Convexly vs on-chain analytics tools for prediction markets

Capabilities side by side

"Not the category's framing" is descriptive, not a criticism: a broad on-chain intelligence platform and a narrow statistical audit layer are optimizing different things.

What on-chain analytics tools do well

The category's strength is breadth and visibility. Tools such as Nansen, Arkham, Dune, and Artemis de-anonymize and label addresses, surface token flow and holdings, and turn raw on-chain activity into dashboards and alerts across many chains and asset types. If your question is "what is happening on-chain right now, which known wallets are involved, and how do the numbers look across the market", that is the job this class of tool is built for, and prediction markets are a small corner of it.

Arkham

Arkham's strength is attribution. It links wallets to real-world entities across many chains and asset types, and turns raw on-chain flow into dashboards and alerts a researcher can act on quickly. If your question is "who is behind this wallet, and what is that entity doing across the chain", that is the job Arkham is built for. For a Polymarket wallet, Arkham can tell you the identity side of the picture; knowing the entity does not tell you whether its record separates from luck.

Dune

Dune's strength is flexibility. It exposes raw on-chain data across many chains as SQL, so you can ask almost any question and shape the answer into the dashboard you want, and prediction markets are one small dataset among many you can reach. The tradeoff is that the analysis, and its correctness, is yours to write: uncertainty and multiple- testing correction are not provided by default. You can query the same wallets in Dune and bring them to Convexly for the skill-vs-luck read.

Artemis

Artemis's strength is market and volume analytics. It covers many chains, protocols, and sectors, and turns raw on-chain activity into dashboards a researcher can scan quickly, with prediction markets tracked as one sector at the market and volume level. If your question is "how much is trading, where, and how is activity trending", that is the job Artemis is built for; individual-wallet skill is not what it sets out to measure.

What Convexly adds that the category does not

Convexly is not trying to watch the whole chain. It applies the statistics an audit requires to one domain: prediction-market wallet skill on Polymarket, with a public, version-controlled methodology. Concretely:

• A four-state skill read (skilled / not separable from chance / insufficient / flagged) instead of a raw PnL rank, with every state gated by a frozen threshold.
• Every read publishes a realized entry edge together with its 95 percent confidence interval and the count of resolved positions it was computed on; a point estimate is never shown on its own.
• A concentration read travels with each wallet, so a record built on one lucky market is distinguished from a diversified one.
• Cohort screens apply a Benjamini-Hochberg false-discovery-rate correction, and a wallet that clears the frozen bar after that correction carries an FDR-cleared badge.
• Nulls are published, including against Convexly's own board: in the frozen 2026-06-09 scan, 0 of the 35 testable wallets in the published top-50 cohort cleared the corrected bar (full table).
• Enterprise cohort work is anchored by a size-matched negative control of 500 seeded random draws, and methods are frozen and version-controlled, with follow-up test designs filed in public registries before the analyses run, as a standing practice documented on the research index.

Which one for which job

Following broad on-chain activity, de-anonymizing wallets, tracking token flow, or reading market dashboards across chains: an on-chain analytics tool. Deciding whether a specific Polymarket record is evidence of skill rather than luck before you act on it, including before you copy it: Convexly. The two are complementary, and the honest answer for many desks is both.