This Week's Contract
Will Ken Paxton win the 2026 Texas Republican Primary? (Polymarket, 60c) resolves May 26, 2026 — less than a month out. With $4.2M in volume and a 29-day horizon, this contract sits squarely in the short-term politics window where calibration analysis is most meaningful. Le (2026) assigns a slope of 1.11 for the politics 1w–1mo cell on Polymarket, which is notably lower than Kalshi's equivalent slope of 1.83 for the same horizon. Applying the recalibration formula p* = p^b / (p^b + (1-p)^b) with b = 1.11 to a market price of 60c yields a calibrated probability of 61% — a 1pp edge. That's essentially no mispricing. The market has Paxton priced correctly by historical standards.
The risk profile tells a more cautionary story. The overall score of 26/100 is low, but the manipulation score of 55/100 stands out. Texas statewide primaries attract well-capitalized political actors with direct financial and ideological stakes in the outcome — exactly the environment where concentrated positions can distort prices. The liquidity score of 10/100 compounds this: thin order books mean a single large trade moves the price meaningfully. Traders who arrive late to a news event on this contract pay for it in slippage, not just position loss.
For a directional bet on Paxton, the calibration data doesn't reveal an exploitable edge — 1pp is within noise. The more important question for any trader already positioned here is whether this contract is truly a standalone bet, or whether it's one leg of a correlated portfolio they haven't fully accounted for.
Correlated Positions: The Hidden Risk That Blows Up Prediction Market Portfolios
Prediction market traders learn quickly that diversification means holding multiple contracts. What they learn more slowly — often after a painful drawdown — is that holding multiple contracts in the same underlying event is not diversification. It's leverage in disguise.
Consider a trader holding three Fed monetary policy contracts: "Fed cuts in June," "Fed cuts in July," and "Fed cuts in September," each sized at 20% of their bankroll. On paper, that's three separate bets. In practice, a single hawkish FOMC statement — one piece of information — moves all three contracts down simultaneously. The trader's effective exposure isn't 3 × 20% across independent risks. It's a 60% leveraged position on one macro thesis: that the Fed will cut rates this cycle. When the thesis breaks, all three legs break together, and the portfolio drops 60% from a single event.
The diagnostic question is straightforward: What single news event would move all these contracts in the same direction? If the answer is obvious, the positions are correlated and must be sized as a group. The practical rule follows directly — if you'd risk 10% of your bankroll on one Fed cut contract, risk 10% total across all Fed contracts combined, not 10% per contract.
Apply this framework to the Paxton contract. The Texas Republican primary produces multiple tradeable contracts: Paxton winning outright, John Cornyn's share, George P. Bush's share, and any runoff scenarios. A trader who holds Paxton Yes at 60c and simultaneously holds Cornyn No as a hedge is not running two independent positions. They are both resolved by the same set of events: endorsement news from Trump, a major opposition research drop, early voting turnout in specific counties, or a late-breaking debate moment. One news cycle — say, a credible Trump endorsement shift — reprices every candidate contract in the primary simultaneously.
This correlation is harder to see than the Fed example because the contracts are framed as opposing candidates rather than the same question. But the underlying driver is identical: Texas Republican primary voter preference in May 2026. The number of independent information sources resolving this race is small. Holding three candidate contracts in the same primary is structurally equivalent to holding three Fed cut contracts on the same cycle.
Snowberg & Wolfers (2010) identify the cognitive mechanism behind why traders consistently misprice this: probability misperceptions cause people to evaluate each contract in isolation rather than as part of a joint distribution. Each position feels like a separate forecast. The correlation is cognitive, not just statistical, which is why it persists even among experienced traders.
The correct sizing approach: treat all Polymarket contracts tied to the 2026 Texas Republican Senate primary as a single position. Sum your exposure across Paxton, Cornyn, Bush, and any runoff contracts. That total — not each individual leg — is your actual bet size. If your risk tolerance for this race is 10% of bankroll, allocate 10% across all legs combined. The Paxton contract at 60c is fairly priced at 61% calibrated probability. The question is whether your portfolio is.
Methodology
Calibration analysis applies Le (2026) logistic recalibration to contracts with >$50K volume and >7 days to resolution. The Le slope transforms market prices in logit space: p* = p^b / (p^b + (1-p)^b), where b is the slope for a given (platform, category, horizon) cell, estimated from 292 million historical trades. Slopes >1 indicate underconfidence (prices compressed toward 50%); slopes <1 indicate overconfidence.