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Polymarket’s morning-of-election favorite won 238 of 273 sampled 2026 primaries, an 87% hit rate. But Bitquery Research’s audit reports that a hypothetical dollar on every favorite would have lost four cents per dollar. The contrast is the point: favorites can win frequently and still be priced too expensively to make backing all of them profitable.
How accurate was Polymarket in the 2026 primaries?
In an audit published October 9, 2026, Bitquery Research found that the candidate priced as the favorite on voting morning won 238 of 273 sampled Senate, House, and gubernatorial primaries—87%. The audit’s headline return for backing every one of those favorites was negative four cents per dollar. These are Bitquery’s calculations, not independently revalidated here. Read the Bitquery audit.
“Accuracy” can mean different things. The 87% figure answers how often the favorite won. It does not show whether the prices were good value. For that, the implied probability represented by the price needs to be compared with how often candidates at that price actually won—and with the hypothetical payout from the bets.
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The biggest calibration problem was among favorites priced from 50 to 90 cents. They won 71% of their races, even though their average price was 77 cents. In other words, this group won less often than its average price implied. Bitquery reports that the gap accounts for the loss across all favorites.
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By contrast, favorites priced at 90 cents or more won 177 of 182 races. A high win rate in that band still does not make every wager profitable: the price paid matters, and a rare loss can outweigh many small wins when a contract is bought at a very high price. The audit’s figures are historical results for its sample, not a promise about future markets.
What the audit measured—and what it left out
Bitquery compared each candidate’s market price with the primary result. Its voting-morning price was the average of trades during the 24 hours before 12:00 UTC on voting day. If there was no trade in that window, it used the last trade in the preceding 30 days. Runoffs were scored on their runoff date.
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- Races: Senate, House, and gubernatorial primaries in the 50 states. Special elections and territories were excluded.
- Sample: 273 races were scored. Nine markets were omitted because they settled before voting, had no usable voting-morning price, or had no settlement record.
- Market data: Polygon trades only. The audit does not include Polymarket’s US app or other platforms with off-chain books.
- Period: Data collection begins September 1, 2025, although some markets traded earlier. The reported $36.3 million in traded volume counts activity from that start date.
- Outcome: The study tracked who won the primary, not necessarily who ultimately appeared on the general-election ballot. Bitquery warns that primary-market results may not carry over to general elections.
The audit was reported by Gaurav Agarwal for Bitquery Research. Its measurements were made October 9, 2026, using Bitquery Polygon data through October 8, 2026. A wallet in this dataset is a blockchain address; it should not automatically be treated as one individual.
What individual races reveal about the limits of favorites
Wisconsin: a 96-cent favorite lost
In Wisconsin’s Democratic gubernatorial primary, Francesca Hong was priced at 96 cents on voting morning, but David Crowley won by less than a point. Bitquery says $1.6 million was traded in the market, making it the audit’s costliest miss by traded dollars. Hong’s daily average price had not fallen below 89 cents during the final week. This is a striking example of a high-confidence favorite losing, not evidence that such outcomes are common.
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New Jersey: a very unlikely candidate won
Justin Murphy won New Jersey’s Republican Senate primary despite being priced at 0.4 cents on voting morning; favorite Alex Zdan was at 79 cents. An outcome priced as highly unlikely can happen, but one race cannot establish how well the market generally prices long shots.
Texas: a favorite’s price shifted after an endorsement
In the Texas Republican Senate race, favorite Ken Paxton’s price rose from 78 cents to 94 cents after Donald Trump endorsed him. Paxton then won the runoff by 28 points. This sequence describes one market and result; it does not prove that the endorsement caused the price move or the win.
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How much should readers infer from the results?
The audit’s summary—“Quite accurate, but too sure of its favorites”—captures the difference between predicting a winner and pricing a bet. Its results suggest that the favorite often won in this sample, while the market’s confidence, especially in the 50-to-90-cent band, was not matched by realized win frequency.
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Timing also matters. In 15 races, the favorite changed during the final week: the new favorite won seven, the previous favorite won five, and another candidate won three. That is a small, specific slice of the audit, not a general rule for when to trade or whether late market moves are predictive.
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These findings do not establish how Polymarket performs in general elections, on its US app, or across all prediction markets. Primary contests are local, the data covers a defined set of Polygon trades, and a past hit rate is not a trading recommendation.
How to inspect or reproduce the analysis
Bitquery says readers can inspect Polymarket data through its API or obtain full trade-history files from the Bitquery Data Store. Reproducing the headline result requires matching the audit’s scope and price rule: include the specified state primaries, calculate the price using its voting-morning window (or fallback), and compare each favorite with the settled primary winner. A different data source, cutoff, or included race set may produce a different result.
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