Sports analytics annotation

Sports Analytics Data Annotation for Tracking, Pose and Events

Player and ball tracks, skeletons and match events, labelled frame by frame from broadcast, tactical and high-speed cameras, so analytics models see the game the way coaches do.

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Tools we can work with

Roboflow
SuperAnnotate
Darkhorse
Roboflow
SuperAnnotate
Darkhorse

TL;DR

High-frame-rate video needs rules for fast, small and blurred objects. The ball is labelled where the evidence supports it, not where it ought to be.
Players keep persistent track IDs through tackles, crowds and camera cuts, checked on playback.
Skeletal pose and event spans follow written definitions, so a shot, pass or serve starts and ends on the same cue every time.
Multi-camera projects keep one identity per player across views. Tracks, poses and events are audited on playback.

Tracking

High-frame-rate player and ball tracking

Sports footage is fast. The ball can cross the frame in a few frames, and players cluster, collide and swap positions constantly.

Player tracks and identity

Each player gets one track ID for the clip, often linked to team and jersey number when they are readable. When players overlap in a tackle or a crowd, the guideline says how to keep identities apart and when to mark a track as uncertain. Reviewers watch tracks on playback because identity swaps are almost invisible frame by frame.

Ball tracking

The ball is small, fast and often blurred into a streak. The rule states where the box or point goes on a blurred ball, how to label it when hidden by a player, and when it is out of play. Visibility flags separate a visible ball from an inferred position, so the model doesn't learn guesses as fact.

At high frame rates, keyframe interpolation can be used for smooth ball flight, but contact moments such as hits, kicks and bounces are labelled frame by frame.

For a plain-language overview, read our guide to sports video annotation.

Pose

Skeletal pose estimation labels

Technique analysis, injury research and motion models depend on body keypoints that stay consistent through fast movement.

Skeletons and visibility

We use the skeleton layout your model expects, such as a COCO-style body layout or a custom one with extra points for equipment like a racket or bat. Each keypoint carries a visibility flag. Joints hidden by the body or by another player are placed only when the guideline allows inference.

Fast limbs blur. The guideline defines where a keypoint goes on a blurred wrist or foot, so annotators make the same call every time.

Keypoint and skeleton rules for still frames are explained on our image annotation page.

Events

Event and action boundaries

Match events are where analytics value sits, and where annotators disagree most if the rules are loose.

Start and end cues

Every event type, such as a pass, shot, serve, tackle or rally, has written start and end cues tied to something visible: the first forward motion of the racket, the frame the ball leaves the foot, the bounce that ends a rally. Events can carry attributes such as outcome, player ID and body part.

Overlapping and fast events

A pass can lead straight into a shot, and a rally can contain dozens of hits. The guideline says whether events may overlap, how close together two events can be, and how to label them when the camera cuts in the middle of play.

Interpolation, ID-switch checks and event rules are covered in depth on our video annotation page.

Multiple cameras

Multi-camera consistency

Broadcast, tactical and high-speed cameras each see a different part of the play. Labels from different views have to agree on who is who and when things happened.

Identity and time across views

When clips are synchronised, a player keeps one ID across every camera, and events are aligned to a shared timeline. Consistency checks look for a player present in one view and missing in an overlapping one, or an event timed differently between views.

Broadcast footage brings replays, graphics and cuts. These segments are tagged so they can be excluded or labelled separately rather than mixed into live play.

Quality

Calibration and two-stage QA

Projects start with a calibration pilot on your hardest clips, such as crowded set pieces, fast rallies and poor broadcast angles.

Review on playback

Annotators self-check each clip against the guideline. An independent reviewer then audits tracks, poses and events at playback speed and frame by frame, looking for identity swaps, missing ball frames and events with drifting boundaries. Our team has delivered a 700k-frame tennis annotation project in CVAT.

Inspect Sample Deliverable & QA Rubric

Illustrative sample

# Illustrative sample, not client data
# frame,track_id,x,y,w,h,conf,class,visibility
1201,7,642,318,58,164,1,player,1.0
1201,99,903,240,9,9,1,ball,1.0
1202,7,645,317,58,165,1,player,1.0
1202,99,921,236,12,7,1,ball,0.6
1203,99,,,,,1,ball,0.0

Stage 1 — Annotator self-check

Every object in the label set is tagged
Boxes/segments are tight to visible edges
Class names match the guideline exactly
Occlusion and truncation flags set

Stage 2 — Independent reviewer audit

Independent reviewer re-checks a sampled batch
Errors logged against the rubric by type
Batch returned if it misses the accuracy threshold
Edge cases added back to the guideline

Sports analytics annotation FAQ

How do you label a ball that is blurred or hidden?

The guideline defines where the box or point goes on a motion-blurred ball and how to mark it when a player hides it. Each ball label carries a visibility flag, so inferred positions are kept apart from visible ones.

How do you stop player IDs from swapping in crowded play?

Reviewers check every track on playback, where swaps are easy to see, and the guideline sets rules for tackles, crowds and players leaving and re-entering the frame. Uncertain identities are flagged rather than guessed.

Can you use our own skeleton layout?

Yes. We can use a standard body layout or your custom skeleton, including extra points for equipment. Every keypoint has a visibility flag, and the rules for blurred and hidden joints are agreed in the calibration pilot.

How are events kept consistent across cameras?

When footage is synchronised, events are aligned to one shared timeline and players keep one ID across views. Checks flag players missing from overlapping views and events timed differently between cameras.

Send us your fastest, most crowded clips.

Start with a Free Calibration Pilot