Collect. Annotate. Validate. Build Better AI
Sports models fail on fast motion, occluded players, and inconsistent event definitions. We track, tag, and annotate match video through a multi-stage QA process, so your model trains on labels it can trust.
The accuracy of your labels is the accuracy of your AI model, and our process is built around that.
Sport moves fast. Players cross and block each other, the ball is small, and one match can contain thousands of events that must each be tagged at the right moment. A mistaken player ID follows a player through the whole game, and a late event tag shifts every statistic built on it.
Those errors become wrong analytics, unreliable coaching insight, and tracking software that loses trust.
Qualitas Global treats sports annotation as a data operations problem, and our team has worked on sports annotation since 2007. Annotators are trained on your sport rules and tagging classes before production begins, and we scale teams quickly for match volume. Every batch passes internal and external quality checks.
Our semantic segmentation services label every pixel of stadium images, separating the pitch, court, stands, boards, players, and sky. This human-annotated data helps models read the playing area and the venue around it.
Our video annotation services follow each player, the ball, and other match objects across frames with persistent IDs, including jersey numbers. This object tracking annotation gives AI sports analytics models clean movement data, even in crowded play.
We use 2D bounding box annotation to mark every player on the field and assign each one a consistent identity throughout the match. These labels keep players correctly identified through overlaps, substitutions, and camera changes.
Our keypoint annotation places points on shoulders, elbows, hips, knees, and ankles to capture each player’s posture and movement. This annotation for machine learning trains pose models that coaches use to study technique and performance.
We tag match events such as kick-offs, goals, whistles, bounces, and possession changes with exact timestamps, and divide footage into labeled phases. This data annotation covers tennis, badminton, football, American football, basketball, and other sports.
Our trained teams tag live and recorded matches in real time, then review the data to produce match-level records. This machine learning sports analytics data supports tactical analysis, performance reports, and live statistics.
We combine tracking, pose, and event labels for each athlete to build a detailed record of movement, involvement, and technique. This human-annotated data helps models measure individual performance and compare it across matches.
Annotators identify the pivotal moments in a match, including goals, saves, fouls, and crowd reactions, and cut them into short clips. These labeled moments support highlight reels and automated montage models.
We use bounding box annotation to locate sponsor and brand logos on boards, kits, and screens, and follow them across frames. This data annotation service helps models measure brand exposure during live and recorded matches.
Explore our data collection services.
Domain-trained expert annotators and daily throughput tracking against SLA targets.
A rigorous multi-level quality assurance process that ensures a 97%+ quality benchmark, validated on every project.
A risk-free 2-day pilot before full production, and team deployment within 24 business hours of sign-off. You see our work before you commit to volume.
Every engagement is tailored to your goals, with annotation guidelines, QA, and delivery cadence adapted to your project.
Pay-per-verified-productive-hour billing. You are not paying for our learning curve or mistakes, our internal rework, or idle bench time.
ISO/IEC 27001:2022, ISO 9001:2015, TISAX Level 3, HIPAA, and GDPR compliance. Biometric access, 24*7 CCTV monitoring, and policies and procedures that prohibit cameras and unauthorised storage devices on the floor.
100% in-house delivery. No outsourcing, no work-from-home access to client data, no exceptions.
Sports video annotation is the structured labeling of data so a machine-learning or analytics system can learn from examples or be evaluated against defined criteria. For sports analytics, the work begins with a written label taxonomy, clear inclusion and exclusion rules, and representative sample data. The exact scope depends on the model objective and the client's acceptance criteria.
Depending on the approved scope, sports analytics projects may involve players, ball or equipment, field markings, actions, events, player identity, and frame-by-frame or clip-level labels specified by the sport and use case. The team should define each label, the unit being annotated, how to treat uncertain cases, and whether the task is frame-level, object-level, document-level, or text-level. These examples describe possible project requirements, not a guarantee that every modality or use case is currently offered.
Output formats and tooling should be confirmed during discovery rather than assumed from a general industry page. The project brief should specify the required schema, field names, class IDs, metadata, export structure, and any client platform access. Qualitas Global should confirm compatibility with the requested tools before the pilot begins; no specific format or third-party platform is promised here.
A quality plan should define the annotation guideline, reviewer responsibilities, sampling or review coverage, error categories, escalation rules, and acceptance criteria before production. Multi-level quality checks are a stated part of the intended service approach, but the exact workflow and quality SLA need internal confirmation. Disagreements should be documented, corrected, and reflected in updated guidance where the same issue could recur.
Data access, storage, transfer, retention, deletion, and physical work arrangements should be agreed before any sensitive files are shared. The project owner should confirm the applicable security controls and whether any certification or regulatory commitment applies to the specific engagement. Do not assume a particular ISO standard, HIPAA status, GDPR status, or other certification without current internal evidence.
Pricing should be scoped around the task definition, data complexity, annotation guideline, expected review effort, language or domain requirements, and the delivery arrangement. The stated commercial model is based on productive hours, with scope and acceptance criteria agreed in advance. A quote should clarify what counts as productive work, how rework is treated, and which activities are included, without relying on an unverified public price.
Yes, the recommended first step is to discuss a representative sample and agree on a pilot scope before committing a larger dataset. The pilot can test the label definitions, edge-case handling, reviewer workflow, output structure, and acceptance criteria. Start dates, sample size, turnaround, staffing, and scale-up capacity should be confirmed by the project team after reviewing the data and requirements.
Common challenges include Fast motion, player overlap, camera cuts, similar uniforms, ball visibility, and consistent event timing across long sequences. A clear guideline should show positive and negative examples, explain ambiguous cases, and define when annotators should escalate rather than guess. To get started, share the intended model or business use, data types, approximate volume, required outputs, security constraints, and a sample if permitted. Qualitas Global can then confirm fit and propose a pilot.
We audit, correct and validate auto-annotated aerial data against your guidelines before it reaches your training pipeline.
Whether you need precisely annotated training data, real-world data collection, a dedicated in-house team, or other staffing solutions, Qualitas Global delivers all three under one roof. Backed by over a decade of experience, we back every engagement with a risk-free 2-day pilot before you commit.
