Prediction markets have spent years on the edge of betting and finance, but the idea is becoming harder to ignore as users grow more familiar with trading-style products and event-based wagering gains momentum.
In this interview with Yogonet,Slotegrator Product Owner Maksym Shtun explains how prediction markets work, how they differ from traditional fixed-odds betting, why the peer-to-peer model changes the relationship between users and platforms, and where the format could move next across sports, politics, entertainment, advertising, and corporate forecasting.
How would you explain prediction markets to someone who has never heard of them before?
A prediction market is a platform where people trade on the outcomes of future events. Instead of just placing a bet and waiting, you’re buying a “share” in an outcome — and that share has a price that moves based on what the crowd collectively believes will happen.
If you think an outcome is more likely than the current price suggests, you buy. If you think it’s overpriced, you sell. It’s the wisdom of crowds turned into a financial instrument.
How do prediction markets differ from traditional sports betting?
Traditional sports betting puts the player against the bookmaker. The bookmaker sets the odds, builds in a margin, and profits regardless of outcomes over time. In a prediction market, the player trades against other participants, so the market finds its own price through supply and demand.
There’s no house edge built into the structure. The platform facilitates the exchange rather than betting against the player. That’s a fundamentally different relationship between the platform and the user.
Are prediction markets more accurate than expert forecasting? What does the research show?
The research broadly says yes, with caveats. The Iowa Electronic Markets, running since 1988, have consistently outperformed polls in election forecasting, and Polymarket called 49 of 50 states in the 2024 US presidential election while polls missed swing states by 3–5 points.
The mechanism is straightforward: real money filters out noise. Research from Brookings shows prediction markets quickly incorporate new information and generally exhibit lower statistical errors than professional forecasters and polls.
But the edge is conditional. Inaccuracies were especially prevalent during Brexit and the 2016 US election, and they tend to underperform on long-range or politically polarized questions. Liquidity matters enormously — a thin market is just a few people’s opinions with money attached.
So the honest answer: prediction markets are a genuinely superior forecasting tool for near-term, high-volume, well-defined events. They’re not an oracle, but they’re certainly a better-than-average signal.
What’s the fundamental difference between a P2P exchange model and a traditional fixed-odds bookmaker?
In fixed-odds betting, the bookmaker sets the price and the bettor either takes it or leaves it. Their margin — the overround — is baked into every bet. A typical high-street bookmaker runs 5 – 10% overround on football, sometimes much higher on niche markets.
The average bettor has no idea how much they’re paying — you need patience, awareness, and a calculator to even figure out the margin. Experienced bettors, on the other hand, are fully aware that the odds are invisibly tilted against them, even if they don’t bother figuring out exactly how much it is.
In a peer-to-peer exchange, however, one user offers odds and another accepts them. The platform just matches those two sides and takes a small commission — usually 2–5%, on winnings only. The margin shrinks dramatically because nobody is pricing in house risk. Instead of betting against the house, players bet against each other; the “house” just facilitates.
Also, in a traditional bookmaker, players can’t verify that the house actually has the liquid funds necessary to cover a substantial bet. In a P2P exchange, the liquidity is other users — people who disagree with you on the outcome. And liquidity becomes something you can actually feel: if nobody else is interested in your market, you can’t get matched. That’s actually healthy information. A liquid market means many informed people are pricing the same event. A thin market is a warning sign.
For sophisticated bettors, the difference in value over time is enormous — not just in the margin, but in what the model tells you about the quality of the price you’re getting.
In a P2P model, who sets the odds — and what does that mean for the bettor?
The participants do. Anyone can post an offer — essentially saying “I’ll back this outcome at these odds.” Someone else can either accept that offer or post a counter.
The market price emerges from the aggregation of all these individual decisions. It’s the same principle as a financial exchange. When millions of people are trading, the resulting price tends to be more accurate than any single expert’s estimate — that’s the core insight behind prediction markets
How does removing the bookmaker’s margin change the experience for end users?
It changes the economics fundamentally. In traditional fixed-odds betting, the overround — typically 5% to 10% depending on the market — is a constant drag on returns. Over time, that margin makes it nearly impossible for even skilled bettors to profit.
In a P2P exchange, the margin collapses to a small commission on matched bets. That really matters to players; psychologically, it feels completely different. It’s the same mental model as a bank or a brokerage: a clean, transparent fee for executing and settling a transaction. You know exactly what you’re paying and why.
That changes the relationship entirely. Traditional bookmakers operate by working an edge into the odds to ensure they profit. In a P2P model, the operator has no reason to tilt the odds in any direction. They make money on volume and activity, not on outcomes. Whether you win or lose is irrelevant to them. That alignment removes the adversarial dynamic completely.
The result is an environment where users feel comfortable. They’re not fighting against the house — they’re using infrastructure, the same way you use a stock exchange. You pay a fair fee for a fair service. That trust is actually the product. For people who’ve always been unhappy about the overround — and many experienced bettors are — that shift from adversarial to transactional is what makes P2P feel fundamentally different.
How do prediction markets resolve outcomes — and what happens when a result is disputed?
Outcome resolution is one of the most critical design challenges. You need a resolution mechanism that is trustworthy, transparent, and resistant to manipulation. The most robust approaches use either verified external data sources — official results feeds, regulatory records — or decentralized oracle systems where multiple independent parties confirm the outcome.
Dispute resolution protocols need to be defined upfront and visible to all participants before they enter the market. Ambiguity at resolution is where trust gets destroyed.
Why are prediction markets gaining traction now, after years of being a niche concept?
A few things converged. Regulatory environments in several key markets have opened up. Technology has made it possible to build genuinely liquid, low-friction exchanges at scale — the infrastructure problem is largely solved. And culturally, there’s a generation of users who are comfortable with financial instruments, with crypto, with the idea of trading rather than just betting.
Prediction markets sit at the intersection of those trends. The timing is right in a way it wasn’t ten years ago.
What sectors beyond sports could prediction markets disrupt?
The potential is broad. Political and electoral markets are the obvious ones — we already see strong interest there. Financial event markets are interesting, though they overlap with regulated derivatives territory. Corporate prediction markets — used internally by companies to forecast product timelines, sales figures, project risks — have been quietly gaining adoption for years. Google, HP, and Microsoft have all run internal prediction markets for exactly this reason.
But one angle that doesn’t get enough attention is prediction markets as an engagement and advertising tool. The mechanic already exists — anyone who’s watched a stream on Twitch or Kick knows that audiences love predicting what happens next. Streamers already ask their communities to call outcomes using channel points. It’s massively engaging. The step to real-money micro-predictions is not that large, and the user appetite is clearly there.
Imagine a brand running a campaign where instead of a passive ad, users stake a few dollars on an outcome tied to a product launch, a sports moment, or a live event. The engagement is completely different — you have skin in the game, you’re watching, you’re invested. It turns advertising from something people skip into something people opt into.
Anywhere there’s uncertainty, a crowd willing to put something on the line, and a verifiable outcome, you have the ingredients for a prediction market. Entertainment, culture, live content — these aren’t fringe use cases. They might end up being the ones that bring prediction markets to a genuinely mass audience, because the barrier is low and the fun is immediately obvious. A few bucks and something to root for — that’s a product people already understand
Where do you see prediction markets in five years?
Mainstream. And honestly, the numbers already make that prediction for me, so I can’t really argue against it. Look at what’s happened in just the last 18 months. Kalshi secured $1 billion at an $11 billion valuation — its third round of fundraising in a single year, more than doubling its valuation in just a few months.
Polymarket received a $2 billion strategic investment from ICE, the NYSE’s parent company, at an $8 billion pre-money valuation. That’s a signal. And the traditional players are feeling it. Classic bookmakers can already tell they’re losing players to prediction markets.
The P2P exchange model specifically will become dominant in regulated event-based wagering, for the same reason exchange-traded products took share from OTC products in finance — better pricing, more transparency, fairer for participants. The platforms that lead will be the ones investing now in liquidity, user experience, and regulatory relationships. Prediction markets are no longer just a novelty or niche interest.
Original article: https://www.yogonet.com/international/news/2026/04/30/118812-slotegrator-on-prediction-markets-34the-trust-is-the-product-34
