Is Convexly a replacement for Arkham?

No. They answer different questions. Arkham answers 'who controls this wallet, and what entity is behind on-chain activity' across many chains. Convexly answers 'does this Polymarket wallet's resolved record separate from chance, after correcting for how many wallets you looked at'. Identity and skill are different questions, and many researchers would use both.

What does Arkham do that Convexly does not?

Entity attribution. Arkham links wallets to real-world entities across many chains and asset types, with dashboards and alerts across the on-chain economy. Convexly does not attempt any de-anonymization: its scope is prediction-market wallet skill on Polymarket, plus the market-quality surfaces built on the same statistics.

What does Convexly do that an entity-attribution platform does not?

Statistical audit machinery on a Polymarket record. Convexly returns a four-state skill read (skilled, not separable from chance, insufficient, or flagged); every read and every realized entry edge estimate carries a bootstrap 95 percent interval and the count of resolved positions it is computed on; cohort screens apply a Benjamini-Hochberg false-discovery-rate correction; and nulls are published, including against Convexly's own leaderboard, where 0 of the 35 testable wallets in the published top-50 cohort cleared the corrected bar in the frozen 2026-06-09 scan.

I know who a wallet is. Do I still need the skill read?

Knowing the entity behind a wallet does not tell you whether its Polymarket record separates from luck. A large profit can come from a few lucky resolutions or from a genuine edge, and only a statistical read distinguishes them. Convexly's free analyzer returns a four-state read with its 95 percent interval and resolved-position count, so identity from a tool like Arkham and skill-vs-luck from Convexly answer complementary halves of the same question. A past read is not a forecast, and no read is an instruction to act.

Convexly vs Arkham for prediction markets

Side by side

Dimension	Arkham	Convexly
Primary job	On-chain intelligence and entity attribution: linking wallets to real-world entities across many chains	Statistical audit of prediction-market wallet skill: does a record separate from chance
Core question	Who controls this wallet, and what is it doing on-chain	Does this Polymarket wallet's resolved record reflect skill or luck
Coverage	Many chains and asset types; a broad entity-attribution universe	Polymarket prediction markets: one venue, one narrow question
What a wallet read means	An identity or entity label summarizing who is behind observed on-chain behavior	A four-state statistical read (skilled / not separable from chance / insufficient / flagged), each state with a frozen threshold
Uncertainty	Not the product's framing	Every skill read and realized entry edge carries a bootstrap 95 percent interval plus its resolved-position count; a point estimate is never shown alone
Multiple-testing correction	Not the product's framing	Benjamini-Hochberg FDR correction across every cohort screen (primary q = 0.10, with q = 0.05 / 0.20 sensitivity in enterprise work)
Chance baseline	Not the product's framing	Size-matched negative control: 500 seeded random cohorts run through the identical test in enterprise cohort work
Negative results	Not part of the product	Published, including against its own cohort: 0 of 35 testable top-50 wallets cleared the corrected bar (2026-06-09 scan)
Best for	Learning who is behind a wallet, across chains and tokens	Deciding whether a Polymarket record is evidence of skill before acting on it

"Not the product's framing" is descriptive, not a criticism: an entity-attribution platform and a narrow skill-audit layer are optimizing different things.

What Arkham does well

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.

What Convexly does differently

Convexly does not try to identify anyone. It applies audit-grade statistics to one domain: prediction-market wallet skill on Polymarket, with the methodology set out in the published Edge Score methodology paper. 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 carries a bootstrap 95 percent interval, and the free analyzer also reports realized entry edge, where each estimate travels with its 95 percent confidence interval and the count of resolved positions it is computed on. A bare number is never shown.
Cohort screens apply a Benjamini-Hochberg false-discovery-rate correction, and enterprise cohort work is anchored by a size-matched negative control of 500 seeded random draws.
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).
Methods are frozen and version-controlled, and follow-up test designs are filed in public registries before the analyses run, as a standing practice documented on the research index.

Which one for which job

Learning who is behind a wallet, or following an entity's activity across chains and tokens: Arkham. Deciding whether a specific Polymarket record is evidence of skill before you act on it, including before you copy it: Convexly. Identity and skill-vs-luck are complementary halves of the same question, and the honest answer for many desks is both.

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