GEO Industry Research Series: What 107 Pro Sports Teams Taught Us About the AI Citation Gap

Across 107 professional sports teams, AI models mentioned team brands in 70% of responses but cited an official team domain only 20% of the time. That 49-point gap is the central finding of this study: teams are present in the answer, but too often absent from the source layer.

We ran GEO reports across the NBA (30 teams), NFL (32 teams), MLB (30 teams), and WNBA (15 teams). Each report tested roughly 20 fan-relevant questions across ChatGPT, Claude, and Perplexity, covering schedules, tickets, youth programs, stadium experience, team history, merchandise, and general team discovery.

Professional sports should have every advantage in AI search. The brands are recognizable. The fan interest is persistent. The content libraries are deep. Yet recognition alone is not translating into owned-domain authority.

The constraint is not whether AI models know the team. It is whether the team's content is accessible, structured, and specific enough to become the source.

Across the teams in this study, the average brand mention rate was 70%. The average citation rate, meaning how often an AI model linked to the team's own domain as its source, was 20%.

That is a 49-point gap, wider than the 36-point gap we found when applying the same methodology to 28 retail brands earlier this year.

The NBA posted the widest gap. Its teams were recognized more consistently than teams in any other league and cited less. The NFL sample had lower mention rates, driven largely by more specific, narrow-intent queries, but it also showed the tightest relationship between mentions and citations. No league closed the gap.

The same directional pattern appears beyond sports and retail. Gradial's broader GEO monitoring uses a shared query set per industry, so those absolute rates are not directly comparable with the dedicated team audits in this study. The direction, however, is consistent: mention rate outpaces citation rate by roughly 2x in financial services and software, about 8.5x in automotive, and nearly 11x in food and beverage.

AI models are consistently more willing to name a brand than to rely on its domain. The strategic question is no longer whether the brand appears. It is whether the brand controls the evidence behind the answer.

Some of the most recognizable franchises in professional sports produced the weakest citation conversion in the study:

• Los Angeles Lakers: 55% mention rate, 4% citation rate
• Chicago Bulls: 76% mention rate, 0% citation rate
• Oklahoma City Thunder: 83% mention rate, 1% citation rate
• Milwaukee Bucks: 92% mention rate, 11% citation rate
• New York Knicks: 80% mention rate, 4% citation rate
• Boston Celtics: 83% mention rate, 15% citation rate
• Golden State Warriors: 84% mention rate, 9% citation rate
• New York Yankees: 93% mention rate, 17% citation rate

These teams have decades of championships, globally recognized athletes, sustained media coverage, and significant marketing investment. AI models know them well. The Lakers report, for example, found that models could discuss "Showtime" and "Mamba Mentality" fluently because that history is embedded in the training data.

When those models needed a source, they often linked to ESPN, Wikipedia, or a league-level statistics page instead of the team's domain.

Brand equity can secure the mention. It does not automatically secure the citation. Citation authority depends on a different operating discipline: publishing content that models can access, understand, extract, and attribute.

A smaller group of teams converted recognition into owned-domain citations far more effectively:

• Baltimore Orioles: 91% mention rate, 81% citation rate, a 10-point gap and the tightest in the study
• Minnesota Vikings: 81% mention rate, 64% citation rate
• Las Vegas Raiders: 86% mention rate, 61% citation rate
• Phoenix Mercury: 88% mention rate, 55% citation rate
• Baltimore Ravens: 77% mention rate, 52% citation rate
• Charlotte Hornets: 90% mention rate, 51% citation rate

None is the largest brand in its league. All were cited at two to twenty times the rate of teams with larger followings. Across the full dataset, mention rate and citation rate had only a weak positive relationship, with a correlation of about 0.4.

Fame helps a team enter the answer. It is not the deciding factor in whether the answer links back to the team. The stronger performers show that teams can create source authority through the way official content is organized and delivered, regardless of franchise scale.

Rival franchises appeared as citation competitors in 22 of the 107 reports (21%). The rival was often not a division opponent, but a same-market team in another sport. The Miami Heat lost citation ground to the Dolphins and Marlins. The Las Vegas Aces lost community-partnership recognition to the Raiders.

Team-to-team competition was still only the fourth-most-common pattern. The more consistent citation winners were intermediaries built around structured answers:

• Resale and ticketing platforms (Ticketmaster, SeatGeek, StubHub, TickPick, SuiteHop): flagged in 47 of 107 reports (44%)
• League-level hub pages (nba.com, nfl.com, mlb.com, and wnba.com generic content rather than the team's subdomain): flagged in 43 reports (40%)
• ESPN: flagged in 26 reports (24%)
• Merchandise retailers (Fanatics, Dick's Sporting Goods, NFLShop): flagged in 17 reports (16%)
• Wikipedia: flagged in 15 reports (14%)

A fan asking how much tickets cost or where to watch a game is often routed to a resale marketplace or national broadcaster instead of the official team site. In the Kansas City Chiefs report, Chiefs.com had a 4% citation rate on discovery queries because AI engines defaulted to NFL.com and third-party parking and ticketing platforms for logistical information.

Those intermediaries are not winning because AI models prefer them as brands. They are winning because they publish direct, structured answers that are easier to retrieve and cite.

Twenty-six of the 107 reports flagged JavaScript-dependent rendering as a high-priority technical issue. Eighteen found that 100% of the team's site content became visible only after JavaScript executed.

Many AI crawlers do not run JavaScript. When the primary content appears only after a client-side script loads, the model may receive an empty page. The content can be accurate, useful, and on brand while remaining functionally unavailable to the systems teams want to influence.

The Lakers report showed the consequence: a 55% mention rate paired with a target-domain citation rate as low as 1%, while 100% of the site's content depended on JavaScript rendering. The Celtics, Bulls, Cavaliers, and Hornets reports flagged the same issue at high priority and recommended server-side rendering or pre-rendering so crawlers receive meaningful content on the first pass.

This is not a copy problem. It is a content infrastructure problem. Teams cannot earn citations from content that the crawler never sees.

The content earning citations was rarely a broad commercial or brand page.

What earned citations: season ticket waitlists, youth camp and clinic pages, community foundation programs, accessibility initiatives, stadium logistics, parking guides, and named fan traditions. Examples included the Celtics' season ticket waitlist, the Tigers' "Play Ball Detroit" program, the Bills' "Give 716" initiative, the Hawks' OneCourt accessibility technology, the Kings' "Light the Beam," and the Vikings' "SKOL" chant.

What did not: general team history, roster pages, and merchandise. Those topics were frequently ceded to Wikipedia, ESPN, StatMuse, Baseball-Reference, and Fanatics, even when the official team content was accurate.

The difference is specificity. AI models cited pages that answered a concrete question with clear details. A ticket price, parking rule, clinic date, or accessibility policy gives the model a discrete answer it can attribute. A broad claim about team history does not.

Fix access before adding volume. If primary content is invisible until JavaScript executes, more content will not improve citation performance. Start with server-side rendering, pre-rendering, canonical signals, and crawlable page structure.

Measure the gap by fan need. A blended mention-to-citation rate can hide the operational opportunity. A team may perform well for stadium reputation and poorly for parking, or rank for family programs while losing merchandise queries. Break the analysis down by tickets, venue logistics, youth programs, accessibility, traditions, and commerce.

Build one authoritative answer at a time. Publish pages that resolve a specific fan question with current details, clear headings, structured data, and an obvious official source. The strongest opportunities in this study were not generic brand pages. They were pages with practical answers.

Treat GEO as a continuous operating motion. Fan queries change during playoff pushes, trades, schedule changes, ticket windows, and major cultural moments. A seasonal audit becomes stale quickly. Monitoring, prioritization, content updates, review, and publishing need to operate as one repeatable workflow.

Connect the finding to execution. Every report in this study identified concrete work: build a landing page, add schema, improve rendering, or clarify a service answer. The advantage comes from getting that work prioritized, governed, and live while the fan need is still current.

The research identifies where authority is leaking. Gradial GEO execution helps teams turn those findings into governed content and technical updates across the systems where the work needs to happen.

This analysis is based on 107 Gradial GEO reports across four professional sports leagues: NBA (30 teams), NFL (32 teams), MLB (30 teams), and WNBA (15 teams). Each report tested roughly 20 fan-relevant queries per team across leading AI search engines, including ChatGPT, Claude, and Perplexity.

Mention rates, citation rates, citation competitors, content patterns, and technical findings are drawn from this report set. Team names are included because the analysis covers public franchise websites, not confidential brand performance data.

This study is part of Gradial's ongoing research into AI search visibility across industries. To understand where your brand appears, which sources earn the citation, and what work should happen next, request a GEO report.