7 Most Accurate Prediction Markets and Why They Work

Prediction markets have one advantage over pundits, polls, and expert panels: you can grade them. Every contract resolves to $1 or $0. Every forecast has a timestamped probability. You can calculate calibration curves, Brier scores, and track records with the same rigor you'd apply to a batting average.

We did. Here are the 7 platforms with the strongest accuracy records, measured by real outcomes on events with real stakes.

One caveat upfront: accuracy depends on the question type. A platform might nail 90% of election outcomes and completely whiff on pandemic timelines. We'll note where each platform excels and where it falls short.

On the morning of November 5, 2024, the FiveThirtyEight polling average had the presidential race as a near-toss-up: Harris 48.1%, Trump 46.8%. Most forecasting models gave Harris a slight edge — Nate Silver's model had her at 52% win probability.

Polymarket told a different story. Trump contracts were trading at $0.62 — implying a 62% probability of a Trump victory. That price had been climbing steadily since mid-October, driven by a combination of early voting data, polling trends in swing states, and what appeared to be a large, well-informed whale trader (later identified by the Wall Street Journal as a French national named Theo) who wagered over $30 million on Trump.

Trump won with 312 electoral votes. Polymarket was closer to the outcome than every major poll and every major forecasting model.

This wasn't a one-off. On the 2024 Republican primary, Polymarket had DeSantis fading months before polls caught up. On the Biden withdrawal, Polymarket contracts on "Will Biden be the Democratic nominee?" dropped below 50% in late June 2024 — three weeks before Biden actually withdrew on July 21.

Where Polymarket gets it wrong: low-liquidity markets. On questions with less than $100,000 in volume, prices can be moved by a single motivated trader and don't reflect true crowd wisdom. Polymarket's accuracy advantage exists primarily on high-volume markets where many informed participants are actively trading.

In March 2020, the consensus expert view was that a COVID-19 vaccine would take 12-18 months at minimum, with many virologists publicly stating that timelines under 18 months were "dangerously optimistic." Anthony Fauci hedged with "12-18 months." The WHO said 18 months.

Metaculus community forecasters were more aggressive. By April 2020, the Metaculus median for "When will a COVID-19 vaccine receive EUA in the US?" was centered on December 2020 — roughly 9 months out. This was based on a detailed analysis of the mRNA platform's speed advantage, Operation Warp Speed's parallel trial design, and historical base rates for vaccine development when regulatory urgency was maximal.

The Pfizer-BioNTech vaccine received FDA Emergency Use Authorization on December 11, 2020. Metaculus was within two weeks of the median forecast, submitted 8 months in advance.

Metaculus's edge on scientific and technical questions comes from its community composition. A disproportionate share of active Metaculus forecasters have STEM backgrounds. On AI capability questions — "When will an AI system achieve X benchmark?" — Metaculus community forecasts have outperformed both expert surveys and market prices, partly because the forecasters are often ML researchers themselves.

The calibration data backs this up. Metaculus publishes its community calibration curve publicly. When the community median says 80%, the event happens approximately 80% of the time. That level of calibration is rare outside of professional superforecaster teams.

Where Kalshi punches above its weight class is economic and financial event contracts — specifically Federal Reserve interest rate decisions. Kalshi launched Fed rate contracts in mid-2023, and their track record since then is near-perfect.

Here's how near-perfect: across the 16 FOMC meetings from July 2023 through January 2026, Kalshi contracts correctly predicted the direction of the Fed's decision (hold, cut, or hike) 16 out of 16 times when measured by the contract trading above $0.50 the day before the announcement. On 14 of those 16 meetings, Kalshi contracts were within 3 percentage points of the CME FedWatch tool's implied probability, suggesting that Kalshi's smaller market is efficiently incorporating the same information as the much larger fed funds futures market.

This isn't surprising. Fed decisions are driven by publicly available data (CPI, employment, Fed governor speeches) that's analyzed by thousands of professionals. The "crowd" trading Kalshi Fed contracts includes fixed-income traders, economists, and finance hobbyists who collectively possess most of the relevant information. When information is widely available, prediction markets tend to converge quickly on accurate prices.

Kalshi's accuracy drops on questions where information is private or where outcomes depend on small-group decision-making (e.g., Supreme Court rulings, cabinet appointments). These markets tend to be thin and volatile, with prices swinging 20+ cents on rumors. The signal-to-noise ratio is lower.

PredictIt's contribution to accuracy research is its longevity. Operating since 2014, PredictIt has generated over a decade of resolved political contracts covering hundreds of elections at the federal, state, and local level.

An analysis by Rothschild and Sethi (2016, updated 2020) found that PredictIt's final prices (the last trade before an event resolves) were better calibrated than RealClearPolitics polling averages in 78% of head-to-head comparisons for US House and Senate races. The effect was strongest in low-profile races where polling was sparse or non-existent — PredictIt's traders were incorporating local information that national polling firms didn't capture.

The platform's accuracy has likely degraded since 2022, when the CFTC's shutdown order scared away institutional-quality traders. Volume dropped roughly 60% from 2022 to 2024. Thinner markets mean less information aggregation, which means less accurate prices. But for the 2014-2022 period, PredictIt's track record is one of the strongest empirical cases for prediction market accuracy in US politics.

The 5,000-trader cap per market is actually an interesting natural experiment. It suggests that you don't need millions of participants for accurate prices — you need a few thousand engaged, motivated traders who collectively possess diverse information. This has implications for corporate and government applications of prediction markets.

The Good Judgment Project (GJP) was born from a tournament run by IARPA — the intelligence community's version of DARPA — from 2011 to 2015. Five research teams competed to develop the most accurate geopolitical forecasting methods. Philip Tetlock's team at the University of Pennsylvania won, and it wasn't close.

The headline stat: GJP's top forecasters (the "Superforecasters") outperformed professional intelligence analysts with access to classified information by 30% as measured by Brier scores. Let that sink in. People reading the same newspapers you read, with no security clearance and no inside information, produced more accurate probability estimates on geopolitical questions than CIA analysts.

How? Tetlock identified the traits that distinguished Superforecasters: they updated beliefs frequently based on new evidence, used base rates as starting points, broke complex questions into sub-components, averaged multiple mental models, and — critically — they were actively open-minded. They treated their own beliefs as hypotheses to test, not positions to defend.

Good Judgment Inc., the commercial spinoff, now sells forecasting services to government agencies, hedge funds, and corporations. The public platform (Good Judgment Open) lets anyone participate and track their accuracy against this benchmark. It's the closest thing to a prediction market accuracy gold standard that exists.

The IEM's accuracy case rests on a single landmark study and decades of confirming data. Berg, Nelson, and Rietz (2008, building on their 2004 analysis) compared IEM election-eve prices to 964 contemporaneous polls across elections from 1988 to 2004. The IEM price was closer to the actual vote share than the poll 74% of the time.

What makes this finding durable is the sample size. This isn't based on one or two elections. It spans five presidential cycles and hundreds of individual poll-vs-market comparisons. The effect is consistent across close races and blowouts, primary and general elections.

The IEM also demonstrated an important finding about temporal accuracy: market prices become more accurate as the event approaches, but they're also more accurate than polls even months in advance. Six months before an election, IEM prices outperformed contemporaneous polls roughly 60% of the time. One week before, that jumped to 80%.

The limitation is that the IEM only covers US elections and a handful of economic indicators. You can't extrapolate its accuracy to other domains. And with $500 deposit caps and negligible volume by modern standards, the IEM's current prices are less informative than Polymarket or Kalshi for any given election.

Betfair's accuracy record is complicated by one very public failure: Brexit. On referendum day, June 23, 2016, Betfair's implied probability for Remain peaked at 94% based on early results from Gibraltar and a handful of initial counts. Even before polls closed, Remain was trading at 85%+. Leave won 51.9% to 48.1%.

But context matters. Betfair wasn't uniquely wrong — every prediction market, every poll, every forecasting model had Remain favored. And Betfair's pre-campaign odds (before the final week) were closer to the outcome than polling averages. The late swing toward Remain in Betfair's prices was driven by a combination of thin overnight liquidity and a small number of large Remain bets that moved the market disproportionately.

Outside of Brexit, Betfair's political accuracy record is strong. On UK general elections, Betfair seat-by-seat markets have outperformed exit polls for individual constituency results. On the 2020 US election, Betfair had Biden at roughly 65% on election day, in line with most forecasting models and more accurate than polls that underestimated Trump's support in key states.

Betfair's deepest accuracy advantage is in sports, where their massive liquidity (GBP 7+ billion in annual sports volume) produces prices that professional bookmakers use as benchmarks. When Betfair prices diverge from bookmaker odds, it's usually the bookmakers who adjust.

Three mechanisms drive prediction market accuracy:

Skin in the game. When you have money on the line, you stop performing for an audience. Poll respondents face no penalty for expressing aspirational preferences or tribal loyalty. Traders face a direct financial cost for being wrong. This filters out cheerleading and amplifies genuine belief.

Continuous updating. A poll is a snapshot. It captures opinion at a single moment and doesn't update until the next poll. Markets update in real-time as new information arrives. A jobs report, a debate performance, an October surprise — the market price moves within minutes. Polls take days to reflect new information (if they do at all).

Diverse information aggregation. Markets aggregate information from participants with different expertise, different information sources, and different analytical frameworks. A political operative, an economist, a sports statistician, and a local activist might all trade the same contract, each bringing different pieces of the puzzle. The market price synthesizes information that no single participant possesses.

This is the Hayek argument, applied to forecasting. Prices aggregate dispersed knowledge more efficiently than any centralized mechanism, whether that mechanism is a polling firm, an expert panel, or an intelligence agency.

Markets fail in predictable ways:

Thin markets. When only 50 people are trading a contract, the price reflects the information of 50 people, not the wisdom of crowds. Most prediction market failures involve low-volume markets where a single trader with strong views (or a manipulative intent) can move prices far from fair value.

Correlated information. Markets work best when participants have diverse, independent information. They fail when everyone is reading the same sources. In the 2016 Brexit vote, most traders were consuming the same UK media, the same polling aggregates, and the same expert commentary. The result was a correlated error — everyone was wrong in the same direction.

Regulatory constraints. PredictIt's $850 position cap means that even if a trader has strong private information, they can't push the price far enough to reflect it. A trader who is 95% confident in an outcome can only risk $850, while on Polymarket they could risk $100,000. Regulatory caps artificially limit information incorporation.

Long time horizons. Markets are less accurate on questions that resolve years in the future. The discount rate is too high, information is too uncertain, and liquidity tends to evaporate on long-dated contracts. Metaculus handles long-horizon questions better because there's no capital tied up.