How Rights Holders Monetise AI Predictions at Live Events

How Rights Holders Monetise AI Predictions During Live Events

For decades, sports rights holders—leagues, clubs, broadcasters, and streaming platforms—earned revenue from broadcasting rights, sponsorships, and advertising. That model is under pressure. Linear TV viewership declines. Advertising rates compress. Streaming services demand lower rights fees to justify their investments.

But there's a new revenue stream emerging, and it's attached to something rights holders already produce: insight and prediction about the sport happening in real time.

When an AI system predicts that a player has a 72% probability of making their next free throw, or a team has a 63% chance of scoring in the next 5 minutes, or a tennis player has a 58% chance of winning the next set—that's valuable information. Not just for bettors. For broadcasters who want richer storytelling. For streaming platforms who want to increase engagement. For sportsbooks who want to offer better products. For fans who want to understand the sport at a deeper level.

Rights holders who can generate, package, and monetise these predictions are building new revenue with minimal cannibalization of existing streams. A broadcaster might lose some viewers to gambling, sure. But they can license that same AI prediction intelligence to 5-10 different sportsbooks, each paying $50,000-$200,000 annually. That's $250,000-$2 million in new revenue from data that was already being generated.

This article explores the mechanics of this emerging model, the partners who are already executing it, and the framework for rights holders to do the same.

Why Rights Holders Should Own This Opportunity

Before diving into execution, it's important to understand why this is worth doing now.

First, it's low-cost revenue. Rights holders already employ statisticians, analysts, and data teams. They already have feeds of real-time game data, player tracking, and performance metrics. They already own relationships with broadcasters and media partners. An AI prediction system layers on top of existing infrastructure with minimal incremental cost. Once built, marginal revenue per additional licensing deal approaches zero.

Second, it aligns with betting regulation, not against it. When a rights holder licenses AI predictions to sportsbooks, it's positioning itself as a partner in the ecosystem, not as a threat. Regulators see rights holders and sportsbooks working together and view that positively. It's cleaner than a rights holder passively ignoring an $8 billion betting market that's built on their content.

Third, it's brand-safe and fan-friendly. The AI predictions aren't gambling advertising. They're insight into the sport. A broadcaster can use them to enhance commentary ("AI predicts 73% chance of a goal in the next 10 minutes"). A streamer can use them for engagement ("Will you bet on what AI predicts?"). A sportsbook can use them for better odds. All of these enhance the experience; none damage the brand.

Fourth, it unlocks investor interest. Rights holders are increasingly valued on their data and technology capabilities, not just content rights. Owning an AI prediction system that generates recurring revenue across multiple verticals (broadcast, betting, streaming, fantasy) is worth a meaningful valuation multiple.

Fifth, it's defensible. Once a rights holder invests in building proprietary prediction models, they have a moat. Their data is exclusive. Their models are trained on years of their sport's data. Competitors can't easily replicate this. It's a source of sustainable competitive advantage.

The Revenue Models: How Rights Holders Monetise

There are several ways rights holders can extract value from AI predictions:

Model 1: Sportsbook Licensing

A rights holder licenses its AI predictions directly to sportsbooks. The sportsbook uses these predictions to:

• Inform odds pricing
• Identify mispriced markets
• Risk-manage liability
• Populate prop betting markets
• Feed player prop recommendations to the betting public

Typical deal structure: Recurring annual license fee ($50,000-$500,000+, depending on the size of the sportsbook and the exclusivity of the data). Often tiered: base fee + revenue share on bets that use the AI data.

Example: A Premier League club licenses its AI predictions on player performance to DraftKings for their Premier League prop betting. The club receives $150,000 annually + 1% of handle on UK-based Premier League props. Over a year, with £500M in handle, that's $150K + $50K = $200K in revenue.

Why sportsbooks buy: They get access to exclusive, high-fidelity data they couldn't generate themselves. A club's internal tracking data is more granular than what's publicly available. A leagues's injury and roster data is authoritative. The prediction models leverage this proprietary context.

Model 2: Broadcaster and Streaming Partnerships

A rights holder licenses AI predictions to broadcasters and streamers to enhance commentary, engagement, and on-screen graphics.

Typical deal structure: Annual license fee ($100,000-$1M+) that covers all AI predictions throughout a broadcast season. Often bundled with existing media rights deals to reduce friction.

Example: ESPN licenses AI predictions from the NBA to power real-time graphics during broadcasts. "AI predicts 78% chance that LeBron will have 20+ points" appears on screen during the third quarter. Viewers engage more deeply with the game. ESPN builds more valuable advertising relationships. The NBA gets paid for the prediction data.

Why broadcasters buy: They get analytics-driven content that differentiates them from competitors. A broadcaster using AI predictions offers a deeper experience than one without. This drives viewership and engagement metrics. For streamers, it's a tool to drive engagement and time-spent.

Model 3: API Licensing to Third-Party Platforms

A rights holder exposes AI predictions via API, allowing multiple third parties (fandoms, fantasy platforms, news sites, etc.) to build products on top.

Typical deal structure: API access fee ($10,000-$50,000 annually) + per-call fees ($0.001-$0.01 per prediction call). Volume-based pricing.

Example: The ATP Tour licenses its AI predictions via API. Tennis news sites, fantasy operators, and fan engagement platforms integrate the API. They all subscribe at different tiers. Total revenue: $500,000+ annually across 50+ partners.

Why third parties buy: They can offer richer content and tools without building their own prediction models. The infrastructure is expensive; licensing is cheap.

Model 4: Embedded Betting Products

A rights holder creates its own betting product (via a partnership with a licensed operator) and uses its AI predictions to power superior odds and recommendations.

Typical deal structure: Revenue share (50/50, 60/40, etc.) on betting handle from bettors who are using AI predictions.

Example: Paramount+ (which holds NFL rights) partners with a licensed sportsbook to offer betting inside the streaming app. The AI predictions power the proposition bets and player props. Paramount+ gets a 40% revenue share. Users never leave the platform. Handle is $100M+ annually. Revenue to Paramount: $2M+.

Why rights holders do this: They maintain control of the user experience. They capture more value. They deepen engagement. They build a direct relationship with bettors (which is valuable data and relationship).

Model 5: Fantasy Sports Partnerships

A rights holder licenses AI predictions to fantasy sports platforms to enhance player recommendations and projection accuracy.

Typical deal structure: Licensing fee ($50,000-$200,000) + revenue share on DFS contests that feature their sport.

Example: A European football league licenses AI player projections to DraftKings and FanDuel. The platforms use the predictions to generate more accurate player salaries and projections. More accurate contests drive better user experience and higher volume. The league receives $200,000 annually + 2% of DFS handle on their sport.

Why fantasy platforms buy: Improved projection accuracy drives better user experience and volume. It's a competitive advantage.

Real-World Success: Who's Doing This Well

Several organizations have already implemented AI prediction monetisation. Their experience offers practical lessons.

La Gazzetta dello Sport (Italian sports media, owned by RCS): Developed AI predictions for Serie A football and licensed them to multiple sportsbooks across European markets. The platform generates predictions on player performance, goals, and match outcomes. Revenue: multiple six figures annually from licensing deals, while also enhancing their own editorial content.

a global broadcaster partner (Global sports streamer): Deployed AI predictions across their football, basketball, and combat sports coverage. The system generates 1.1 billion predictions annually, improving content and engagement. a global broadcaster partner partners with sportsbooks to license the predictions, generating recurring revenue while maintaining their core streaming business. The engagement uplift (18x in some cases) makes their platform more valuable to advertisers.

a heritage racing partner (UK horse racing): Invested in AI prediction systems for thoroughbred racing. The platform predicts race outcomes, horse performance, and betting markets. Revenue streams include licensing to sportsbooks, media partnerships, and direct-to-consumer prediction content for racing enthusiasts. Multiple millions annually.

MARCA (Spanish sports media): Built AI predictions for La Liga, Copa del Rey, and international football. The platform generates predictions that power their editorial content, licensing deals with sportsbooks, and fantasy sports partnerships. Revenue: hundreds of thousands annually and growing.

premium US sports publishers: Through FairPlay's BetTech partnership, deployed AI predictions that power sportsbook betting products and streaming enhancement. The infrastructure generates over $5M in annual revenue through direct betting partnerships and data licensing.

The Technical Foundation: What You Need to Build

To monetise AI predictions, a rights holder needs:

1. Real-time data infrastructure: Player tracking data, box scores, injury reports, weather data, Vegas lines, betting action, and historical performance metrics, all flowing in real time.

2. Prediction models: Machine learning models that ingest the data and output probabilities for relevant outcomes (player props, match outcomes, in-game events).

3. Prediction API: Infrastructure to serve predictions to external partners via REST API or similar, with rate limiting, authentication, and SLAs.

4. Explainability: Tools to show why the model made a specific prediction, what inputs mattered most, and how confident it is. Partners want to understand the reasoning.

5. Auditing and compliance: Audit trails showing how each prediction was generated, for regulatory compliance and customer transparency.

6. Support infrastructure: 24/7 monitoring, SLAs, technical support for partners, and version management for API changes.

Building this from scratch is expensive and time-consuming: $2-5M+ in initial development, $500K-$1.5M annually in ongoing operations.

Alternatively: A rights holder can partner with an existing platform (like FairPlay's FairPlay AI engine) that already has this infrastructure. The cost is licensing fees to the platform (typically 15-30% of prediction-related revenue) instead of full build. But time-to-market shrinks from 18 months to 4-6 months, and the operational burden shifts to the platform.

Most rights holders choose the partnership route, especially initially. As revenue grows, some build proprietary systems to capture more margin.

The Sales and Partnership Process

How does a rights holder actually go to market with AI predictions?

Step 1: Build or partner. Decide whether to build proprietary models or license from an existing platform. Most rights holders partner initially.

Step 2: Identify anchor customers. Who are the first sportsbooks, broadcasters, or platforms you'll approach? Choose partners who:

• Are already betting on your sport heavily (existing motivation to pay)
• Have the technical sophistication to integrate an API
• Can move quickly on decision-making
• Can become a reference customer

Step 3: Develop a data room and pilot. Create a proposal that shows:

• Sample predictions (last season's games, showing accuracy retrospectively)
• Data flow (what will be delivered, how often, format)
• Technical documentation (API specs, rate limits, SLAs)
• Commercial terms (pricing, exclusivity, revenue share)

Offer a 4-8 week pilot to an anchor customer at a reduced rate. This de-risks their decision and gives you real-world feedback.

Step 4: Iterate and expand. Once an anchor customer is happy, use that as a reference to sign 2-3 more customers. Each incremental customer has lower sales cost because you have proof points.

Step 5: Build distribution partnerships. Partner with platform providers (like FairPlay) who have direct relationships with operators and broadcasters. They become your sales force in exchange for a revenue share.

Step 6: Scale and optimise. As revenue grows, invest in product improvements. Track which predictions drive the most value to customers. Double down on those. Discontinue predictions no one cares about.

Pricing Strategy for Predictions

What should a rights holder charge for AI predictions? There are several approaches:

Flat annual licensing fee: $100,000-$500,000 per major partner (sportsbooks, broadcasters). Simple, easy to budget for. Works well if you have a small number of partners.

Per-prediction pricing: $0.001-$0.01 per prediction API call. High-volume partners prefer this because they pay for what they use. You capture upside if volumes grow.

Revenue share: 1-5% of handle on bets that directly use your predictions. Aligns incentives. Partners only pay if they make money. But requires robust tracking and accounting.

Tiered pricing: Combination approach. Base fee + per-call fees + revenue share. Captures value across customers of different sizes.

Recommended approach: Start with a flat annual fee for simplicity. As partnerships mature, negotiate rev-share add-ons if the partner scales successfully. Use per-call pricing for high-volume API access.

For a top-tier rights holder (major league or sport), total prediction-related revenue of $1-3M annually is achievable within 3 years with 5-10 licensed partners. For emerging rights holders, $200K-$500K annually is realistic within 2 years with 2-3 partners.

Addressing Regulatory and Reputational Risk

One concern rights holders voice: "Will betting partnerships hurt our brand?"

The answer: not if executed correctly.

Key principles:

• Position as a data partnership, not a betting sponsorship.
• Require partners to have proper licenses and responsible gambling programs.
• Don't promote or advertise betting to your audience directly.
• Be transparent: "We license prediction data to licensed sportsbooks."
• Ensure predictions are used for odds and markets, not for driving problem gambling.

Most regulated markets actually view rights holder-operator partnerships positively. It demonstrates collaboration and responsibility, not opportunism. The NFL, NBA, Premier League, and other major rights holders have all embraced betting partnerships.

As long as you're licensing data to licensed operators with proper regulatory compliance, and you're not encouraging irresponsible gambling, reputational risk is minimal.

Challenges and Mitigations

Challenge 1: Prediction accuracy under pressure. If your predictions are wrong, partners lose money. They'll stop licensing them.

Mitigation: Invest in model quality, backtest extensively, provide confidence intervals, be transparent about limitations, and continuously improve based on performance feedback.

Challenge 2: Data quality and latency. If injury data is stale or player performance data lags, predictions suffer.

Mitigation: Build redundancy in data feeds, create monitoring/alerting for data quality issues, and set clear SLAs with data providers.

Challenge 3: Exclusive licensing conflicts. If you license to DraftKings and FanDuel exclusively, you alienate other operators.

Mitigation: Offer tiered exclusive deals (regional vs. global, sport-specific, etc.) or non-exclusive licensing with volume discounts.

Challenge 4: Operator consolidation. As sportsbooks merge, the number of potential customers decreases.

Mitigation: Broaden your customer base beyond sportsbooks (broadcasters, streamers, fantasy platforms, news sites). Diversification reduces risk.

Challenge 5: Integration friction. Operators are busy; integrating a new API is a project.

Mitigation: Make integration as easy as possible (good documentation, sample code, technical support). Partner with platforms that handle integration on behalf of operators.

The Financial Opportunity

For a major rights holder (league or top club) with global reach:

• Year 1: 2-3 licensing partnerships signed. Annual revenue: $250K-$500K. Investment: $500K (platform licensing or early-stage development).
• Year 2: 5-7 partnerships, broader customer base. Annual revenue: $1-2M. Investment: $250K (incremental platform licensing, sales).
• Year 3: 10+ partnerships across sportsbooks, broadcasters, streaming platforms, fantasy operators. Annual revenue: $2-3M+. Margins: 60%+ (after platform licensing costs).

Over 5 years, cumulative revenue: $8-12M. Cumulative investment: $2-3M. Net present value (assuming 10% discount rate): $5-8M in value creation.

This is real, recurring, defensible revenue that diversifies a rights holder's portfolio away from traditional media and sponsorship.

The Future: Convergence of Betting and Rights

The trajectory is clear: rights holders and betting operators are converging. The rights holders who move quickly to monetise their data and predictions will capture disproportionate value.

In 3-5 years, it will be standard practice for major sports to license AI predictions. The early movers will have locked in partnerships and built operational efficiency. Late movers will find the best partners already taken and have to accept lower terms.

Next Steps

If you're a rights holder exploring AI prediction monetisation, the first step is to understand your data assets. What proprietary information do you have? What predictions are most valuable to operators and broadcasters?

FairPlay's FairPlay AI engine already powers prediction intelligence for partners including premium US sports publishers, La Gazzetta dello Sport, MARCA,. We generate 1.1 billion predictions annually, processing 125 million daily price changes.

If you're ready to explore a prediction monetisation strategy, let's talk about your specific sport, your data assets, and your partnership goals. We can help you design a go-to-market strategy and execute it.

The opportunity is real. The time window is now. Rights holders who move first will build sustainable competitive advantages and unlock millions in recurring revenue.