When people talk about sports AI, they usually talk about video. Tracking players. Following the ball. Spotting a goal. That work matters, and we cover it in our guide to sports video annotation.
But a sports broadcast is more than pictures. There is a commentator describing the play, a crowd reacting, and a stream of match statistics running alongside. Broadcast AI has to understand all of it. That means the training data has to cover more than video.
Globik's Industries page puts it clearly. Sports analytics, broadcast AI, and performance intelligence applications all depend on high-quality, event-level annotation across video, audio, and structured match data.
That gives us three layers:
Most teams start with video. The other two layers are easy to overlook, and a model with only one of the three sees only part of the match.
Sports commentary annotation is the labeling of spoken audio in a broadcast. Globik lists commentary and audio annotation for broadcast AI applications as one of its four sports dataset services.
In practice, this kind of work usually includes transcribing what commentators say, marking who is speaking, and tying each remark to the moment it describes. A commentator might call a pass, a foul, or a goal. The annotation connects those words to the event on screen, so a model can learn how the language and the action line up.
This is where sports audio annotation differs from ordinary transcription. The words matter, but so does their timing and what they refer to.
Commentary moves fast. It is full of sport-specific terms, player names, and shorthand that fans understand instantly and outsiders do not.
An annotator who does not know the game may mishear a term, mix up two players, or label a remark as the wrong kind of event. Globik's Sports section makes the same point for video: event classification in sports video requires knowledge of the sport, and general annotation workforces cannot reliably tell event types apart without it.
The same logic applies to audio. Its Sports section also notes that frame-level precision across a 90-minute match requires annotators who understand the game. That is why Globik's sports annotation relies on sport-knowledgeable annotators. Our post on what "domain expert" actually means in data annotation explains how to check that claim when you evaluate a vendor.
The third layer is match data labeling. Globik lists match data structuring and performance metric labeling as another of its sports dataset services.
The idea is to turn what happens in a match into organized, labeled records. Events, players, and performance metrics are captured in a consistent structure, so analytics tools can use them directly.
Consistency is the key word. If one batch of data counts an event one way and another batch counts it differently, any statistics built on top become unreliable. Clear labeling rules, applied the same way every time, keep the data usable.
Picture a goal in a football match.
The video layer shows the ball crossing the line and the player who kicked it. The audio layer holds the commentator naming the scorer and describing the move. The match data layer records the event, the player, and the time.
Each layer describes the same moment in a different form. When all three are labeled well, a broadcast AI system can match them up. It can tell that the words it hears, the action it sees, and the number in the record all refer to one event.
When one layer is weak, the others cannot fully make up for it. A model that sees the goal but misreads the commentary, or a record that lists the wrong scorer, creates errors that spread into whatever is built on top.
A video-only dataset teaches a model to see the game. It can follow players and spot events, but it knows nothing about how the game is described or recorded.
A dataset that covers all three layers teaches a model to see, listen, and keep records. It can connect a spoken call to the play it describes and to the statistics that follow.
The second setup takes more work to build. But broadcast AI products depend on exactly these connections, so the extra layers are what make the data match what the product has to do.
When you compare vendors for broadcast AI data, look for a few things.
Sport knowledge: Ask who labels the data and how well they know the game. "People who watch the game" is a better answer than general experience.
All three layers: Check whether the vendor can handle video, audio, and match data, or only one of them.
Language depth: Broadcasts are commentated in many languages. Globik's Voice and Speech work covers native-speaker transcription across 40+ languages and dialect-aware annotation, which matters if your product serves more than one market.
Frame-level and event-level precision: Match data and commentary both need exact timing, so ask how the vendor handles it.
A model that only watches the match will miss half of what a broadcast contains. If you are building sports analytics or broadcast AI, you can read more about our work in the Sports section of our Industries page, browse our full list of services, or get in touch to talk through your data needs.
Sports commentary annotation is the labeling of the spoken audio in a sports broadcast, such as commentators describing play. Annotators transcribe the speech and tie it to what is happening in the match, so broadcast AI systems can learn how the words, the events, and the action on screen connect.
Video shows what happened, but a broadcast also carries commentary and match statistics. Globik's Industries page notes that sports analytics, broadcast AI, and performance intelligence depend on event-level annotation across video, audio, and structured match data. Labeling only video leaves two of those three layers without training data.
Commentary is full of sport-specific terms, player names, and fast descriptions of play. Annotators who know the game can transcribe terms correctly and match each remark to the right event. Globik's sports work relies on sport-knowledgeable annotators, because general workforces cannot reliably tell event types apart without domain knowledge.
It means turning what happens in a match into organized, labeled records that analytics tools can use. Events, players, and performance metrics are captured in a consistent structure, so statistics and performance intelligence applications work from clean data instead of loose notes. Globik lists this as one of its four sports dataset services.
Globik lists four sports services: sports video annotation including event classification, action recognition, and player tracking; frame-level bounding box and keypoint annotation by sport-knowledgeable annotators; match data structuring and performance metric labeling; and commentary and audio annotation for broadcast AI applications.
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