TL;DR
- We tracked 55 UK buyer questions covering boxing cameras, automated streaming, coaching analysis, scoring and promoter operations.
- The category comparison draws on 1,467 answers collected from ChatGPT, Gemini and Google AI Overview on 19 August 2026.
- JABBR led the specialist set at 17.3% visibility, followed by CompuBox at 13.8% and Pixellot at 11.4%.
- JABBR was much stronger in Gemini and Google AI Overview than in ChatGPT.
- YouTube, research papers and Reddit were central to the evidence layer.
Boxing video used to split into two separate jobs. A production team recorded the fight, then an analyst watched it back and counted what happened. Computer vision is starting to join those jobs.
One fixed camera can now promise automated filming, live output, clips, punch statistics and a searchable record for coaches. The most ambitious systems add round scoring or judging support. The buyer is no longer choosing only a camera. They are choosing which parts of production and analysis one system can be trusted to run.
This report measures how answer engines handle that choice. We tracked 55 questions across five practical jobs and reviewed 1,467 category answers collected in the United Kingdom. Five direct brand-check questions were kept outside the category ranking, so the comparison uses the 50 non-branded buyer questions.
Which boxing video and analytics brands lead?
JABBR led the tracked specialist set with 17.3% visibility and 22.6% share of voice. CompuBox followed at 13.8% visibility and 21.0% share of voice. Pixellot reached 11.4%, Veo 9.0%, Spiideo 7.3% and Hudl 6.1%.
The ranking needs context. CompuBox has decades of association with punch statistics. Pixellot, Veo, Spiideo and Hudl have broader sports-camera footprints. JABBR is more tightly tied to combat sports and joins video with automated statistics.
That focus helps when the question is about coaching or scoring. It helps less when a buyer asks for venue coverage, pay-per-view workflows or a camera comparison that spans many sports.
How does performance change by engine?
JABBR reached 21.6% visibility in Gemini and 20.4% in Google AI Overview. ChatGPT was much lower at 10.2%.
That gap matters because the three systems use different mixes of product pages, editorial sources, videos, forum discussions and research. A brand can have strong evidence and still miss one engine when that evidence is not repeated across the source types that engine retrieves.
The answer is not more generic content. It is a clearer set of pages for each buying job, backed by independent proof in the places ChatGPT already uses.
Which buying jobs does each brand own?
JABBR was strongest on fight scoring and judging technology, at 32.2% visibility, and boxing performance analytics and coaching, at 29.6%.
Its weaker areas were AI cameras for combat sports, automated fight streaming and venue or promotion operations. Automated streaming was the largest gap at 3.0%.
That pattern is commercially useful. A coach may already find JABBR when asking how to count punches or review sparring. A gym owner or promoter can ask for the best camera, a low-staff stream or a pay-per-view workflow and get a different shortlist.
The next category leader will connect those jobs without blurring them. Camera specifications, streaming rights, production reliability, coaching analysis and scoring methodology each need their own evidence.
What should buyers compare?
Start with the operating job, then compare the technology.
For filming and streaming, check:
- how many cameras the venue needs
- whether tracking works in a ring with ropes, officials and changing light
- supported stream destinations, graphics and pay-per-view workflows
- upload requirements and what happens when the connection drops
- how quickly clips and highlights become available
For coaching and analytics, check:
- which punches and defensive actions the system recognises
- how accuracy changes by camera angle and athlete level
- whether coaches can correct the data
- how sparring consent, retention and access work
- whether the video and statistics can be exported
For scoring and judging support, ask for the published methodology, the validation set, disagreement rates and the limits of the model. A transparent aid is different from an automated replacement for officials.
Which sources shape the answers?
YouTube was the most-cited domain in the measured set, with 584 citations in the final pull. Research hosted by the US National Institutes of Health received 453. Reddit received 396.
JABBR's own domain also performed well. The DeepStrike page was the most-cited JABBR URL, ahead of the homepage. That is a useful signal: focused product evidence travels further than a broad company claim.
Independent research and boxing media also mattered. Pages from Dartfish, BoxingScene, university repositories and sports-analysis specialists appeared alongside product sites.
For a vendor, the source plan should mirror that mix. Publish the method on the company site, show the system working on video, make the data available for scrutiny, and earn independent coverage that explains where it works and where it does not.
The 2026 opportunity
The specialist market is still open. No tracked brand appeared in even one quarter of the measured answers. Different companies lead production, cameras, coaching analysis and punch statistics.
JABBR currently has the clearest specialist position across the full set. CompuBox still carries strong statistical authority. Pixellot, Veo, Spiideo and Hudl benefit from broader sports-video buying language.
The next move is not to claim one platform does everything. It is to prove each part of the workflow separately, then show how the parts connect.
Sources
- JABBR DeepStrike
- Boxing punch detection with a single static camera
- Dartfish combat-sports video analysis
- Automatic boxing punch recognition using upper-limb kinematics
- Boxing performance analysis and camera systems
If you want to measure the buyer questions in your category, book a 20-minute call with Marco: https://calendly.com/marco-ai-heroes/20min.
