Is Convexly an on-chain analytics tool?

No, it is a different category. On-chain analytics tools such as Nansen, Arkham, Dune, and Artemis de-anonymize wallets, surface flow and holdings, or build market-data dashboards across many chains. Convexly is an independent audit layer for one question: does a Polymarket wallet's resolved record separate from chance. Many desks would use both.

Which independent prediction-market analytics vendor gives a skill-vs-luck read?

That is the specific gap Convexly fills. On-chain analytics platforms rank and label wallets by observed activity; none of them, as a class, deliver an independent skill-vs-luck read carrying a confidence interval and a false-discovery-rate correction. Convexly's free analyzer returns a four-state read (skilled / not separable from chance / insufficient / flagged) with its realized entry edge, the 95 percent interval on that edge, and the count of resolved positions it was computed on.

Do I still need Nansen, Arkham, Dune, or Artemis if I use Convexly?

Probably yes, if you need what they do. They cover breadth: de-anonymization, token flow, holdings, and market dashboards across many chains and asset types. Convexly covers depth on one narrow question for Polymarket wallets, and does not attempt de-anonymization, live flow, or cross-chain market data. They are complementary, different tools for different jobs.

How does Convexly report a wallet's edge?

Every realized entry edge is published with its 95 percent confidence interval and the number of resolved positions it was computed on, alongside a concentration read and, for a wallet that clears the frozen bar after the false-discovery-rate correction, an FDR-cleared badge. A point estimate is never shown on its own, and a past read is not a forecast.

Is any of this a signal to copy a wallet?

No. Convexly does not take custody, broker trades, route investment advice, or recommend copying any wallet. A skill read describes whether a resolved record separates from chance at the frozen bar; it is not advice to act. Edge Score is a cross-sectional ranker, and its per-wallet temporal holdout did not clear the filed threshold, so it should not be read as a forecast of one wallet's future performance.

Convexly vs on-chain analytics tools for prediction markets

Capabilities side by side

Capability	On-chain analytics tools	Convexly
Wallet de-anonymization and labeling	Core strength of the category: maps addresses to entities and clusters across many chains	Not offered: Convexly reads a wallet's resolved Polymarket record, it does not de-anonymize who owns it
Token flow and holdings	Core strength: real-time balances, transfers, and holdings across the on-chain economy	Not offered: Convexly works from resolved Polymarket positions, not live token flow
Market-data dashboards	Core strength: price, volume, and liquidity dashboards across chains and tokens	Not the focus: only a market-quality research preview (canary preview), built on the same statistics
Independent skill-vs-luck read	Not the category's focus: rankings summarize observed activity, not a statistical skill test	Core strength: a four-state read (skilled / not separable from chance / insufficient / flagged), each at a frozen bar
Confidence intervals on every estimate	Not the category's framing	Every realized entry edge travels with its 95 percent interval and the resolved-position count, never a bare number
False-discovery-rate control across cohorts	Not the category's framing	Benjamini-Hochberg FDR correction across every cohort screen, with an FDR-cleared badge on wallets that clear the bar
Concentration read	Available as raw holdings data, not as a skill-context signal	Reported with each wallet, so a record built on one lucky market is distinguished from a diversified one
Published negative-results registry	Not part of the category	Published nulls, including against its own board: 0 of the 35 testable top-50 wallets cleared the corrected bar in the frozen 2026-06-09 scan

"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.

What Convexly adds that the category does not

Convexly is not trying to watch the whole chain. It applies audit-grade statistics 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).

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.

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