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Backing Polymarket’s 2026 Primary Favorites Would Have Lost 4%, Audit Finds

Bitquery Research found that Polymarket favorites won 238 of 273 sampled 2026 primaries, but a voting-morning strategy backing them all would have lost 4 cents per dollar.
From TheFinanceBase Team5 min to read
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Backing every Polymarket favorite on voting morning across a sample of 2026 Senate, House and gubernatorial primaries would have lost about 4 cents per dollar, even though those favorites won 238 of 273 races, or 87%. The contrast is the point: a prediction can be right often and still be a poor bet if its price is too high. The figures are Bitquery Research’s calculations from Polygon trades, measured October 9, 2026 against data through October 8; they do not cover Polymarket’s US app or establish what will happen in future races.

What the 2026 audit found

Bitquery Research’s audit, published October 9, 2026, examined primary markets for Senate, House and gubernatorial races in the 50 states. It reports that the candidate priced as the favorite on voting morning won 238 of 273 scored races, an 87% hit rate. But a hypothetical strategy of backing each favorite at the measured price returned a loss of four cents per dollar.

Those are different measures. The win rate asks how often the most likely candidate won. The return also depends on what a bettor paid: a favorite can win frequently yet be overpriced relative to its chance of winning. The audit’s overall figures are specific to its sample, pricing method and Polygon data, not an independently validated forecast or trading recommendation. Read Bitquery Research’s audit and methodology.

Why an 87% win rate still produced a loss

The mismatch was concentrated in the middle of the price range. Bitquery reports that favorites priced from 50 to 90 cents won 71% of their races, while their average price was 77 cents. In other words, the group won less often than its average price implied. That difference can outweigh wins elsewhere when calculating the return from buying every favorite.

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At the high end, favorites priced at 90 cents or more won 177 of 182 races. That is a very strong historical record, but it is not a guarantee: five candidates in that price band did not win. Nor does a high hit rate alone establish value. The amount paid for a contract and its payout if correct matter alongside the likelihood of winning.

Accuracy, calibration and return are not interchangeable

  • Hit rate: the share of selected favorites that won—in this audit, 238 of 273.
  • Calibration: whether outcomes occur about as often as prices imply. The 50–90-cent group’s 71% win rate lagged its 77-cent average price.
  • Hypothetical return: the gain or loss from a specified buying strategy at specified prices. Bitquery reports a four-cent-per-dollar loss for backing every favorite on voting morning.

The audit’s concise characterization was “Quite accurate, but too sure of its favorites.” That wording describes this sample; it should not be read as a general assessment of every Polymarket market or election.

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How Bitquery measured a voting-morning favorite

Bitquery used the average of trades in the 24 hours before 12:00 UTC on voting day as the candidate’s voting-morning price. If no trade occurred in that window, it used the last trade in the preceding 30 days. Runoffs were scored on their runoff date. These choices matter: a displayed price at another time, or a different way of handling a market with no recent trades, could produce a different favorite or result.

The audit covered Senate, House and gubernatorial primaries in the 50 states, excluding special elections and territories. It scored 273 races and omitted nine markets because they had settled before voting, lacked a usable voting-morning price or had no settlement record. The dataset begins September 1, 2025, although some markets traded earlier; reported trading totals count activity from that start date.

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The audit observes Polygon trades only. It does not include Polymarket’s US app or other platforms using off-chain order books. Bitquery reported $36.3 million traded on these primary markets on Polygon since September 2025. That is the publisher’s total for the defined markets and period, not a measure of all Polymarket election activity. A wallet is a blockchain address and should not automatically be treated as one individual.

Wisconsin illustrates the risk in a high-priced favorite

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 calls it the year’s costliest miss by traded dollars: $1.6 million was traded in the market. Hong’s daily average price had not fallen below 89 cents in the final week, according to the audit.

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The example shows why a near-certain price can still fail and why a notable exception does not, by itself, show that markets are generally wrong. Other cases in the audit also point in different directions: Justin Murphy won New Jersey’s Republican Senate primary despite a voting-morning price of 0.4 cents, while favorite Alex Zdan was at 79 cents. In Texas’s Republican Senate contest, Ken Paxton’s price rose from 78 cents to 94 cents after Donald Trump endorsed him; Paxton later won the runoff by 28 points. These are individual outcomes, not evidence that an endorsement caused a price move or result.

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What a changing favorite says—and does not say

In 15 races, the favorite changed during the final week. The new favorite won seven, the earlier favorite won five, and a different candidate won in three. This describes how the late shift played out in Bitquery’s sample; it does not establish that switching to a newer favorite is a profitable strategy.

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Prices can respond to new information and trading, but the audit’s historical counts do not identify why a price changed or whether a specific signal can be used reliably in another election. A person assessing a market would need to consider the contract rules, price, timing, liquidity and the possibility of losing the amount staked—not just the identity of the favorite.

How far these results can be generalized

The audit measures who won the primary, not necessarily who ultimately appeared on the general-election ballot. It also examines primary contests, which Bitquery cautions are smaller local races; the report does not establish that the same pattern carries over to general elections. Its sample ends with data through October 8, 2026, and its figures are the audit publisher’s calculations rather than a separately revalidated dataset.

Readers who want to reproduce or extend the analysis can inspect Polymarket data through Bitquery’s API or obtain full trade-history files from the Bitquery Data Store, as described in the audit. Any replication would need to preserve the same market scope, UTC cutoff, fallback pricing rule and exclusions to make a like-for-like comparison.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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