How to Find Good Traders on Prediction Markets

Prediction markets reward two different skills: picking outcomes and picking who picks outcomes. Most newcomers over-index on the former-chasing the flashiest market on the feed-while the sustainable edge often lives in the latter. On Polyman at polyman.fun, we built copy trading and AI scoring because we kept seeing the same failure mode: talented retail users burning cycles on market trivia instead of curating a small set of traders whose process they understood.

This page is the field guide: what to measure, what to ignore, what should make you walk away, and how our six-component score plus TRADE / CAUTION / AVOID labels compress weeks of manual research into minutes-before you ever hit follow. For context on the product surface, read copy trading predictions and our walkthrough on how to copy winning traders; for ranked starting points, pair this with best prediction traders and top Polymarket traders.

Why finding good traders matters more than finding good markets

Markets are cheap to browse and expensive to trade well. Liquidity gaps, last-minute information, and resolution mechanics punish impulsive entries. A strong trader has already internalized those frictions-they size for gap risk, they know when a price is an invitation versus a trap, and they repeat a process across dozens of markets. When you copy, you are not buying a single contract; you are renting a decision system.

That is why we optimize Polyman around trader discovery first. The feed and leaderboard exist to answer: who is behaving professionally with real capital at risk, and does their history survive basic statistical hygiene? Markets will always be noisy; people who survive the noise with a documented book are the scarce input.

The metrics that actually matter

Start with win rate from closed positions. Realized outcomes beat narrative: did they bank wins and eat losses honestly, or is the profile propped up by open marks that have not resolved? Pair that with PnL across multiple periods-1D, 1W, 1M, and all-time-so you are not fooled by one lucky sprint or one bad stretch that is already reversing.

Next, insist on prediction count and sample size. Activity feeds lie by truncation; unique markets traded tells you whether the edge is broad or a handful of hero trades. Layer in activity recency: inactive for weeks means stale process, ghosted risk, or a strategy that does not match your need for ongoing signals. Finally, read portfolio size at risk-open position value shows conviction and capacity; micro tickets can be noise, while whale-size books demand respect and tighter slippage discipline when you mirror them.

One rule we repeat internally: display what Polymarket proves, not what a spreadsheet wishes were true. That is why we anchor profiles to positions and closed outcomes instead of treating every fill line as a full story-one position can spawn dozens of executions, and mistaking fills for edge is how retail talks itself into heroes who are actually one unresolved ticket away from looking ordinary.

Red flags to watch for

• Tiny sample sizes. A handful of trades can look like genius; it is usually variance. Demand depth or downgrade them to experimental follows only.
• Inactive for weeks. Copy engines need fresh behavior; dormant leaders strand your allocation in yesterday’s thesis.
• Single-market concentration. One giant line item is a catalyst trade, not a diversified process-fine if that is what you want, catastrophic if you thought you were buying stability.
• Suspiciously perfect records. Real traders bleed sometimes; immaculate curves deserve extra scrutiny on resolution timing, market selection, and whether wins are repeatable.

Using Polyman's AI scoring to shortcut the search

Manual due diligence does not scale; neither does blind trust. Polyman's model aggregates six components-think win quality, profitability context, activity and recency, how much capital is live, breadth of markets, and consistency signals-into a 0–100 style score with clear TRADE, CAUTION, and AVOID labels. The minimum fifteen predictions gate exists so CAUTION cannot dress up as certainty on a three-trade meme account.

Use the score as triage: high scores earn a profile deep-dive; middling scores go on a watchlist; low scores require a thesis for why you disagree with the model. The AI layer is a filter, not a substitute for reading open positions and understanding what markets they actually play.

Building your trader watchlist

Treat discovery like portfolio construction. Compare the same names across periods so you see who mean-reverts and who is genuinely streaky. Note category specialization-politics scalpers behave differently from macro generalists-and decide whether you want correlation or diversification in your follower set.

Finally, study position sizing patterns: ticket clustering, average hold sizing versus bankroll, and whether they add into winners or double down into losers. Those habits become your habits when you copy. Keep three to five names in rotation, cut mercilessly when process drifts, and promote slowly when the data keeps confirming the story.

Good traders are the compounding asset on Polymarket; good markets are just the venue. Build the list with rigor, let Polyman surface the signal, then move deliberately-small size, clear rules, and a leaderboard tab you actually revisit weekly.