How to Label Sports Video for AI (Annotation Explained)

Labelling sports video for AI means annotating footage so a model can learn from it — drawing boxes around objects like the ball and players, and marking events like goals and tackles with their timestamps. This annotated footage becomes the training data that teaches a computer-vision model what to look for. It is unglamorous, painstaking work, and it is also the single biggest factor in whether a sports AI system works on real footage or only in a demo.

What annotation involves

Annotation comes in layers that mirror the computer-vision stack. Object annotation marks where things are in each frame. Tracking annotation links those objects across frames. Event or action annotation labels what is happening and when. Each layer is more time-consuming than the last, and each teaches the model a different skill. The stack itself is described in What Is Computer Vision in Sports?.

Why representative data matters most

The golden rule is that training data must look like the footage the model will face in production. A model trained on clean tactical cameras will struggle on a broadcast feed full of replays and overlays. This is why we keep stressing real broadcast footage over idealised data — the point we made in Training AI on Broadcast Footage Is Harder Than You Think. Annotate the messy reality, not a tidy approximation of it.

The scale required

Good models need a lot of labelled data. RISE has built its training set to 731,854 detection annotations and 185,208 images, with 9,065 labelled clips for event recognition, drawn from an archive of 164,609 scouted videos at 92% completion. The numbers are large because rare events need enough examples to learn from, and broadcast variety — different leagues, cameras, weather, kit — has to be represented.

Quality over raw quantity

• Consistency — the same event labelled the same way every time.
• Coverage — enough examples of rare events, not just common ones.
• Accuracy — clean boxes and precise timestamps, since errors teach the model the wrong thing.
• Representativeness — the full range of real conditions the model will meet.

Common labelling mistakes

Most labelling problems are not exotic. The frequent ones are inconsistency between annotators, labelling only the easy and common events while neglecting rare ones, sloppy timestamps that teach the model the wrong moment, and training entirely on clean footage that does not match production. Each of these quietly degrades the model in ways that are hard to diagnose later, because the failure shows up not in the labels but in the model's behaviour on real matches months afterwards. Disciplined, representative labelling prevents far more problems than any clever architecture ever fixes.

Manual, assisted, or automated labelling

Labelling can be fully manual, model-assisted (where a rough model pre-labels and humans correct), or increasingly automated for the easy cases with human review on the hard ones. The trend mirrors the rest of sports AI: automate the predictable, keep humans on the ambiguous, and never let the labelling drift away from the footage that matters.

Garbage in, garbage out

No principle matters more in machine learning than this: a model can only ever be as good as the data it learns from. Sloppy, inconsistent or unrepresentative labels produce a model that fails in exactly those ways, and no amount of clever engineering downstream rescues it. This is why serious teams invest so heavily in the unglamorous labelling stage — it is the foundation everything else stands on, and the cheapest place to ruin a model is at the very start.

Who does the labelling

Labelling is usually a mix of trained annotators and people who understand the sport. Domain knowledge matters more than people expect: someone who understands football labels events more consistently than a generic annotator, because they know what actually counts as a chance or a foul. The best labelling operations pair clear guidelines with reviewers who can resolve the genuinely ambiguous cases, because consistency across thousands of examples is what teaches a model cleanly rather than confusing it.

Labelling is never really finished

A common misconception is that labelling is a one-time job done before training. In practice it continues: as a model is deployed and meets new footage, its mistakes reveal gaps in the training data that need new labels. The best systems treat labelling as an ongoing loop — deploy, find failures, label those cases, retrain — rather than a single upfront effort. This is why a model improves on your footage over time, and why the labelling pipeline matters as much as the model itself.