Best Esports Organizations: G2 Leads at 53.5% of AI Answers

AI Search Visibility for Best Esports Organization to Follow: 2026 Brand Study

G2 Esports led the measured AI visibility results for "best esports organization to follow" prompts. G2 Esports appeared in 53.5% of answers returned, with a 95% interval of 45.3% to 61.4%. The result matters because a brand omitted from generated recommendations has less opportunity to enter a fan's or sponsor's shortlist.

Fnatic Ltd. ranked second at 46.5%, and Team Liquid ranked third at 45.1%. Their intervals overlap with G2 Esports and with each other, so this sample does not establish that one is more visible than the others. It does show that these three brands formed the leading group in the measured prompts.

On this page

1. Which esports organizations lead the leaderboard?
2. How do the leading brands appear in answers?
3. Where do the engines disagree?
4. Which brands lead each demand cluster?
5. Where do cited answers get their evidence?
6. What does this mean for esports brands?
7. How can an esports organization improve its AI visibility?
8. How was this AI visibility measured?
9. Frequently asked questions

Which esports organizations lead the leaderboard?

G2 Esports ranked first among the brands measured, followed by Fnatic Ltd., Team Liquid, Team Vitality, T1, Natus Vincere, Gen.G, FaZe Clan, Cloud9, and Thieves. Appearance rates use 144 answers returned.

The ranking records how often each brand was named, not a judgment about competitive performance, sponsorship value, or fan preference. The measured prompts covered Counter-Strike, League of Legends, Valorant, sponsorship, and organization headquarters in Europe and Asia.

Appearance rate is the share of the 144 returned AI answers in which each brand was named.

The leading intervals overlap: G2 Esports spans 45.3% to 61.4%, Fnatic Ltd. spans 38.6% to 54.7%, and Team Liquid spans 37.2% to 53.3%. The difference between these brands is within the margin of this sample. Team Vitality's 28.7% to 44.2% interval also overlaps Team Liquid's interval.

Where do the engines disagree?

The engines disagree materially on which organizations to name. Perplexity (sonar-pro) returned 37 answers, ChatGPT (OpenAI) returned 35, Gemini (Google) returned 36, and Claude (Anthropic) returned 36. Each engine uses a different model and retrieval tool, so a gap can reflect system design rather than a brand's underlying visibility.

Appearance rate by engine

Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.

Perplexity (sonar-pro) named several organizations much more often than Claude (Anthropic). Fnatic Ltd. appeared in 25 of Perplexity's 37 answers and 2 of Claude's 36. Natus Vincere appeared in 21 of 37 Perplexity answers and 2 of 36 Claude answers. Virtus.pro appeared in 10 of 37 Perplexity answers and in none of ChatGPT's 35, Gemini's 36, or Claude's 36 answers.

ChatGPT (OpenAI) was the strongest engine for Cloud9, naming it in 16 of 35 answers, compared with 11 of 37 for Perplexity (sonar-pro), 6 of 36 for Gemini (Google), and 1 of 36 for Claude (Anthropic). It also named Evil Geniuses in 8 of 35 answers, compared with 1 of 37 on Perplexity (sonar-pro), 2 of 36 on Gemini (Google), and none of Claude's 36 answers.

Claude (Anthropic) named fewer of many leading brands, but it named Team Vitality in 8 of 36 answers and Team Falcons in 4 of 36 answers. Team Falcons did not appear in any of ChatGPT's 35 answers. Sentinels, EDward Gaming, Astralis, and several lower-ranked brands also had no Claude appearances. These are engine-specific patterns, not category-wide verdicts.

Which brands lead each demand cluster?

The demand-cluster view identifies which brands led the measured question types, including Counter-Strike, League of Legends, Valorant, brand sponsorship, and Europe and Asia headquarters. It separates broad appearance from the contexts where an organization is most often selected.

Leaders by demand cluster

The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count.

Cluster leadership is useful for planning category pages and supporting material. A brand can have lower overall appearance yet lead a narrower question type. These findings apply only to the prompts measured on August 31, 2026.

Where do cited answers get their evidence?

Among citation-eligible brands, G2 Esports and Cloud9 had the highest owned-domain citation rate at 7.8% of 141 answers with retrieval evidence. Team Liquid followed at 6.4%, and Fnatic Ltd. had 5%. Appearance and citation rates use different denominators and should not be compared as if they measure the same event.

Share of the 141 answers with retrieval evidence that cited a URL on the brand's own website.

Owned-site citations were limited across the measured results. Several eligible brands appeared in answers but had no owned-domain citations, including Gen.G, Paper Rex, Team Spirit, MOUZ, Karmine Corp, Team Falcons, Ninjas in Pyjamas, LOUD, OG, Rex Regum Qeon, and Virtus.pro.

The engines leaned most often on esports data and reference publishers. escharts.com appeared in 69 of 141 answers with retrieval evidence, a 48.9% source penetration rate. en.wikipedia.org appeared in 53 answers, or 37.6%; vlr.gg appeared in 47, or 33.3%; and liquipedia.net appeared in 45, or 31.9%.

Most-cited publishers

Publishers cited as evidence, counted once per answer rather than once per link.

The source mix suggests that generated recommendations draw heavily on external databases, reference pages, community discussion, and esports publications. A brand's own site can support its factual footprint, but the measured answers often cited third-party sources.

What does this mean for esports brands?

Interpretation: the leading group has broad name recognition across the measured engines, but no organization has a uniform advantage everywhere. G2 Esports, Fnatic Ltd., and Team Liquid had strong results on Perplexity (sonar-pro) and ChatGPT (OpenAI), while Claude (Anthropic) named each far less often.

Interpretation: brands can use the cluster and engine views to identify the recommendation contexts where they are present or absent. The owned-citation results point to a separate task: publish clear first-party material that can support factual claims about teams, games, sponsorship work, and geography, while monitoring the external sources that appeared in retrieved answers.

Interpretation: do not treat the overall ranking as a single selection score. The sampled questions ask different things, and the engines returned different answer volumes and brand patterns.

How can an esports organization improve its AI visibility?

An esports organization improves its AI visibility by keeping the third-party databases that engines cite accurate and by publishing first-party facts that engines can quote. These ten steps follow from the measured sources, engines and demand clusters above.

1. Fix the sources engines cite most. escharts.com appeared in 48.9% of answers with retrieval evidence, Wikipedia in 37.6%, vlr.gg in 33.3% and Liquipedia in 31.9%. Keep your team's roster, titles, headquarters, founding year and ownership accurate on each, using sourced edits and the sites' own conflict-of-interest rules.
2. Earn coverage where engines already look. Reddit appeared in 27% of cited answers, Esports Insider in 22.7% and HLTV in 14.9%. Pitch results, roster news and sponsor announcements to those outlets and answer community threads honestly under your own name.
3. Publish a first-party facts page for each roster covering the games played, current players, headquarters city, founding year, owner, sponsors and official merchandise store. Owned domains were cited in only 0% to 7.8% of answers, so a page that states plain facts gives engines something to quote.
4. Give each game its own page. The leader differed by game in this sample: Team Vitality led the Counter-Strike cluster, T1 the League of Legends cluster and Paper Rex the Valorant cluster. Each cluster had only 16 answers, so treat the gaps as signals to test, not settled rankings.
5. Publish a sponsorship page with audience facts, past activations and a contact. The sponsorship cluster was led at only 37.5% by FaZe Clan and Thieves, which suggests the answer is still open. That cluster also had only 16 answers.
6. Publish an official-store page that states which store is official, what ships where and how returns work. Where-to-buy answers named G2 Esports in all 4 answers in that cluster, a very small sample, so verify it with your own prompts.
7. Track each engine separately. Perplexity named G2 Esports in 75.7% of answers and Claude in 19.4%, and Fnatic Ltd. appeared in 25 of 37 Perplexity answers and 2 of 36 Claude answers. A single blended number hides which engines you are missing.
8. Make your official domain unambiguous. Three of the 25 ranked brands, Thieves, EDward Gaming and Bilibili Gaming, had no official domain established before scoring. Register Wikidata and add sameAs links from your site to your official social and database profiles.
9. Use one brand name everywhere. Product lines and variants such as Fnatic and Fnatic Ltd. can split mentions, so keep the same legal and public name across your site, store, social profiles and press releases.
10. Re-measure on a schedule with repeated runs. The top three brands' intervals overlap, and a single answer can vary between runs, so compare monthly averages and not one-off results.

For help running a category audit, see 5W's GEO optimization practice and the Gaming and Video Game PR practice.

How was this AI visibility measured?

This flagship study measured 24 stratified prompts three times per engine on August 31, 2026. It covered Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). Appearance rates use 144 answers returned. Citation and source rates use 141 answers with retrieval evidence. Of 288 attempted answers, 144 were returned and 144 failed.

How this study was measured

• Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked.
• Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
• 144 answers returned of 288 attempted, 141 with retrieval evidence.
• Product lines rolled up to parent brands using a registry fixed before scoring.
• Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.

Results describe these prompts on this date. They do not measure all esports searches, fan demand, competitive results, or sponsorship outcomes. Confidence intervals describe variation in this sample, and overlap means the study does not support a claim that one overlapping brand is more visible than another.

Methodology appendix

This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.

What was measured

• Category: best esports organization to follow
• Measurement date: 2026-08-31
• Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
• Answers attempted: 288
• Answers returned (appearance denominator): 144
• Answers with retrieval evidence (citation denominator): 141 (49% of attempted)
• Answers that failed outright: 144
• Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27

Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable.

How the prompts were sampled

Fixed sampling frame: client-named subcategories are allocated a guaranteed cell each, then a documented set of generic commercial demand clusters (general best, primary use case, specific need, quality tier, price/value, beginner, comparison, durability, specialist, emerging, retail channel, trust) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure.

Engines and systems measured

Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.

How each metric is defined

• Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
• Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
• 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
• Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
• Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
• Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
• 0% — the metric could be evaluated and the event did not occur.
• Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
• Cross-engine consistency — the share of engines that named the brand at least once.
• Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.

Evidence rules

• An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
• Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
• Publisher identity could not be established for 1 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
• Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.

Entity handling

Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.

No roll-ups were applied in this study.

Organizations held for human classification before the next study: FURIA, The MongolZ, Anyone's Legend, Hanwha Life Esports, FlyQuest, Complexity Gaming and ENCE. Non-organization strings that the extraction step had captured, such as a player name, ranking-page titles and template placeholders, were removed from this list in the October 27, 2026 update.

Limits of these findings

• This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-08-31. Engine behaviour changes without notice.
• The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
• Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
• A brand not listed here was not measured as absent — it simply was not named in these answers.
• Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
• 3 of 25 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero.