How AI Search Is Recommending Online Betting Sites

Buyer discovery in the online betting category is shifting from traditional search and brand marketing to AI-generated shortlists. Bettors are increasingly asking AI systems to recommend the best sportsbooks, compare platforms, and evaluate sign-up offers, creating a new competitive battleground where recommendation power matters more than brand awareness alone.

The LLM Authority Index benchmark for June 2026 reveals a market dominated by two operators, DraftKings and FanDuel, which together capture the overwhelming share of AI recommendation value across all buyer stages. Several well-known brands, including ESPN Bet and Caesars Sportsbook, are functionally invisible to AI systems, while bet365 shows strong visibility but converts very little of that presence into ranked recommendations. CiteWorks Studio interprets this benchmark to help brands understand where AI-led discovery is reshaping competitive dynamics and what the evidence suggests about the current state of recommendation-stage visibility.

Methodology

1. Market studied: Online betting sites, including sportsbook and fantasy sports platforms.
2. Brands/entities included: FanDuel, DraftKings, bet365, Hard Rock Bet, BetMGM, Fanatics Sportsbook, BetRivers, ESPN Bet, Caesars Sportsbook, Bally Bet. This universe may not include all regional or emerging operators.
3. Data collection date/window: June 2026, snapshot-based.
4. AI platforms tested: ChatGPT, Gemini, Copilot, Perplexity, Google AI Mode, Google AI Overviews.
5. Number of prompts tested: Prompt count was not provided. 966 observations were analyzed across three public high-intent buyer-stage clusters.
6. Prompt categories: Consideration prompts (best platforms), evaluation prompts (platform comparisons), and decision prompts (pricing, fees, and offers).
7. Definition of a mention: A mention means the company appeared in an AI-generated response, regardless of sentiment or rank.
8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality recommendation or ranked recommendation that earns recommendation credit. This is the key CiteWorks distinction: visibility is not the same as recommendation credit.
9. Ranking/scoring metrics used: Valid recommendation coverage, top-three rate, rank-one rate, top-ten rate, average rank, net sentiment score, monthly AI Authority Value, monthly AI Recommendation Value, monthly AI Visibility Assist Value, and captured share of AI opportunity.
10. Limitations: This is a point-in-time benchmark. AI outputs can change with model updates, source changes, and content shifts. Modeled values are estimates and not revenue. This report is not a full audit or full market census.

Key Findings

Recommendation power is concentrated in two brands. DraftKings and FanDuel together account for the vast majority of valid recommendations across all three buyer stages. DraftKings holds a monthly AI Authority Value of $1.21 million, and FanDuel follows at $1.03 million. Both brands achieve top-three rates above 7.7% and rank-one rates above 4.5%, far exceeding every other operator in the category.

bet365 has a significant visibility-to-recommendation gap. bet365 ranks among the top three brands by AI Authority Value at $1.05 million, but nearly all of that modeled value comes from visibility assist rather than direct recommendations. Its monthly AI Recommendation Value is approximately $1,100, compared to $410,488 for DraftKings and $228,493 for FanDuel. bet365 is widely mentioned by AI systems, particularly in pricing and fee discussions, but rarely earns a ranked recommendation.

Hard Rock Bet is the most efficient recommender in the category. Hard Rock Bet appears in only 7.6% of observations but earns 21 valid recommendations, every one of which is a rank-one placement. Its average rank of 1.0 is perfect across those recommendations. When AI systems recommend Hard Rock Bet, they place it first, suggesting strong source alignment in specific use cases or regional contexts.

Several major brands are functionally absent from AI shortlists. ESPN Bet appears in only 3 of 966 observations and earns zero recommendations. Caesars Sportsbook appears in 11 observations with a net sentiment score of 0.0, meaning AI systems are as likely to frame it negatively as positively. Bally Bet registers zero presence across all observations. These brands are being bypassed entirely as AI systems construct buyer shortlists.

The modeled monthly AI opportunity value for this category is $76.2 million. This figure represents the estimated value of AI-influenced betting decisions within the benchmark period. Brands that fail to earn valid recommendations are leaving this modeled value to competitors.

What Changed in the Market

Buyers in the online betting category are no longer moving only from Google results to brand websites. They are also asking AI systems to compare sportsbooks, explain reputation, summarize sign-up bonuses, surface alternatives, and recommend shortlists. This shift is particularly consequential for a trust-heavy industry where legitimacy, licensing, user reviews, and promotional offers directly influence choice at the decision stage.

AI platforms are becoming primary shortlist builders for bettors. When a user asks for the best sports betting platform or requests a comparison of welcome offers, AI systems synthesize information from multiple public sources and present a ranked or curated list. Being mentioned in these responses is no longer sufficient. The critical metric is whether a brand receives a positive, ranked recommendation that places it in the top three or at rank one.

Traditional search visibility does not translate automatically into AI recommendation power. A brand can hold strong organic search positions and still fail to appear in AI-generated shortlists. The difference lies in how AI systems evaluate source credibility, citation architecture, and the consistency of positive framing across the public web. These are distinct dynamics from traditional SEO, even though they overlap with it at the source layer.

The commercial stakes are material. With a modeled monthly AI opportunity value of $76.2 million, brands that do not earn AI recommendations are leaving that modeled value to operators who have built stronger public evidence layers. The benchmark evidence suggests that the current market is not competitively balanced at the recommendation stage.

What the Benchmark Found

Raw visibility leaders. DraftKings appears in 35.5% of all observations, and FanDuel appears in 34.3%. These two brands dominate raw mention presence across all six platforms and all three prompt clusters. BetMGM appears in 14.6% of observations, and Hard Rock Bet appears in 7.6%.

Valid recommendation leaders. FanDuel earns 84 valid recommendations, the highest count in the category. DraftKings follows with 80 valid recommendations. Hard Rock Bet earns 21 valid recommendations, all at rank one. BetMGM earns 16, bet365 earns 10, and Fanatics Sportsbook earns 8.

Top-three leaders. FanDuel achieves a top-three rate of 8.0%, and DraftKings achieves 7.8%. Hard Rock Bet achieves 2.2%. No other brand exceeds 1.2%.

Rank-one leaders. DraftKings achieves a rank-one rate of 4.8%, and FanDuel achieves 4.6%. Hard Rock Bet achieves 2.2%, with every recommendation it earns landing at rank one. No other brand exceeds 0.6%.

Value-weighted winners. DraftKings leads with a monthly AI Authority Value of $1.21 million. bet365 holds $1.05 million, but that figure is almost entirely visibility assist, not recommendation value. FanDuel holds $1.03 million, with a much higher proportion coming from direct recommendation credit. Hard Rock Bet holds $580,176 in AI Authority Value, driven by its perfect rank-one placement rate.

Visible but under-recommended. bet365 is the clearest example. It appears in 3.9% of observations but earns only 10 valid recommendations, giving it a recommendation coverage rate of approximately 1.0% relative to its total observations. BetMGM appears in 14.6% of observations but earns only 16 valid recommendations, a recommendation coverage rate of approximately 1.7%. Both brands have meaningful AI presence that does not convert into shortlist placement.

Strong recommendation quality despite lower visibility. Hard Rock Bet demonstrates that a focused evidence layer can support exceptional placement quality. Its perfect rank-one rate indicates that when the right sources align, AI systems will place it first. Fanatics Sportsbook shows a similar pattern with a net sentiment score of 0.54 and an average rank of 1.88 among its recommendations, though its total recommendation count remains low.

Cautionary visibility risk. Caesars Sportsbook has a net sentiment score of 0.0, indicating that AI systems are as likely to frame it negatively as positively. For a brand with Caesars' market recognition, this framing pattern is a material concern. The single recommendation it earns lands at rank five.

Platform-specific patterns. DraftKings and FanDuel lead across all six platforms tested. Hard Rock Bet performs particularly well on ChatGPT and Copilot, where it achieves rank-one rates above 2.6%. bet365's visibility assist value is concentrated on ChatGPT, Copilot, and Google AI Mode, where it appears frequently in pricing and fee discussions but rarely earns ranked placement.

Prompt-cluster-specific patterns. In the consideration cluster (best platforms), FanDuel leads with a rank-one rate of 5.4% and a top-three rate of 8.2%. In the evaluation cluster (comparisons), DraftKings leads with a rank-one rate of 7.3% and a top-three rate of 11.1%. In the decision cluster (pricing and offers), DraftKings leads with a rank-one rate of 3.3% and a top-three rate of 5.2%.

Why Visibility Is Not Enough

A brand can appear in AI answers and still fail to win the buyer shortlist. This is the central distinction the benchmark reveals, and it has direct commercial consequences.

Raw mention presence measures how often a company is named in AI responses. It does not measure whether the company is actually recommended. A brand can be mentioned neutrally, listed as one of many options without endorsement, or framed with caution. None of these appearances count as recommendation credit. bet365 is the clearest illustration: it is widely mentioned across six AI platforms but earns a monthly AI Recommendation Value of approximately $1,100, compared to $410,488 for DraftKings.

Valid recommendation coverage measures how often a company is positively recommended or placed in a ranked shortlist. Top-three placement and rank-one placement are the positions that most influence buyer action. DraftKings and FanDuel dominate these positions. Hard Rock Bet achieves perfect rank-one placement in every recommendation it earns, demonstrating that quality of placement matters as much as quantity.

Neutral or cautionary mentions do not drive buyer action and can actively suppress it. Caesars Sportsbook appears in 11 observations but has a net sentiment score of 0.0, meaning AI systems are as likely to associate it with concern or qualification as with endorsement. This type of visibility does not advance shortlist eligibility.

Modeled benchmark value is not revenue. The AI Authority Values and AI Recommendation Values reported here are modeled estimates of the potential influence of AI-generated recommendations. They are directional indicators of recommendation-stage competitive position, not booked sales, pipeline value, or projected ROI.

Ahrefs visibility, traditional search rankings, and backlink strength are supporting signals for the public evidence layer. They do not prove AI recommendation influence on their own. A brand can rank well in Google and still be invisible in AI-generated shortlists if the source architecture does not support recommendation-grade framing.

The Citation Layer

The public sources that appear to shape AI answers in the online betting category include official brand websites, editorial reviews from sports and gambling publications, comparison articles from affiliate and media sites, user reviews on platforms such as Trustpilot and Reddit, forum discussions, and regulatory or government pages.

DraftKings and FanDuel benefit from extensive media coverage, user reviews, editorial comparisons, and official brand content that AI systems can retrieve, verify, and synthesize. Their public evidence layer is deep, consistent, and positively framed across multiple source types. This breadth of authoritative, retrievable content appears to support their dominance in both raw visibility and valid recommendation coverage.

bet365 demonstrates the risk of visibility without recommendation-grade evidence. It appears widely in AI responses, particularly in pricing and fee discussions, but the evidence available to AI systems may not be structured to support a confident positive recommendation. The visibility is present; the recommendation architecture appears incomplete.

Hard Rock Bet illustrates a different dynamic. Its perfect rank-one placement rate suggests that when the right sources align in specific use cases or regional markets, AI systems will place it first without hesitation. The sources supporting Hard Rock Bet's recommendations may be concentrated and particularly authoritative within those specific contexts, giving AI systems sufficient confidence to lead with it.

ESPN Bet, Caesars Sportsbook, and Bally Bet appear to lack the layered public evidence needed to earn AI recommendations consistently. Despite significant brand recognition and marketing investment, they do not appear to have the citation architecture that AI systems draw on when constructing buyer shortlists.

The traditional search footprint, including organic rankings, keyword visibility, and referring domain strength, contributes to the public evidence layer by creating more retrievable, search-indexed content for AI systems to draw from. However, search visibility is a contributing factor, not a sufficient condition. Brands need citation architecture that includes authoritative, retrievable, and positively framed content across multiple independent source types to earn recommendation-grade positioning in AI-generated responses.

What Brands Need to Fix

Weak valid recommendation coverage. bet365 and BetMGM appear frequently in AI responses but earn recommendations at rates far below their visibility share. These brands need to improve the quality, structure, and authoritativeness of the public evidence that supports shortlist-grade recommendation in AI responses.

Low top-three and rank-one presence. Most brands in the category have near-zero top-three and rank-one rates. Even BetMGM, which appears in 14.6% of all observations, achieves a rank-one rate of only 0.4%. Improving placement quality requires building the source credibility and framing consistency that earns top-position trust from AI systems.

Thin prompt-cluster coverage. Some brands perform better in consideration prompts than in evaluation or decision prompts. bet365 accumulates visibility assist in pricing and fee discussions but earns almost no recommendations in that cluster. Brands need coverage across all three buyer stages: consideration, evaluation, and decision.

Neutral or cautionary framing. Caesars Sportsbook's net sentiment score of 0.0 requires investigation into which sources are driving mixed framing and what changes to the public evidence layer could shift AI systems toward consistent positive framing.

Functionally absent from AI shortlists. ESPN Bet, Bally Bet, and Fanatics Sportsbook have very low mention rates and near-zero recommendation coverage. Building the public evidence layer that AI systems can retrieve and synthesize is the starting point for these brands.

Inconsistent or thin entity information. Brands without structured, consistent, and authoritative information across the web about their licensing, jurisdictions, product offerings, and user satisfaction are less likely to be recommended. AI systems rely on convergent signals across multiple sources to assign recommendation confidence.

Weak third-party validation. Comparison articles, editorial reviews, and user reviews from authoritative sources appear to shape AI recommendation decisions in this category. Brands that lack sufficient third-party validation are less likely to earn shortlist-quality placement.

Underdeveloped owned content. Official brand content structured for AI retrievability, including clear pricing pages, feature comparison content, regulatory and licensing information, and trust signals, can strengthen the evidence layer that AI systems synthesize when constructing responses.

How CiteWorks Studio Helps

1. Map AI recommendation visibility. Track prompts, platforms, company presence, valid recommendations, top-three and rank-one performance, framing, and citation sources across the online betting category and for specific brands within it.
2. Identify the sources shaping AI answers. Find the editorial, review, forum, regulatory, directory, owned, and search-visible sources that influence brand framing and recommendation eligibility across AI platforms.
3. Build the citation architecture plan. Strengthen the public evidence layer so AI systems have more accurate, consistent, and persuasive source material to synthesize when constructing buyer shortlists in this category.

Commercial Takeaway

AI-led discovery is changing where buyer shortlists are formed in the online betting category. Bettors are no longer relying solely on brand marketing or traditional search results. They are asking AI systems to recommend sportsbooks, compare platforms, and evaluate offers. The shortlist is being built inside AI-generated responses, and brands that are not recommended are being bypassed at the moment of highest buyer intent.

Brands can lose recommendation-stage visibility even when they appear in AI answers. bet365 demonstrates this plainly: it is widely mentioned but rarely recommended, and its modeled recommendation value reflects that gap. Competitors can intercept demand in high-intent prompt clusters, as DraftKings and FanDuel have done across all three buyer stages and all six platforms tested.

Traditional search and source visibility still matter because they contribute to the public evidence layer that AI systems draw on. But search presence alone is not sufficient. The market now rewards recommendation power, not just mention presence. The opportunity is to improve recommendation-stage visibility by building the citation architecture that earns ranked, positively framed placement in AI-generated shortlists. With a modeled monthly AI opportunity value of $76.2 million in this category, the competitive cost of absence at the recommendation stage is not marginal.

See Where Your Brand Stands in AI Recommendations

The benchmark shows the market shape. A brand-specific analysis reveals which prompts your brand wins or loses, which AI platforms are under-recognizing your brand relative to competitors, which source layers are shaping the recommendations buyers see, and what changes may improve your shortlist eligibility across high-intent buyer stages.

CiteWorks Studio can show where your brand appears, where competitors are being recommended instead, which prompt clusters carry the most commercial risk, which sources are shaping AI answers in your category, and what needs to change to improve recommendation-stage visibility.

Request an AI Visibility Audit or AI Company Discovery Report to understand your brand's current position in AI-led discovery for the online betting category.

Benchmark Source

This analysis is based on the 2026 AI Market Discovery Index for Online Betting Sites, published by LLM Authority Index. The benchmark dataset and public industry report were supplied for this category analysis.