When teams hedge risks using prediction markets, a hundred-billion-dollar financial market emerges

Prediction markets are no longer just places for fans to trade: now, teams are starting to use them themselves.

Here’s a simple example: a basketball team promises the head coach that if they make the playoffs, they’ll pay a $20 million bonus. This is a straightforward incentive: if the team wins enough games and reaches the playoffs, the bonus is paid.

But from a financial perspective, this promise is a huge liability. As soon as they make the playoffs, the $20 million must be paid out, regardless of the team’s income or financial health that year.

To manage this risk, teams usually buy insurance. brokers design policies and find insurers willing to underwrite them; insurers may then transfer part of the risk to reinsurance companies to avoid bearing the full exposure alone. The final price of this coverage is privately negotiated between institutions. The premium implicitly reflects the team’s probability of advancing, but this number is never made public — it only exists in the quotes given to the team.

Now, the same risk has another solution.

The team’s probability of advancing is already priced elsewhere. In prediction markets, this probability is traded daily, visible to everyone, and fluctuates in real time as expectations change.

Teams no longer have to rely solely on private insurance quotes; they can reference the publicly available market probabilities to hedge part of the bonus risk.

How sports insurance works

To understand how this system operates, let’s look at what has changed in the sports industry over the past 20 years.

Today, professional sports generate nearly $560 billion annually, growing about 7% per year. Revenue mainly comes from media rights, sponsorships, licensing, streaming platforms, and global commercial partnerships.

As revenue sources expand, the contracts tied to them have also grown.

Now, team salaries include not just base season wages but also performance-based clauses linked to specific milestones. For example, if a team reaches the conference finals, the head coach might earn an extra $5 million bonus; players hitting 1,000 rushing yards, scoring 25 goals, or reaching minimum game appearances can also earn extra pay; some contracts even specify that bonuses increase if the team advances further in the playoffs. These clauses are written into contracts with automatic triggers: once conditions are met, the team must pay the corresponding reward.

Teams manage these exposures through insurance, rather than passively bearing the risk and hoping incentives don’t all hit at once. They work with professional brokers, who seek insurers willing to cover performance payouts; these insurers often transfer part of the risk to reinsurance companies, spreading the exposure across larger pools of capital. A simple bonus clause in a contract can, behind the scenes, become an entire financial chain.

Insurers measure exposure using a concept called “insurable value,” which roughly equals the future income dependent on continued performance—such as salaries, incentives, endorsement income. If a player cannot participate, these revenues are affected.

Data shows explosive growth in these exposures. For example, during the 2014 FIFA World Cup, the total insurable value of all participating teams was estimated at about $7.3 billion. By the 2022 World Cup, this figure soared to around $25 billion. In less than a decade, the financial value directly tied to performance more than tripled.

When so much income is linked to performance, uncertainty can no longer be left to chance; it must be managed. An entire industry has emerged: the global sports insurance and reinsurance market is currently valued at about $9 billion and is expected to double by 2030. Coverage includes event cancellations, athlete disabilities, sponsorship guarantees, and performance bonuses.

Market players include specialized brokers like Game Point Capital, which handle hundreds of millions of dollars in sports insurance annually; underwriters like Lloyd’s, which write over $200 million in annual sports-related accident and health policies; and large reinsurance firms that also cover catastrophes like hurricanes and aviation accidents. Because playoff bonuses are priced similarly to risks like storms and earthquakes, the pricing process is cautious and private. Brokers and insurers negotiate, each using their models to estimate milestone probabilities and set premiums. Teams see only the costs, not the underlying probabilities.

Why private reinsurance is more expensive

The price of sports insurance depends not only on the likelihood of teams hitting their goals but also on many external risks.

Ideally, if a team has a 10% chance of reaching a milestone, the premium would roughly reflect that 10% risk plus a small profit margin. But the reinsurance market is not ideal.

Reinsurers have limited capital. Every dollar invested in playoff bonus insurance reduces the capital available for hurricanes, aviation, and catastrophe bonds. They must balance portfolios across different regions and risk types. When evaluating sports risks, they consider factors like probability, available capital, outcome volatility, and correlation with existing risks.

Another constraint is that the sports reinsurance market is highly concentrated. A few global firms dominate most underwriting capacity. Whether a team can access coverage, and how much, often depends on the reinsurance company’s own portfolio.

All these factors add up, meaning the premium offered to teams includes not just the raw probability but also many hidden costs.

When probabilities are no longer hidden in a black box

Until now, the probability of outcomes has been embedded in every step: reinsurance modeling, broker negotiations, premium setting. But these numbers have never been public.

Imagine if these probabilities were priced openly in the market—what would happen? Prediction markets have realized this in a very interesting way.

Platforms like Kalshi have launched contracts based on discrete real-world events, including sports outcomes. These contracts pose simple questions: Will Team X make the playoffs?

Each contract settles at $1 if the event occurs, or $0 if it does not. For example, if the contract trades at $0.06, it implies a 6% market-implied probability.

This number isn’t set by an underwriting committee; it’s determined by real buyers and sellers trading with real money, updating their assessments of the probability and price in real time.

This mechanism is already in use. Game Point Capital, for example, uses Kalshi markets to hedge basketball performance bonuses. In one case, a playoff-related contract traded at about 6%, while off-exchange quotes implied around 12-13%. In another, a second-round advancement contract traded near 2%, while private reinsurance prices were 7-8%.

That’s not a trivial difference. For a $20 million exposure, a 6% versus 12% implied probability means millions of dollars in premium cost.

You might ask: these are just trader numbers—why trust them? Why are market-implied probabilities more credible than models used by insurers?

Extensive research shows that market-based odds are powerful predictors of actual outcomes. Decades of academic studies on sports betting markets demonstrate that bookmaker odds are highly efficient at forecasting results. More recently, comparisons between prediction markets and traditional sports betting for the 2024–25 NBA season—covering about 1,000 games—show that Polymarket and conventional sportsbooks have nearly identical prediction accuracy.

In games where the market-implied probability exceeds 95%, both approaches have accuracy above 90%.

Election markets tell an even clearer story. During the 2024 U.S. presidential election, a study comparing Polymarket and traditional polls found that Polymarket provided more accurate predictions, especially in swing states.

When thousands of participants continuously update their expectations in real time, the collective probability often aligns remarkably well with reality.

Prediction markets enable continuous price discovery. New information is constantly incorporated and priced, without waiting for the next review by an underwriting committee.

But for markets to be truly useful, they must be scalable. During recent major events like the Super Bowl, Kalshi handled about $22 million in trading without significant price swings. This indicates both sides have genuine depth, enough to support large hedges without impacting prices.

As these markets grow, a new class of permissionless financial instruments has emerged around prediction markets.

For example, Kalshinomics analyzes event contracts like stock or bond analysis, tracking how probabilities evolve over time, liquidity before and after major events, and whether prices deviate from fundamentals.

Platforms like PredictionIndex aggregate and rank various prediction markets, showing total trading volume, contract types, blockchain platforms, and trading mechanisms—visualizing the entire ecosystem’s scale.

When a result’s probability can be priced in real time and can effectively absorb capital, it becomes a tool that institutions can actually use. Teams can hedge performance bonuses directly with publicly traded probabilities; sponsors can hedge viewership risk; studios can hedge box office milestones. In principle, any payoff tied to a verifiable outcome can be converted into a tradable contract.

Institutions no longer need to negotiate bespoke insurance contracts; outcomes themselves can be traded openly.

The final piece that makes this structure truly practical is identity. Traditional insurance is effective because counterparties are verified, contracts are enforceable, and exposures are auditable. Public markets have lacked this layer.

Companies like Dflow are linking real-world identities to trading activity. This means market participants can be identified, screened, and connected to real entities, rather than remaining completely anonymous. This enables contract settlement, exposure management, and integration into existing compliance frameworks.

In practice, it’s starting to look less like a typical trading venue and more like a functional insurance layer built directly on transparent probabilities.