Perform or Kinexon is the safer match because both emphasize configurable schemas and API-driven ingestion.
Underestimating upfront taxonomy alignment for custom schema mapping
Stats Perform custom schema mapping can require upfront taxonomy alignment so that event attributes map consistently across competitions and downstream analytics. Exercises.com and TeamBuildr also require planning for schema configuration so sport-specific metrics do not end up with broken mappings.
Expecting enterprise RBAC and audit depth when governance is limited
Strava provides privacy controls and structured activity analytics, but org-wide admin, RBAC, provisioning controls, and audit-log depth are limited compared with admin suites. For multi-team governance and traceability on dataset and workflow changes, Stats Perform is designed with RBAC and audit log support.
Overloading throughput during dense telemetry sessions without capacity planning
Kinexon can require throughput tuning for dense multi-athlete sessions, which can affect operational stability during peak fixtures. Catapult also needs capacity planning for high-throughput ingest to avoid latency when busy fixtures generate large data volumes.
How We Selected and Ranked These Tools
We evaluated Hudl, Stats Perform, Kinexon, Catapult, S&C Platform, TeamBuildr, Exercises.com, TrainingPeaks, Final Surge, and Strava across features, ease of use, and value because sports performance tracking decisions depend on how data moves, how much control teams get, and how quickly workflows become operational. Each overall rating is a weighted average in which features carry the most weight while ease of use and value each matter a lot for real-world rollout timelines. This editorial research did not include hands-on lab testing or private benchmark experiments, so scores reflect the product capabilities described in the provided review content.
Hudl separated itself for teams focused on coach-facing evidence because it pairs structured video tagging tied to team and player context with role-based access and repeatable coaching review workflows, which lifted its features score and kept ease of use high for video-centric operations.
Frequently Asked Questions About Sports Performance Tracking Software
How do Hudl and Stats Perform differ in the way performance data ties to analysis workflows?
Which platforms are more suitable when sensor or device telemetry must map into a controlled data model?
How do Kinexon and TeamBuildr handle role-based access and configuration governance for staff workflows?
What integration and API patterns matter most when teams need automation across multiple downstream tools?
How does Strava’s model of activity and segments compare to enterprise event tracking systems?
Which tools support extensibility when custom event processing or external synchronization is required?
What is the practical difference between export-and-sync workflows and full API-driven reads and writes?
Which platform is a better fit for strength and conditioning program tracking rather than match-centric event timelines?
What are common data migration pitfalls when moving existing workout or performance records into these systems?
Conclusion
After evaluating 10 wellness fitness, Hudl stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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