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

The Game Behind The Game

Vaidik Mandloi

Reprinted from: Mars Finance

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

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

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

To manage this risk, teams usually buy insurance. Agents design policies and find willing insurance companies to underwrite; those 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 and only exists in the quotes given to the team.

Now, there’s another way to handle the same risk.

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 at 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 also grow.

Now, team salaries are no longer just basic season wages; they include 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 payments must be made.

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 find 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 with 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 such 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, that number 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 spans event cancellations, athlete injuries, 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 sports-related accident and health premiums each year; 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 achieving goals but also on numerous 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, retained 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 depends largely on the reinsurance companies’ own portfolios.

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

When Probabilities Are No Longer Hidden

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

Imagine what would happen if this probability were priced openly in the market. 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 either $1 or $0. For example, if the price is $0.06, it implies a 6% 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 probability and price in real time.

This mechanism is already in use. Game Point Capital 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 difference means millions of dollars in premium costs.

You might ask: These are just trader numbers—why take them seriously? Why trust market-based odds more than insurer models?

Extensive research shows that market-implied 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 show similar success rates: in about 1,000 NBA games in the 2024–25 season, Polymarket and traditional sportsbooks had nearly identical prediction accuracy.

In games with over 95% implied probability, both approaches correctly predicted outcomes over 90% of the time.

Election markets are even more conclusive. During the 2024 U.S. presidential election, a study comparing Polymarket and traditional polls found Polymarket’s predictions to be more accurate, especially in swing states.

When thousands of people continuously update expectations in real time, collective probabilities tend to be surprisingly close to reality.

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

But for these markets to be truly useful, they must be scalable. During recent major events like the Super Bowl, Kalshi handled about $22 million in trading with no 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 analysts, tracking how probabilities change 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—integrating the entire field into a clear overview of market size.

When a 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 risks related to viewership targets; studios can hedge box office milestones. In principle, any payoff based on a clear, verifiable outcome can be turned into a tradable contract.

Institutions no longer need to negotiate bespoke insurance contracts; the outcome itself can be traded openly.

The final piece that makes this structure truly usable for institutions 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 with trading activity. This means market participants can be identified, screened, and connected to real entities, rather than remaining completely anonymous. This also makes contract settlement, exposure management, and integration into existing compliance frameworks possible.

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.