Per-Market Information Leakage and Order-Flow Skill: Two Methodological Lenses on Informed Trading in Decentralized Prediction Markets

April 2026 saw notable methodological convergence in the academic study of informed trading on decentralized prediction markets. Three methodological approaches surfaced almost simultaneously, each addressing the same body of documented cases on Polymarket but operating at different methodological layers: Mitts and Ofir [2026] apply a composite statistical screen to over 210,000 wallet–market pairs and estimate $143 million in aggregate anomalous profit; Gomez-Cram et al. [2026] apply an event-level sign-randomization test to the platform's complete transaction history, classifying 3.14% of accounts as "skilled winners" who drive most price discovery, and separately apply a single-event lifecycle-and-conviction heuristic that flags 1,950 accounts as "insiders"; Nechepurenko [2026] develops the Information Leakage Score (ILS) framework, which quantifies per-market information front-loading at the article-derived public-event timestamp.

This paper provides a methodological comparison and a sketch of how the approaches combine. The central organizing claim is that these are three distinct layers of detection, not three competing methods on a single layer. Sign-randomization, in particular, is best understood as an account-level test of persistent directional skill conditional on opportunity selection — not a direct test of insider trading, and not a per-market measure. The heuristic insider flag in Gomez-Cram et al. [2026] is methodologically separate from their skill classifier, applies to a population (single-event, recently-created accounts) that the skill classifier explicitly excludes by design, and has unknown precision against an external labelled set. The Polymarket sample on which all three approaches are evaluated pools politics, sports, crypto, and other categories with structurally different information technologies, so a platform-wide "skilled winner" classification is mechanism-ambiguous and category-conditioned decompositions are required before the methodology can be used as a surveillance layer.

Against this backdrop, ILS^dl provides a complementary per-market quantification. The January 2026 U.S.–Venezuela operation cluster, where the U.S. Department of Justice indictment of Master Sergeant Gannon Van Dyke provides a rare external enforcement benchmark on at least one alleged informed trader, illustrates how the layers stack: account-level lifecycle heuristics identify a small set of suspicious accounts with face-valid enforcement alignment; legal investigation establishes whether a specific trader actually possessed non-public information; per-market scoring would quantify how much information was leaked into each contract before public observation. None of the three layers subsumes the others, and a combined surveillance pipeline gains in precision precisely because each layer filters a different dimension of the problem.