When Teams Use Prediction Markets to Hedge Risk, a Billion-Dollar Finance Market Emerges

Original Title: The Game Behind The Game

Original Author: Vaidik Mandloi, TOKEN DISPATCH

Original Translation: Luffy, Foresight News

The prediction market is no longer just a place for fans to trade: now, teams themselves are starting to use it.

Take a simple example: a basketball club promises to the head coach that if the team makes it to the playoffs, a $20 million bonus will be awarded. This is a direct and clear incentive measure, where if the team wins enough games to enter the playoffs, the bonus will be paid out.

However, from a financial perspective, this commitment represents a significant liability. Once the playoffs are reached, this $20 million must be paid out, regardless of the team's annual revenue or financial situation.

To manage this risk, teams usually purchase insurance. Agents will design the policy and find an insurance company willing to underwrite it; the insurance company may then pass on some of the risk to a reinsurance company to avoid bearing the full exposure alone. The final price of this coverage is privately negotiated between the parties. The premium implicitly reflects an assessment of the team's probability of advancement, but this number is never made public and only exists within the quoted price provided to the team.

Now, the same risk has another solution.

The team's probability of advancement has already been priced elsewhere. In the prediction market, this probability is traded daily, visible to all, and fluctuates in real-time with changing expectations.

The team no longer needs to rely solely on private insurance quotes; it can refer to the public market probability to hedge some of the bonus risk.

How Sports Insurance Works

To understand how this system operates, let's first look at how the sports industry has evolved over the past 20 years.

Today, the annual revenue of professional sports is close to $560 billion, with an annual growth rate of about 7%. Revenue mainly comes from media rights, sponsorships, licensing, streaming platforms, and global commercial partnerships.

As revenue sources have expanded, so have the contracts tied to them.

Today, a team's compensation is no longer just the season's base salary but also includes many performance-based clauses tied to specific milestones. For example, if the team reaches the conference finals, the head coach may receive an additional $5 million bonus; players can earn extra rewards for reaching 1000 rushing yards, 25 goals, or meeting the minimum number of appearances; some contracts even stipulate that if the team goes further in the playoffs, the bonus will increase further. These clauses are written into contracts as automatic triggers, and once the conditions are met, the corresponding compensation must be paid.

The team will manage this kind of exposure through insurance, rather than passively bear the risk, hoping that the incentive will not concentrate and trigger a crisis. They work with professional brokers, who then approach insurance companies willing to underwrite performance-based payouts; these insurance companies usually transfer part of the exposure to reinsurance companies, spreading the risk to a larger pool of funds. A simple bonus clause in the contract will, behind the scenes, become an entire financial chain.

Insurance companies measure the scale of exposure using a concept called "insurable value," which is simply put as: future income dependent on continued performance, including salaries, incentives, endorsement income, etc. Once a player is unable to compete, all of this income is affected.

Explosive growth of this kind of exposure can be visually seen from the data. For example, during the 2014 FIFA World Cup, the total insurable value of all participating teams was estimated to be around $73 billion. However, by the 2022 World Cup, this number had soared to around $250 billion. In less than ten years, the financial value directly linked to performance had more than doubled.

When so much income is tied to performance, uncertainty cannot be left to chance; it must be managed. An entire industry has thus emerged, with the current estimated size of the global sports insurance and reinsurance market being around $9 billion, expected to double by 2030. Its coverage ranges from event cancellations, athlete disability, to sponsor guarantees and performance bonuses.

In the market, there are professional brokers like Game Point Capital, handling hundreds of millions of dollars in sports insurance every year; on the other side are underwriting entities like Swiss Re, signing over $200 million in sports-related accident and health premiums each year, along with large reinsurance companies that also underwrite catastrophes such as hurricanes and aviation accidents. Because playoff bonuses, in the pricing logic, belong to the same category of risk as storms and earthquakes.

Therefore, the pricing process is cautious and confidential. Brokers negotiate with insurance companies, insurance companies negotiate with reinsurance companies, each party using its own models to estimate the probability of reaching milestones and calculating premiums. The teams only see the cost but do not see the underlying probabilities.

Why Private Reinsurance Prices Are Higher

The price of sports insurance depends not only on the probability of the team achieving its goals but also on a large number of external risks.

Ideally, if a team has a 10% probability of reaching a milestone, the premium roughly reflects 10% risk + a small profit margin. However, the reinsurance market is not an ideal world.

Reinsurance companies have limited capital. For every $1 invested in playoff bonus insurance, $1 less is available for hurricane, aviation, catastrophe bond, and other businesses. They must continuously balance their portfolios between different regions and risk types. Therefore, when evaluating sports risk, they comprehensively consider: probability, retained capital, result volatility, and correlation with existing risks.

Another constraint is: the sports reinsurance market is highly concentrated. A few global players hold the majority of underwriting capacity. Whether a team can obtain coverage and how much coverage they can get often depends on the reinsurer's own portfolio.

All these factors combined result in the final premium offered to the team, which not only includes the pure milestone probability but also a significant amount of hidden costs that the team cannot see.

When Probability Is No Longer Hidden in a Black Box

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

Now, imagine: what happens when this probability is priced in an open market? Prediction markets have achieved this in a very interesting way.

Platforms like Kalshi have launched contracts for discrete real-world events, including sports outcomes. The contracts pose a simple question: Will Team X make it to the playoffs?

Each contract ultimately settles at either $1 or $0. For example, if a contract is traded at $0.06, it implies an implied probability of 6%.

This number is not set by an underwriting committee but is traded by real buyers and sellers with real money, constantly adjusting based on their real-time judgment of probability and price.

This mechanism has already been put into practice. Game Point Capital, for instance, used the Kalshi market to hedge basketball-related performance bonuses. In one case, a playoff-related contract traded on the exchange at around 6%, while the over-the-counter market implied a price of around 12-13%. In another case, a second-round advancement contract traded on the exchange close to 2%, while the private reinsurance market had a price of 7-8%.

This is far from a trivial difference. For a $20 million exposure, the gap between 6% and 12% implied probabilities means a cost difference of millions of dollars in premiums.

You might ask: These are just numbers pointed out by traders, why should we take them seriously? Why are they more trustworthy than an insurance company's model?

A wealth of research shows that market-based odds are powerful predictors of real-world outcomes. Academic studies spanning decades on the sports betting market have shown that bookmaker odds are highly efficient at predicting match outcomes. More recently, comparing prediction markets with traditional sports betting directly: in a study of around 1000 NBA games in the 2024–25 season, Polymarket's prediction success rate was nearly identical to that of traditional betting platforms.

In matches where the market's implied probability exceeds 95%, both sides have accuracy above 90%.

The conclusions in election markets are more pronounced. During the 2024 U.S. presidential election, a study comparing Polymarket to traditional polling showed that Polymarket was more accurate in predicting the final outcome, especially in swing states.

When thousands of people continuously update their expectations in real-time markets, the collective probability often astonishingly aligns with reality.

Prediction markets have facilitated continuous price discovery. Any new information entering the system is consistently updated and priced, without the need to wait for the next underwriting committee review.

However, for a market to truly have practical value, it must be able to handle scale. In recent major events like the Super Bowl, Kalshi processed around $22 million in trades without significant price fluctuations. This demonstrates that both long and short positions in the market have genuine depth to support large-scale hedging without impacting prices.

As these markets grow, a new set of permissionless financial tools has emerged around prediction markets.

For example, Kalshinomics involves analyzing event contracts like an analyst would analyze stocks and bonds, tracking how probabilities change over time, liquidity performance around major events, and whether prices deviate from fundamentals.

There are platforms like PredictionIndex that centrally track and rank various prediction markets, where you can see total trading volume, contract types, public chains, trading mechanisms, bringing the entire field together to visually represent market scale.

When the probability of an outcome can be dynamically priced and effectively attract funds, it becomes a tool that institutions can truly use. Teams can now directly hedge performance bonuses using publicly traded probabilities, sponsors can hedge risks related to viewership targets, and studios can hedge box office milestones. In principle, any income dependent on a clear and verifiable result can be transformed into a tradable contract.

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

For this structure to be truly usable by institutions, there is one final puzzle piece: identity. Traditional insurance works because the counterparty is verified, contracts are enforceable, and exposures are auditable, elements that have been missing in the open markets.

Companies like Dflow are binding real-world identities to transactional behavior. This means that market participants can be identified, screened, and associated with real-world entities rather than being entirely anonymous. This also enables contract settlement, exposure management, and position alignment within existing compliance frameworks.

In practical effect, it is increasingly looking less like a typical trading venue and more like a functional insurance layer operating directly on top of public probability.

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