Polymarket Analytics in 2026 — The Complete Guide to Tracking Edge, ROI, and Fair Value on Prediction Markets

Polymarket is a prediction-market venue where positions depend on the specific market question and outcome rules.^[3] An analytics record should retain those identifiers alongside fills, fees, and settlement.

Position and P&L views show outcomes, but evaluating a decision process also requires the fill, cost basis, timestamp, exposure, and a separately recorded probability estimate where one exists. This guide shows how to keep those measures distinct.

Review the available plans while deciding how to record and analyze the positions discussed below.

Why Native Polymarket P&L Isn't Analytics

Realized P&L in USDC is an accounting output, not an analytical one. It tells you what happened. It doesn't tell you why. Two Polymarket traders can both finish a month up $2,000 with completely different underlying edge — one grinding out consistent Fair Value Spread across dozens of contracts, the other riding a single lucky political trade.

Real Polymarket analytics answers three questions:

• How did my entry compare with my model? A 42¢ YES fill and an independent 50% estimate available at fill time imply an illustrative 8¢ gross FVS, subject to model uncertainty and fees.
• How much risk did I take? Kelly-fractional sizing is one calculation based on an estimated edge; portfolio exposure and probability error still matter.
• How do categories differ? Segmented ROI can expose variation across markets, but conclusions need adequate samples, comparable records, and cost context.

The Four Metrics That Define Polymarket Analytics

Four measures can organize the review, provided missing inputs and their sources remain visible.

1. Fair Value Spread (FVS). Compare a fill with an independent fair-probability estimate that existed at or before that fill. Later market quotes and settlement outcomes are separate measures; if an at-fill estimate is absent, FVS remains missing.

Positive FVS means the model estimate exceeds the entry price for that side. Whether the model is calibrated and whether a strategy is profitable require separate evidence, costs, and adequate coverage.

2. True ROI on capital deployed. Polymarket contracts are binary — either $1 or $0 at settlement. ROI has to be calculated against capital-at-risk, not against notional exposure.

A YES bought at $0.20 that resolves YES returns 400% ROI on that capital. The same $1,000 spread across 20¢ contracts vs 80¢ contracts has radically different risk/reward profiles. Portfolio-level ROI has to weight this.

3. Kelly-fractional sizing. The Kelly Criterion tells you what fraction of your bankroll to allocate to a bet given your estimated edge. On Polymarket:

Fractional Kelly reduces a calculated stake, but the calculation remains sensitive to errors in the probability estimate and to combined portfolio exposure.

4. Segmented ROI by market category. Aggregate P&L hides everything. Segmented ROI reveals it.

• Politics & elections
• Crypto price markets
• Sports outcomes
• Geopolitics & world events
• Culture / entertainment

Tagged trades can reveal category differences. Review sample size, costs, liquidity, and model-estimate coverage before changing a strategy.

How Polymarket's Structure Changes What You Track

Polymarket isn't a sportsbook, and treating it like one produces bad analytics. Three structural differences matter:

• Prices are direct probabilities. A share at $0.42 is a 42% implied probability. No decimal-odds conversion, no vig baked into the line the way a sportsbook does. Your analytics stack should compute in probability space natively.
• Liquidity varies wildly by market. A headline political market might have $50M+ in volume. A niche cultural market might have $10K. Your ROI numbers on thin markets are less reliable — a good analytics view surfaces liquidity alongside performance.
• USDC settlement, on-chain positions. Every trade is auditable on Polygon. Good tools can import your position history directly rather than requiring manual entry — but they still need a place to record your fair value estimate at entry, because that lives in your head, not on-chain.

Building a Polymarket Analytics Workflow

A consistent review workflow can make decisions and data gaps easier to examine. It does not establish skill or promise profitability:

• Step 1 — Record an independent estimate when available. Keep the probability, model source, and timestamp at or before the fill. If no such estimate exists, leave FVS unavailable rather than reconstructing it from a later quote.
• Step 2 — Tag by category. Politics, crypto, sports, geopolitics. Tag every trade at entry so segmented ROI works.
• Step 3 — Review sizing assumptions. If using Kelly, check the estimated probability, fees, model error, and combined exposure before interpreting a suggested stake.
• Step 4 — Review by period. Weekly summaries can describe realized ROI and available FVS coverage; small or incomplete samples need more context before drawing conclusions.
• Step 5 — Review weak segments. Investigate negative FVS or ROI with sample size, costs, coverage, and model calibration before changing exposure.

What Polymarket Analytics Doesn't Do (And Shouldn't Pretend To)

Analytics can show recorded prices, outcomes, and model comparisons. It cannot certify that a model estimate is correct or that a past pattern will persist.

The trap is treating tracking as a substitute for research. It isn't. Tracking is what turns your research into a measurable process. If your process is bad, better analytics just tells you that faster.

How evrai Handles Polymarket Analytics

Evrai supports connected or manual Polymarket position records with fills, balances, P&L, and exposure. Fair Value Spread is only available when an independent estimate existed at or before a fill; it is separate from settlement and later market marks.

Because the same dashboard also tracks Kalshi and sportsbook records, you can review positions across venues. Compare like contract rules and distinguish realized P&L from independent fair-value estimates. See the methodology for coverage and timing requirements.