Are Prediction Markets Accurate? What the Data Shows

Short version: prediction markets are pretty accurate, more often than not, and they're usually better than pundits — but they are absolutely not infallible, and "accuracy" is a trickier word than it sounds. Here's the longer version, in plain English.

Two ways to be "accurate"

When people say a prediction market was right or wrong, they usually mean one of two things:

Hit rate. If the market priced a contract above fifty cents and the YES ended up happening, that's a hit. Below fifty cents and it didn't happen, that's also a hit. Hit rate is "how often did the favored side win?"
Calibration. If the market said something was a seventy-percent chance, did it actually happen about seventy percent of the time across many such markets? Calibration is the harder, fairer test — it grades not just the direction but the confidence.

The standard calibration metric is the Brier score. Lower is better. A perfect forecaster scores zero. Always guessing fifty-fifty scores 0.25. Real prediction markets across politics, sports, and weather typically land somewhere in the 0.10 to 0.20 range, which is genuinely good — and a lot harder to fake than hit rate.

What the research actually says

Decades of academic work — from the Iowa Electronic Markets in the 1980s through Hollywood Stock Exchange box-office contracts, to the political contracts on PredictIt and now Kalshi — keep finding the same broad pattern: prediction markets are competitive with the best statistical models, and they reliably beat individual experts and most pundits. They're not magic, but they aggregate a lot of information into one number, fast.

Where they really shine is on questions with clear, public, time-stamped data: elections, macro releases, weather, sports. Where they struggle is on thin, illiquid contracts where one loud trader can move the price; on questions whose resolution rule is fuzzy; and on once-in-a-decade events where there's no base rate to lean on.

When markets miss

A few patterns show up over and over when markets get things wrong:

Recency bias. One loud headline drags the price further than the data justifies.
Thin liquidity. Quiet contracts move on small flows. Wide spreads are noise, not signal.
Resolution ambiguity. The contract settles on a specific source or window the crowd is mis-reading.
Base-rate neglect. The crowd anchors on the current story and forgets how often the thing actually happens.

Bubba's Edge is built to look in exactly those spots — where the news in the price and the data in the price aren't lining up. More on how Bubba scores it.

How to judge a forecaster's accuracy (including Bubba's)

Don't trust hit rate alone. Anyone can win 80% of the time by only picking the lopsided markets. The real questions are: how many calls? Across how many categories? What's the Brier score? And — crucially — was the call snapshotted at entry, before the outcome was known, with no cherry-picking?

Bubba's answer to that is the public ledger and the public stats page. One official Bubba estimate per market, snapshotted at entry, graded against the real Kalshi settlement, with hit rate and Brier broken out by category and updated live. If the method doesn't work, the numbers will say so.

The honest bottom line

Prediction markets are one of the better tools we have for pricing uncertain real-world events. They beat most experts most of the time. They are not oracles, and the ones with the cleanest data tend to be the most accurate. Treat any single price as a calibrated guess, not a fact, and you'll be using them the way they actually work.

Information only — not financial advice. Past accuracy does not guarantee future accuracy.