Computer vision is a measurement pipeline, not a magic camera. Accuracy depends on the sport, camera, calibration, labels and the exact event being measured.
Four building blocks
Detection finds an object in a frame. Tracking links that object across frames. Pose estimation predicts body landmarks. Event recognition combines movement and context to label an action.
A product may use one block or all four. Ask which output is directly observed and which is inferred.
Why sports footage is hard
Players overlap, uniforms look alike, cameras move, lighting changes and the ball may occupy only a few pixels. Broadcast cuts break tracks. Wide tactical cameras preserve geometry but sacrifice visual detail.
Models trained on one league, venue or camera setup may not transfer cleanly to another.
What accuracy means
A single accuracy percentage is rarely enough. Teams should examine missed objects, identity switches, position error and event precision by match condition. The useful metric depends on the coaching or media job.
For clip retrieval, a false positive is inconvenient. For officiating support, a small spatial error can be decisive.
Questions to ask a vendor
Request validation on footage like yours, not only a headline benchmark. Ask who owns uploaded video and derived data, how corrections work, what happens offline and whether results can be exported.
The strongest answer includes limits, not only capabilities.
PRIMARY & AUTHORITATIVE SOURCES
Source notes
Sources are selected for direct relevance. Vendor sources establish what a product or partnership claims; they do not independently prove performance. We state that distinction in the analysis.