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Discussed in this piece:
1) How a football club and a New York bar used prediction markets to hedge specific risks.
2) The market participants and institutions that took the other side of those trades.
3) The kinds of “insurance‑like” use cases prediction markets could enable in the future.
This piece is written and researched by Omar El Safy, and edited by Pet Berisha.
…But before we get into it, if you haven’t downloaded our Q1 2026 report - you should definitely do that. It’s totally free 👇️
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What does a football club in the Navarre region of Spain and a bar on the Upper East Side of New York have in common?
Not much at all.
Apart from the fact that both of them used prediction markets to hedge their balance sheets.
Osasuna, a Spanish team in La Liga, wanted to protect itself against relegation from Spain’s top tier. A European club CFO’s worst nightmare, as it comes with the pulverisation of revenue
Across the world, The Jeffrey, a bar on the Upper East Side, NYC, tried to protect itself against a free-drinks promotion during the NBA finals.
In financial terms, both are examples of hedging in action: tools to trade a fixed, known cost today for protection against a much larger loss tomorrow.
It’s standard for large corporates that regularly use futures, options, and swaps, but still rare for SMEs.
Prediction markets are often portrayed as pure vanilla speculation tools, and the evidence still supports that, for now… However, these 2 case studies evidently exemplify that they can also behave like financial instruments: ways for real businesses to hedge specific risks.
01 · What Actually Happened?
Osasuna
Osasuna, located in northern Spain, is best known as the birthplace of the running of the bulls.
They compete in the Spanish La Liga, and in the final weeks of the 2025/26 season, they were fighting for their survival. As previously mentioned, relegation is devastating for any European soccer team. Your best players want to leave. Coaches too. Your revenue is destroyed across every metric: gate receipts, sponsorship, broadcast and more.
Through intermediaries (who used Kalshi), they purchased relegation coverage through the British insurance broker Howden.
At a €1.2 million premium (fixed fee), for a potential €6 million payout, as insurance in case the club dropped into the second division.
The club’s own statement described it as “insurance to cover potential financial consequences stemming from possible relegation, a common practice in professional football.”
For a club of Osasuna’s size (mid-table side), this would be detrimental to their balance‑sheet hit that can define the next few seasons, which is why they were willing to pay seven figures just to keep that worst‑case scenario under control.
What the payoff of their options looked like:
Table shows a stylised €6m payout here; the real relegation hole would likely be larger once TV, sponsorship and matchday are combined.
No coverage, relegated. The full revenue loss hits the club with nothing to offset it.
Coverage, relegated. The club has paid €1.2 million. The insurer pays back roughly €6 million. Still worse than staying up, but the balance sheet shock is cushioned.
Coverage, survive. Osasuna finished 17th on goal difference and stayed in La Liga. No payout. The €1.2 million is gone, but it was always the cost of not being exposed to the worst case.
Osasuna stayed up on goal difference, so the hedge never paid out and the club “only” lost the €1.2 million premium, the cost of not being exposed to the worst case.
The Jeffrey
The Jeffrey bar, Upper East Side, New York
The Jeffrey’s problem was different and smaller.
Andrew Freedman (owner) was approached by Kalshi after a similar promotion was spotted by the Kalshi team during the NBA Eastern Conference Finals, which had cost him $3,700 in free drinks.
Freedman, whose day job is as co-managing partner of law firm Olshan, whose focus is on advising hedge funds, was open to the idea and placed the trade on Kalshi himself.
The problem: a New York Knicks win in Game 1 of the NBA Finals could turn a generous promotion at the Bar into a real loss.
The mechanics looked like this:
Customers who arrived before tip-off would get a free bar tab of up to $100 if the Knicks beat the San Antonio Spurs. Estimated exposure: roughly $10,000.
Freedman spent $5,000 on a Kalshi contract on “Knicks win Game 1.” With the Knicks at only 37% to win, that position would return roughly $8,000 if they won.
Knicks win: the bar covers tabs and takes a roughly $10,000 hit. The Kalshi contract pays back ~$8,000. Net: close to flat on the night
Knicks lose: no tabs, no promo cost. The $5,000 hedge expires worthless, booked as a marketing expense. Net: extra foot traffic, manageable cost.
Figures are stylised, based on public descriptions of the promotion and hedge, to illustrate the order of magnitude rather than exact dollar amounts.
The Knicks won 105-95. The hedge paid out. Freedman did not get rich off the trade; he basically broke even on a risky promotion and bought himself peace of mind for the night.
02 · Why do Prediction Markets Show up Here?
Neither Osasuna nor The Jeffrey invented the idea of insurance or hedging risk; they just plugged into a new piece of plumbing.
Sports insurance for binary outcomes has existed for decades.
Borussia Dortmund, for example, held a Champions League qualification policy from 2012 that paid out in 2015 when they finished seventh and missed European football entirely.
Klopp's last season in charge of Dortmund 2014/2015
So for a club like Osasuna, the route is well established: a broker like Howden structures the policy, a specialist market or Lloyd’s‑style syndicates absorb the risk, and the club pays a premium.
For a sports insurance broker like Game Point Capital, which issues hundreds of millions of dollars in sports coverage each year, this kind of relegation and performance risk is familiar territory; what’s new is that some of that risk can now be facilitated through event contracts on Kalshi instead of only traditional methods of reinsurance.
For a small business like The Jeffrey, the closest traditional product is prize indemnity insurance: you run a promo with a big prize, pay a relatively small premium, and an insurer takes the low‑probability, high‑cost payout if someone wins.
Large underwriters like Tokio Marine HCC or ESP Speciality know how to price and write this coverage. On paper, Freedman could have gone down that route.
So why could using a prediction market be better?
You need a broker and an underwriting process that cares about your specific risk.
Have a ticket size large enough to justify their time.
Have patience for the documentation, collateral, and days or weeks back and forth to process.
Most small businesses never hedge. Fewer than 10% of SMEs use any form of financial hedging, compared to 92% of Fortune 500 companies. The tools simply weren’t built with them in mind.
If you run a one‑night, $10,000 promo at a bar, traditional insurance or derivatives are the wrong fit. Too slow, too paperwork‑heavy, and too expensive relative to the risk.
Freedman, by contrast, placed his hedge on his phone before tip‑of.
For Osasuna, a one‑club relegation hedge is awkward to express in listed derivatives.
For The Jeffrey, a one‑night Knicks promo is too small and too weird for a standard policy.
Both, however, line up cleanly with a liquid yes/no market on the underlying event.
This is where prediction markets show up: they turn all of that bespoke, high‑friction structuring into a standardised contract that simply pays $1 if the event happens and $0 if it does not. In other words, they do not replace derivatives across the board; they start to fill the edges where the risk is binary, the size is awkward, and the old tools are clunky.
03 · Who Is Taking the Other Side?
Once you call something a hedge, the natural next question is: who is absorbing the risk?
On Kalshi, the answer is not “the house.” The platform is not the counterparty. It’s often the market participants on the institutional or retail side that are the participants where the prices are set by the market.
For Osasuna, Howden took on the insurance.
According to Semafor, their partners then used Game Point Capital and Greenlight Commodities to structure and place a position on Kalshi.
Susquehanna International Group was sitting at the other end.
When Osasuna survived, they pocketed over $1 million. That is the same logic a market maker applies to any trade: inventory a risk, earn a spread.
For The Jeffrey, the picture is simpler. On Kalshi, anyone can take the other side: a Knicks fan who wanted exposure, a trader who thought the market was mispricing the odds, or both.
Freedman’s hedge was matched against that pool. No intermediaries, no insurance chain.
Same structure. Very different counterparties.
The other side of these trades is also turning more institutional.
SIG was first, in April 2024.
Jump Trading followed in early 2026, taking equity stakes in both Kalshi and Polymarket in exchange for liquidity provision.
In April 2026, Jump provided the liquidity for Kalshi’s first block trade: a California carbon allowance contract, brokered by Greenlight Commodities for a Houston-based environmental fund.
Clear Street became the first institutional futures commission merchant on Kalshi.
Marex is building the infrastructure to route hedge fund flow to both platforms.
The next version of these trades might have a market with even more depth.
This is the important thing to understand about prediction markets as a structure. The same contract means something entirely different depending on who is holding it:
For Osasuna’s insurer, it was a hedge against a real financial obligation.
For The Jeffrey, it was insurance on a marketing promotion.
For SIG, it was a trading position with an expected value.
For a retail Knicks fan on Kalshi, it was speculation.
The instrument is identical but the motivation and the economics, are not.
The CFTC classifies these as commodity derivatives and frames them as tools for the public to “forecast, plan for, and hedge” future events.
State regulators, particularly around sports, often see the same product and call it gambling.
The law has not caught up to the reality that a single contract can be all four things at once.
04 · What Comes Next?
The Jeffrey example might be the easiest model to scale, but not the most important one.
Sports bars and football clubs make the mechanism legible, vs something abstract.
The deeper point is how event contracts impact Insurance as a category.
Traditional insurance works by building a statistical model of how often events happen, pricing a policy against that history, and paying out when a loss is verified.
That model works best when you have lots of similar risks and a long track record of claims to learn from.
A whole swath of risks do not look like that:
Novel risks or fast changing risks
One-off outcomes
Politically determined results
These do not fit cleanly into the traditional insurance machine. Insurers can write bespoke covers here, but they tend to be slow, judgment‑heavy, and hard to scale.
Prediction markets work differently. A contract trades at a price between $0 and $1. That price is the market’s implied probability. If a relegation contract trades at 30 cents, participants collectively believe there is a 30% chance of relegation.
The price aggregates information from everyone willing to stake money on their view. No actuary required.
That mechanism works for categories traditional insurance struggles with.
Which is: Institutions are already using Kalshi to take positions around CPI prints and Fed decisions, markets where the outcome is binary, dated, and economically significant.
The California carbon trade Jump brokered in April is the same logic applied to a government auction.
The category that opens up most is parametric insurance.
The standard insurance pays when you can prove a loss. Parametric pays automatically when a specific measurable trigger is hit, with no claims process.
E,g:
A farmer buys a policy that pays if rainfall drops below a threshold.
A shipping company buys one that pays if a port is closed for more than five days.
The payout is tied to the event, not the damage.
Prediction markets are a version of this, compressed into a contract with lower barriers to entry.
Any risk that is binary, dated, and publicly verifiable is a candidate.
A relegation battle (Osasuna) and a one‑night drinks promo ( The Jeffrey) are just two early case studies for what we might see more of if prediction markets are truly to be the next frontier of markets
Disclaimers
This newsletter is for informational purposes only and is not financial, business or legal advice. These are the author's thoughts & opinions and do not represent the opinions of any other person, business, entity or sponsor. Any companies, platforms, markets or projects mentioned are for illustrative purposes unless specified.
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