How Prediction Market Platforms Are Making Forecasting More Interactive

Traditional forecasting has long relied on a well-established expert-to-audience pipeline. However, in recent years a new business model has changed up the sector. One that might be more suited to the fast-paced, data-driven and highly interactive digital-first world of today.

Prediction markets have turned forecasting into a process anyone with an account (and money to put down) can participate in it, while attracting billions of dollars of financial interest at the same time. With top platforms Kalshi and Polymarket recently reaching a collective $50 billion of monthly trading volume in the US alone, these markets are already used by millions of people across the US.

But what does that all mean for forecasters? How accurate are prediction markets, and how have they changed the game of forecasting so much in just a few short years?

Live Probabilities Change the Game for Forecasting

In a traditional forecast, an expert will sit down and do the research to come up with a prediction. Then they will publish it to the world. From there that number becomes a static prediction attached to the expert, and it will be judged against the future outcome.

In real life though, circumstances change quickly. A published forecast could be outdated by new information within minutes, or someone could refute the prediction with a better theory.

At prediction markets, the price (and therefore the probability) are adjusted as the market opinion changes. This makes it a dynamic way of forecasting that is continually updated until the outcome is determined.

As well as making forecasting more interactive in individual cases, it has also opened up wide-scale forecasting to new areas of the economy. Experts in video game trends, cultural trends and financial markets can now easily put that knowledge to the test.

For example, creating a financial trade to make a profit on a prediction of a Federal Reserve interest rate cut could have once taken some serious financial know-how – but with a prediction market, anyone can get in on the market.

Wisdom of the Crowds and the Forecasting Process

The wisdom of the crowd is a well-known psychosocial phenomenon, and prediction markets say they have managed to successfully translate that to a modern digital platform. While prediction by online votes is nothing new, prediction markets argue that having people put money down behind their prediction makes the crowd prediction more accurate.

The theory goes that people will subvert traditional polling by trolling with ridiculous answers or even just feeling afraid to admit their true opinion – yet this isn’t true when money enters the equation.

Prediction markets have gone to some lengths to quantify their accuracy, as it is a key part of their appeal beyond being a wagering platform.

A Brier score is a measure of the accuracy of quantified probabilistic predictions. It is measured on a scale of 0 to 1, with zero being 100% accurate and 1 being completely inaccurate. According to Kalshi’s own research – so take that as you will – its contracts have an average Brier score of 0.08, narrowing to 0.02 at the close of a contract.

In contrast, an expert individual might expect to have a Brier score of 0.15 or even 0.2, while many people who claim to be expert forecasters are no better than random guessing (0.25 on the Brier score).

Although prediction markets claim their contract markets are very accurate, there has been limited large-scale, third-party research into the topic. While the accuracy may be up for debate, what isn’t is that prediction markets have turned forecasting into an interactive game of sorts.

Why Kalshi May Be Moving Away from Sports Contracts

Prediction markets borrow elements from a wide range of entertainment sources – sports betting, social media, live news. However, they also can claim to be real societal and financial tools through their uses in forecasting and increasingly institutional investment

While this argument worked with the federal Commodity Futures Trading Commission, and has clearly made prediction markets very popular with the public, it hasn’t escaped comparisons to sports betting.

That crossover is also part of what makes prediction markets more interactive than traditional forecasting. Instead of simply reading an expert prediction before a game or event, users can react to new information themselves, take a position and watch the market probability move as thousands of other traders do the same.

Various states have taken legal action against prediction markets, claiming they should be regulated under state, not federal, laws – and New Jersey’s case against Kalshi has now even gotten as far as the Supreme Court.

The majority of Kalshi’s revenues, and a significant amount for Polymarket, come from sports contracts. While sports forecasting is profitable and of interest to many people, it’s hard to argue that it is a public good in the way forecasting commodity prices or the weather are.

Polymarket Shows How Forecasting Can Become a Consumer Product

Polymarket takes the interactive side of forecasting a step further by presenting real-world questions as markets that users can trade rather than simply read about.

Instead of following a static prediction, users can take a position on an outcome, watch the market price move as new information arrives and change their position if their own view changes. That creates a much more active relationship with forecasting, particularly around politics, entertainment, technology and major news events.

The platform has also adopted many of the onboarding features people already associate with consumer-facing betting and trading products. Guides covering a Polymarket sign up bonus, for example, now sit alongside explanations of deposits, market mechanics and user experience. That overlap helps explain why prediction markets increasingly feel less like specialist forecasting tools and more like mainstream interactive platforms.

The important distinction is that the underlying product is still a market built around event outcomes. Users are not simply choosing a prediction to follow; they are participating in a live market where collective expectations are continuously reflected in the price.

Prediction Markets Have a Gender Imbalance – And They’re Trying to Fix It

On the subject of how prediction markets have changed forecasting, it is worth noting prediction markets’ gender imbalance. For companies that position themselves as useful tools for understanding market opinion, underserving roughly 50% of the population somewhat undermines those claims.

That matters for interactivity because prediction markets depend on participation. A broader mix of traders can bring different information, experiences and perspectives into the market, potentially changing how quickly prices react and which signals are reflected in the collective forecast.

Kalshi is a clear example, because it has apparently actively been trying to bring in more female traders. As well as recruiting female influencers, it has also ramped up the number of contract markets on cultural events, including reality TV, awards shows and fashion. A strategy that has worked out for them, with reports circulating that the percentage of females among all traders on the platform doubled from 13% to 26% over six months in 2026.

However, the platforms still heavily skew towards a young male audience. Which, you would think, should have some impact on the accuracy of certain markets. This makes a different business case for opening doors to women traders. Not only are they a new and largely untapped audience for prediction markets, but also one that can strengthen their claim to be accurate forecasting tools that use collective intelligence to predict a huge range of events.

Forecasting Is Becoming Something People Participate In

The biggest change prediction markets bring is not simply another way to make predictions. They turn forecasting from something people consume into something they can actively participate in.

Prices move as new information arrives, traders can challenge the prevailing view with their own money, and probabilities can change continuously rather than remaining fixed until the next expert forecast is published. Whether prediction markets ultimately prove more accurate across every type of event is still open to debate, but they have already made forecasting far more immediate, participatory and interactive.