Polymarket Prediction Contracts

Updated 22 June 2026

Polymarket prediction contracts are tradable, on-chain instruments that use ERC-1155 tokens to represent mutually exclusive outcomes for future events.
They employ settlement legibility—using repeatable templates, authoritative evidence, and timed resolution—to ensure clear contract formation and reliable trading.
Empirical findings reveal that market pricing is influenced by cross-market frictions, calibration errors, and specialized risk-management protocols for binary outcomes.

A Polymarket prediction contract is a tradable, on-chain instrument that allows market participants to take positions on the outcome of future, well-defined events by trading ERC-1155 tokens representing distinct, mutually exclusive outcomes. These contracts form the backbone of a decentralized information aggregation system, with primary applications in forecasting, economic hedging, and empirical study of crowd belief formation. Settlement and market formation processes on Polymarket are deeply shaped by operational, legibility, and economic constraints that define not only what can be traded, but also how prices, liquidity, and risk profile emerge in practice (Adegbenro, 13 Jun 2026).

1. Contract Structure and Settlement-Legibility

Each Polymarket contract encodes a question with a finite, usually binary, outcome set $\Omega$ and a set of outcome tokens. For a binary contract, two ERC-1155 tokens represent "YES" and "NO" positions, each redeemable for USDC upon resolution. The key structural elements include human-readable question strings, resolution time windows, and smart contract–enforced settlement via oracles.

A central distinguishing concept is settlement legibility, defined as the degree to which an event can (a) be described in a repeatable template, (b) be settled upon determinate, third-party evidence, and (c) be closed at a knowable time. The operationalization involves a legibility score $L_i = D_{1i} + D_{2i}$ , each $D_j \in \{0,1,2\}$ , reflecting repeatability and determinacy, with mean $L$ ordering observed inventory by topic: sports ( $L=3.99$ ), elections ( $L=3.98$ ), politics/government ( $L=2.52$ ), foreign policy ( $L=1.36$ ), security/conflict ( $L=0.67$ ) (Adegbenro, 13 Jun 2026).

The practical implication is that only events with high $L_i$ —clear templates, authoritative records, and precise closure—are reliably offered as contracts. Selective inventory formation is not explained by public importance: for instance, Africa-topic Polymarket inventory is 77% football, whereas salient civic events are rarely listed, and civic depth in Latin America is highly localized (notably, Venezuela and U.S.-targeted events).

Intercoder reliability for these legibility codings is extremely high (Krippendorff's alpha $L_i = D_{1i} + D_{2i}$ 0 for the two main dimensions), confirming the protocol's precision.

2. Market Formation, Inventory Selectivity, and Topic Geography

Formation of prediction contracts is a selective process, not merely a reflection of public demand. Using an audited dataset of 6,047 contracts (Africa and Latin America topics), the inventory reveals stark topic and regional asymmetries:

Sector	Africa (\%)	LatAm (\%)
Sports	77.4	5.5
Elections	8.3	30.2
Politics/Govt	12.0	22.3
Foreign Policy	2.5	5.5
Security/Conflict	12.0	31.6

In Africa, contract value is dominated by sports (notably AFCON at 58.6% of Africa value), with minimal representation of major civic or conflict events. In Latin America, non-sports value is concentrated in Venezuela (security/conflict, elections) and to a lesser extent across various national elections.

A logistic regression formation model, using a frame of 131 externally assembled civic events, demonstrates that legibility ( $L_i = D_{1i} + D_{2i}$ 1) is directionally predictive of contract listing ( $L_i = D_{1i} + D_{2i}$ 2, $L_i = D_{1i} + D_{2i}$ 3), but falls short of pre-registered thresholds for strong acceptance. Additionally, within listed markets, higher legibility negatively correlates with value traded (OLS $L_i = D_{1i} + D_{2i}$ 4, $L_i = D_{1i} + D_{2i}$ 5), reflecting a "collider" effect: low-legibility events require exceptional salience to be admitted, and thus among the listed, those with lowest legibility have the highest trading value (Adegbenro, 13 Jun 2026).

3. Price Dynamics, Information Processing, and Market Microstructure

Prices in Polymarket binary contracts are routinely treated as probability forecasts: the price of a "YES" token at time $L_i = D_{1i} + D_{2i}$ 6 ( $L_i = D_{1i} + D_{2i}$ 7) is interpreted as the market-implied probability of the event's realization. Empirical analysis confirms that while prices are responsive to new information, their calibration as true probabilities is domain-dependent.

The study of cross-venue pricing—for example, between Polymarket and crypto-option sources (Binance/Deribit)—reveals persistent, significant pricing wedges for economically equivalent threshold events. For September 2023 Bitcoin contracts, the mean Polymarket premium over risk-neutral option-implied probability is 5.6 percentage points (pooled mean 6.3 pp, $L_i = D_{1i} + D_{2i}$ 8, $L_i = D_{1i} + D_{2i}$ 9; half-life of mean reversion ≈4 hours). The gap is largest at low option-implied probabilities and long maturities, mirroring a favorite–longshot pattern, and is robust to transaction cost and measurement error controls (Portnaya, 17 Jun 2026).

A plausible implication is that Polymarket pricing is shaped by a combination of retail speculative demand and cross-market frictions, and cannot be directly equated to risk-neutral belief without adjustment.

4. Calibration, Forecast Interpretation, and Bias Decomposition

Systematic analysis of 292 million trades across 327,000 contracts decomposes calibration error into universal horizon effects, domain-specific biases, domain-horizon interactions, and trade-size effects. On Polymarket, persistent underconfidence is observed in politics (mean recalibration slope $D_j \in \{0,1,2\}$ 0), with prices compressed toward 0.5 (i.e., a contract trading at $D_j \in \{0,1,2\}$ 1 implies a true frequency closer to 0.78). Sports and crypto markets show mild underconfidence ( $D_j \in \{0,1,2\}$ 2 and $D_j \in \{0,1,2\}$ 3 respectively), but the large-trade scale effect so prominent on Kalshi does not replicate on Polymarket ( $D_j \in \{0,1,2\}$ 4, 95% CI [–0.151,+0.395]) (Le, 23 Feb 2026).

Unadjusted interpretation of prices as direct probabilities will mislead, particularly in political contracts, necessitating recalibration formulas (e.g., $D_j \in \{0,1,2\}$ 5) for accurate inference of true event probabilities.

5. Economic and Risk Engineering of Complex Derivatives

Extension of Polymarket contracts into perpetual futures overlays exposes the unique risk geometry of bounded, terminal-jump processes. Standard perpetual designs—static volatility-based margin and basis-only funding—are shown to be non-portable to bounded contracts: static margin fails to absorb median terminal jumps at leverage $D_j \in \{0,1,2\}$ 6, and relative-basis funding fails to control positions near the outcome boundaries.

A six-component resolution-aware risk-engine incorporates jump-adjusted margin, leverage compression to $D_j \in \{0,1,2\}$ 7 as resolution approaches, boundary-corrected funding, composite index estimation (mid, depth-weighted, VWAP), and halt protocols. Empirical evaluation over 13,298 markets evidences stylized boundary-depth asymmetry and large terminal price jumps (median $D_j \in \{0,1,2\}$ 8), but indicates mixed performance on welfare and insurance-fund drawdown floors. The halt protocol effectively controls execution-channel liquidation, but not bad-debt from terminal jumps (Nechepurenko, 11 May 2026).

This suggests that event-linked derivative design requires risk infrastructure tailored for binary payoffs and explicit management of boundary phenomena.

6. Vulnerabilities, Settlement Pathologies, and Architecture Limits

Polymarket's hybrid settlement model—in which fills are matched off-chain and settled on-chain—introduces a critical vulnerability window. "Ghost Fills" occur when a trade is reported as filled off-chain, but is cancelled by a failed on-chain settlement, frequently due to adversarial state mutations (nonce bump, balance drain, allowance revoke, proxy trap). Over 1.95 million such reverts were observed in a nine-month window, with $D_j \in \{0,1,2\}$ 9 directly attributed to these attacks, placing $L$ 0 billion of collateral at risk and costing operators at least $L$ 1 in gas (Shen et al., 15 Jun 2026).

Platform-level mitigations include a switch to deposit-wallet architecture (escrowing collateral) and nonce-elimination, reducing revert rates from 8% to 0.3%. However, complete elimination of the off/on-chain consistency window requires more fundamental protocol redesign.

7. Implications for Research and Forecasting Practice

Platform inventory reflects what can be credibly settled, not pure trader demand—settlement legibility is a dominant constraint ordering what uncertainties are rendered tradable. Empirical and operational results show that Polymarket contract formation, price informativeness, and risk geometry are tightly linked to template repeatability and settlement determinacy.

For inference and research, prediction-market inventories should not be conflated with maps of public interest. Comparative studies reveal persistent, directionally–meaningful but quantitatively imperfect, translation from market price to probability—especially salient in domains with variable legibility or asymmetric incentive structure. Methodological advances in calibration and cross-platform arbitrage are necessary for reliable probabilistic inference from Polymarket contracts (Adegbenro, 13 Jun 2026, Portnaya, 17 Jun 2026, Le, 23 Feb 2026, Nechepurenko, 11 May 2026, Shen et al., 15 Jun 2026).