7 Free Prediction Market Platforms Worth Trying

The best predictor of whether you'll be good at prediction markets is not your IQ, your education, or your subject-matter expertise. It's your calibration — how well the confidence levels you assign to predictions match reality. And the only way to build calibration is practice.

Free platforms let you build that skill without financial risk. They also let you discover whether you actually enjoy forecasting before you commit money to it. Some people find prediction markets addictive and rewarding. Others find them tedious. Better to figure that out with play money than with your savings account.

Research from the Good Judgment Project and others consistently shows that play-money and reputation-based forecasting platforms produce accuracy comparable to real-money markets — especially when the community is engaged and skilled. The money isn't what makes predictions accurate. The thinking is.

Metaculus is the platform that professional forecasters, policy researchers, and AI safety analysts actually use. It's been running since 2015, has resolved over 10,000 questions, and publishes detailed calibration data that proves its aggregate predictions are consistently well-calibrated.

The question quality is what sets Metaculus apart. Each question has precise resolution criteria (exactly what counts as "yes"), background context with sources, and community discussion. Compare that to a typical Polymarket contract, which might be a one-liner. Metaculus questions read like mini research briefs.

The scoring system uses a proper scoring rule — you earn the most points by honestly reporting your true probability estimate. Hedging or gaming the system is mathematically disadvantaged. Over time, your track record builds into a calibration curve that shows exactly how accurate you are at each confidence level.

Top Metaculus forecasters have been cited in Congressional testimony, consulted by intelligence agencies, and published in academic journals. The platform runs periodic forecasting tournaments with cash prizes — typically $5,000-$25,000 prize pools — that attract top talent.

The community skews technical. Expect discussions about Bayesian reasoning, base rates, and reference class forecasting. If that sounds intimidating, don't worry — lurking and reading other forecasters' reasoning is one of the best ways to improve.

Best for: People who want to get measurably better at thinking about the future. Especially strong for AI, science, and geopolitics questions.

Manifold is the social media of prediction markets. Anyone can create a market on anything. Will your city council approve the new zoning proposal? Will the next iPhone have USB-C? Will your coworker quit before July? If you can phrase it as a yes/no question, you can make a Manifold market for it.

The platform uses a play-money currency called Mana (M$). You start with M$1,000 and earn more through accurate predictions, daily bonuses, and referrals. Mana can't be converted to cash, but you can donate earned Mana to real charities — Manifold partners with GiveDirectly, the Against Malaria Foundation, and other effective altruism organizations. As of 2026, users have directed over $1 million in charitable donations through the platform.

The user-created market model means breadth is unlimited. Over 500,000 markets have been created on Manifold. The tradeoff: quality varies wildly. Some markets have detailed resolution criteria and hundreds of active traders. Others are inside jokes between three friends. The platform's reputation and ranking systems help surface the good stuff, but it takes time to learn which market creators are reliable.

Manifold's social features — following forecasters, commenting, reacting, leaderboards — make forecasting feel like a game rather than homework. That's a feature, not a bug. The best way to build forecasting habits is to make them enjoyable.

Best for: People who want variety, social interaction, and low-stakes fun. Also excellent for creating custom markets on topics no other platform covers.

Good Judgment Open is the public-facing arm of the Good Judgment Project, the research initiative that produced Philip Tetlock's "superforecasters." The project famously demonstrated that a small group of talented amateurs could consistently outperform intelligence analysts with access to classified information.

The question selection is curated by a professional editorial team with deep expertise in geopolitics, international security, and economics. Expect questions like "Will Iran reach a nuclear agreement by Q3 2026?" or "Will the EU impose new sanctions on Country X by year-end?" These are the kinds of questions that governments and intelligence agencies actually care about.

Your accuracy is tracked over time and compared against other forecasters. Top performers can be invited to join the paid Good Judgment Superforecasting service, where corporate and government clients pay for expert consensus forecasts. It's one of the few pathways from "forecasting hobbyist" to "forecasting professional."

The community is smaller than Metaculus or Manifold but exceptionally skilled. The average Good Judgment Open forecaster is better-calibrated than the average forecaster on most other platforms, partly because the community self-selects for people who take accuracy seriously.

The interface is dated — functional but not pretty. Question pages provide resolution criteria and a timeline, but the presentation is more academic than consumer-friendly.

Best for: Geopolitics nerds, aspiring superforecasters, and anyone who wants to compare their accuracy against some of the best forecasters in the world.

INFER is a forecasting platform run by the Applied Research Laboratory for Intelligence and Security (ARLIS) at the University of Maryland. Formerly known as CSET Foretell (when it was housed at Georgetown's Center for Security and Emerging Technology), INFER focuses on questions relevant to US national security, technology policy, and global development.

The platform is funded by government research grants and serves a dual purpose: it provides useful forecasts to policymakers and generates data for academic research on forecasting methods. When you make predictions on INFER, your forecasts may be aggregated into reports read by people in government agencies.

Question topics include AI governance, cybersecurity threats, international conflicts, public health, and emerging technology adoption. The questions are carefully constructed with precise resolution criteria and time horizons. INFER staff often provide background briefings and relevant research links to help forecasters make informed predictions.

INFER runs periodic tournaments with cash prizes (typically $500-$2,000 for top performers). Your track record is visible and comparable against the community, which includes academic researchers, policy analysts, and graduate students.

The platform is smaller and less well-known than Metaculus or Good Judgment Open, but the question quality is excellent and the policy impact is real. If you care about how forecasting can improve public decision-making, INFER is the most direct connection between your predictions and actual policy discussions.

Best for: Policy wonks, security researchers, and anyone who wants their forecasts to potentially influence real-world decisions.

You can't trade on Polymarket from the US, but you can still watch. The platform's market pages are publicly visible without an account. You can see current prices (implied probabilities), trading volume, price history charts, and community comments.

This is more useful than it sounds. Polymarket's prices represent the consensus of thousands of traders with real money at stake. If you want to know what the market thinks about an upcoming election, a Fed decision, or an AI milestone, Polymarket's prices are among the best real-time signals available.

You can use Polymarket as a research tool: check the market price, compare it against your own analysis, and track whether your judgment was better or worse than the market over time. Some Metaculus and Good Judgment forecasters do exactly this — they use Polymarket prices as one input among many when forming their own probability estimates.

No account needed. No sign-up. Just navigate to polymarket.com and browse. The mobile PWA works well for quick price checks.

Best for: US users who want to see prediction market prices without trading. Also useful as a complement to any forecasting platform — use Polymarket for the market price, then form your own view.

Almanis is a European forecasting platform that combines features of Metaculus (reputation-based scoring, detailed questions) with a broader focus on science, technology, and European affairs. The platform is based in Switzerland and has been growing steadily since its launch in 2019.

Questions cover climate science, European politics, technology adoption, public health, and economic indicators. The resolution criteria are detailed and the community, while smaller than Metaculus, includes researchers from European universities and think tanks.

The scoring system is similar to Metaculus's — a proper scoring rule that rewards well-calibrated probability estimates. Your performance is tracked over time and visible on public leaderboards.

What makes Almanis worth trying is the question selection. The European focus means you'll find questions about EU policy, ECB decisions, European elections, and continental science projects that other platforms undercover. If your knowledge advantage is in European affairs, this is where that advantage has the most value.

The interface is clean and modern — better-looking than Metaculus, though with fewer features. Mobile support is decent through the responsive web design.

Best for: European users who want regionally relevant questions, and anyone interested in science and technology forecasting with a European perspective.

Fatebook takes a different approach entirely. Instead of predicting global events, you predict things in your own life. "Will I finish this project by Friday?" "Will this job candidate accept our offer?" "Will it rain on Saturday?"

The concept is simple: make a prediction, assign a probability, and resolve it when the answer becomes clear. Over time, you build a calibration curve for your personal predictions. Most people discover they're systematically overconfident — they say 90% when the true probability is closer to 70%. Fatebook makes that bias visible.

The platform was created by Ozzie Gooen and is connected to the effective altruism and rationality communities. It's minimalist by design — no gamification, no leaderboards, no social features. Just you, your predictions, and your track record.

You can share predictions with friends or colleagues, creating a lightweight accountability mechanism. "I said there was an 80% chance we'd close this deal by March — let's check." It's surprisingly useful in professional contexts where people make confident statements about uncertain outcomes.

Fatebook is free, requires only a Google login, and works well on mobile. There's no community of strangers to forecast with — it's a personal tool.

Best for: Building personal calibration awareness. Also useful for teams that want to make their internal predictions explicit and trackable.

The intuition says yes — people should forecast more carefully when money is on the line. The research says: it's more complicated than that.

The Good Judgment Project compared play-money and real-money forecasting environments and found that the accuracy difference was small — much smaller than the difference between skilled and unskilled forecasters on either type of platform. In other words, who is forecasting matters more than whether money is involved.

Metaculus's published calibration data shows accuracy comparable to or better than many real-money prediction markets. Manifold's play-money markets on high-profile questions (elections, AI milestones) have tracked Polymarket's real-money prices closely.

Where real money does help: it filters out casual or unmotivated participants. On a play-money platform, someone might forecast 50% on everything just to fill out their profile. On a real-money platform, that strategy loses money. This selection effect means that the average trade on Polymarket or Kalshi carries more information than the average prediction on Manifold.

But on free platforms with strong communities — Metaculus, Good Judgment Open — the reputational incentive is powerful enough to achieve similar filtering. Top Metaculus forecasters care about their accuracy scores the way traders care about their P&L.

The bottom line: start with free platforms. Build your skills. If you're consistently well-calibrated and want to put money behind your predictions, real-money platforms will be there. But you don't need to spend a dollar to become a good forecaster.