AI Prediction Market Analyst: Edge Analysis for Kalshi & Polymarket
2026-08-27
Prediction Market Analyst helps you turn noisy event-contract prices into a clear probability, a stated edge, and an explicit list of what could make the call wrong. While Kalshi, Polymarket, and similar venues now move billions of dollars a month, a quoted price still mixes information, fees, thin books, and settlement quirks. This AI separates those layers so you can decide whether a contract looks underpriced, overpriced, or simply efficient.
✅ Base-rate-first probability estimates instead of story-driven guesses ✅ Fee-adjusted expected value that accounts for spread, vig, and slippage ✅ Resolution analysis that treats "will it happen?" and "will this contract pay?" as different questions ✅ Research across politics, economics, geopolitics, sports, crypto, science, and culture
Prediction markets are liquid enough to matter — and crowded enough to punish sloppy reasoning. To understand why a structured probability workflow now has a larger payoff, it helps to look at how fast this market grew and where traders still get trapped.
Quick Answer: What Is Prediction Market Analyst?
Prediction Market Analyst is an AI research partner that assesses event probabilities, quantifies edge versus market prices, and flags resolution risk on platforms like Kalshi and Polymarket.
Key capabilities:
- Independent fair-value probabilities with a separate confidence label
- Edge math after fees, spread, and likely slippage — not just the raw gap to mid-market
- Settlement-rule review, including sources, timing, and edge cases that can void a correct world-state call
- Cross-platform comparison across Kalshi, Polymarket, Metaculus, Manifold, PredictIt, and Insight Prediction
The Problem: More Volume, More Noise, More Ways to Misread a Price
Event contracts are simple on the surface. You buy a yes-or-no claim, the price is supposed to equal the chance of the outcome, and a correct contract pays a dollar. The CFTC describes that structure as a way to forecast, hedge, or speculate on real-world events — the same binary payoff that now sits under elections, Fed meetings, sports, and crypto headlines.
The scale of that activity has changed the job. Combined monthly volume on Kalshi and Polymarket rose from under $5 billion in September 2025 to about $24 billion in April 2026, according to Pew Research Center analysis of The Block data. That monthly figure already exceeds the roughly $14 billion wagered through U.S. legal sportsbooks in an average month of 2025.
About $24 billion — Combined monthly trading volume on Kalshi and Polymarket in April 2026
More volume does not automatically mean cleaner prices. Sports, politics, and crypto have accounted for about 91% of Kalshi volume and 90% of Polymarket volume since mid-2024, but the mix is not the same crowd. Sports has been about 80% of Kalshi activity versus 39% on Polymarket; politics has been about 4% on Kalshi versus 32% on Polymarket. You are often trading against a different user base, a different fee schedule, and a different settlement process for the "same" event.
But converting a live price into a defensible position is still hard:
- The last trade anchors your probability before you have done any independent work.
- A few cents of "edge" can disappear after fees, spread, and a thin book.
- Contract language can resolve NO even when the real-world event arguably happened.
- Cross-platform quotes diverge, and the cheaper screen is not always the better bet.
- Breaking news decays in minutes, while the durable edge usually sits in base rates and structure.
Regulators are still writing the rulebook around that growth. A 2026 CFTC proposed rule would amend how event contracts are reviewed, and the Commission has argued for exclusive federal authority rather than a state-by-state patchwork. That legal flux is itself a risk factor — not a reason to skip analysis, but a reason to stop treating every listed contract as a finished product.
This is exactly what a dedicated prediction-market workflow was built for.
Why Prediction Market Analyst
Prediction Market Analyst treats every contract as a research problem first and a trade second. It starts with settlement rules, builds a fair probability from base rates and graded evidence, then compares that estimate with the market. The output is not a hot take. It is a probability, a confidence label, a fee-adjusted edge, and the single crux the call actually depends on.
| Traditional Approach | Prediction Market Analyst |
|---|---|
| Read the headline, then accept the screen price | Form an independent estimate, then compare it with the price |
| Treat 62¢ as "a 62% chance" | Separate implied probability from liquidity, fees, and crowd bias |
| Skip the rulebook until something goes wrong | Identify the resolution source, timing, and edge cases before any EV |
| One platform, one number | Compare venues, settlement risk, and fee-adjusted edge |
| High conviction because the story is vivid | Probability and confidence as two different numbers |
Market-based forecasts can be strong aggregators. Research and practitioner summaries often find that prediction markets match or beat polls and unaided expert judgment when contracts are liquid and well specified. That is a reason to respect the closing price — not a reason to outsource your entire process to it. Disagreeing with the market still requires a specific, articulable miss: a stale poll, a misread settlement clause, a retail-heavy board, or a base rate the crowd is ignoring.
Probability With a Confidence Label
A useful forecast has two dials. You can be 72% on an outcome and still have only moderate confidence because the evidence is thin. The analyst keeps those dials separate, using language that maps to ranges ("likely (~72%)") instead of false precision. High confidence is reserved for cases where official data, models, and market structure actually line up.
Edge After Fees, Not Before
Raw edge is fair probability minus market-implied probability. Tradable edge is what remains after platform fees, bid-ask, and the slippage a real order would take. A three-point gap on a short-dated, low-liquidity contract can be negative expected value once those costs are included. Kalshi's 2025 run — about $263.5 million in fee revenue on $22.9 billion of volume — is a reminder that the house cut is not a rounding error.
Resolution Risk Before You Size
"Will X happen?" is not the same question as "Will this contract resolve YES?" Oracle votes, official data prints, ties, delays, and ambiguous wording can break a correct world-state call. The analyst reads settlement language first, flags known dispute patterns, and will recommend passing when the rulebook is the real risk.
Example prompts:
"Fair value the next FOMC decision versus Kalshi. Start from historical hike/hold/cut base rates, then adjust only for evidence you can cite."
"Read this Polymarket resolution text and tell me the edge cases that could make a correct prediction still lose."
"Same election contract on Kalshi and Polymarket — compare prices, fees, liquidity, and settlement risk, then say which screen is actually cheaper after costs."
How Prediction Market Analyst Works
The workflow is built for How-to use: one contract, one pass, a decision you can defend.
Step 1: Pin Down the Contract and How It Settles
Name the venue, the exact question, the resolution source, and the clock. Confirm what happens on delay, tie, partial outcome, or a non-event. If the rulebook is ambiguous, that fact belongs at the top of the note — not in a footnote after you have already sized a position.
"Before any probability, extract the resolution source, date, and edge cases for this Kalshi contract."
Step 2: Build a Fair Probability From Base Rates
Start with the historical class (incumbent re-election rates, typical FOMC paths, hurricane landfall frequencies), then adjust only as far as the evidence quality justifies. Vivid narratives get no extra weight. Official data outranks models; models outrank commentary; social sentiment is a narrative tracker, not a primary input.
"Give me a base-rate-first probability for this Senate race. Show the reference class, then every adjustment and why it is that large."
Step 3: Compare With the Market and Compute Fee-Adjusted Edge
Translate the screen price into an implied probability, subtract fees and likely spread, and state edge in probability points and dollars per contract. If you cannot name what the market is missing in two or three sentences, the default is that the market is right.
"Market is 54¢. My fair value is 68%. Recalculate edge after typical taker fees and a one-cent spread."
Step 4: Map Scenarios, Name the Crux, List What Would Kill the View
Most live questions are not a single path. Lay out two to four discrete scenarios with conditional probabilities, then name the one factor the assessment actually hinges on — a CPI print, a filing deadline, an injury report, a court calendar. State what you could not verify.
"Map 3 scenarios for this geopolitics contract. Assign probabilities, name the crux, and list the next three data points that would move me."
Step 5: Decide Pass, Watch, or Size
Process beats outcome. A high-confidence, durable structural edge is sized differently from a five-minute news lead you are already late to. If the book is efficient, the data is stale, or settlement risk is ugly, the correct output is pass.
Try it free — no credit card required.
Results & Use Cases
🗳️ Election and Policy Contracts
- Scenario: A national election or legislative contract is moving on a single poll spike, and the yes price has jumped overnight.
- Traditional Approach: Chase the new number, or freeze because "the market already knows." Either way, you skip the base rate, the likely late-decider pattern, and whether the contract pays on the certified result, the called result, or a named data source.
- This research partner: Rebuilds the probability from reference-class rates, grades the polling evidence, and checks settlement language before calling the move an edge.
- Independent estimate instead of anchored last-trade
- Explicit confidence (most election work lands in moderate, not high)
- Clear "what would change my number" list
If you also need candidate-level power mapping, legislative calendars, or campaign structure around that contract, Political Analyst can pressure-test the political mechanism while the market work stays focused on price versus fair value.
📉 Fed, Inflation, and Macro Event Contracts
- Scenario: A December FOMC contract is trading as if a cut is nearly baked in, but you have not separated the official path, market pricing, and what the resolution source will actually print.
- Traditional Approach: Paste a FedWatch screenshot into group chat and treat implied odds as a forecast. Miss the distinction between "the committee cuts" and "this strike, this meeting, this official release."
- The analyst: Starts from the historical decision framework, layers only verified data, then reports fair value versus the screen with fee-adjusted EV.
- No unsourced "the Fed is likely to cut" claims
- Scenario table for cut / hold / surprise hike
- Crux named (often a specific CPI or jobs print)
When the same week also hinges on growth, labor, or policy transmission — not just the binary contract — Economics Analyst can unpack the macro mechanism so your probability adjustment is proportional to the data, not the headline.
📱 Breaking Geopolitics on Your Phone
- Scenario: A sanctions, conflict, or regime-risk contract spikes while you are away from a desktop. You have five minutes, a mobile screen, and a price that may already be stale.
- Traditional Approach: Trade the push notification. Volume looks like "informed flow." It usually just means attention.
- The analyst, on web or phone: Forces a resolution check first, then a pass/watch/size call. Thin books and fast half-life news get flagged as low-durability edge.
- Works with the same conversation history on iOS, Android, and web
- Distinguishes minute-scale tape from week-scale structure
- Will tell you to stand down when you are behind the move
If the underlying question is about escalation paths, coalitions, or second-order regional effects rather than a single binary, Geopolitics Analyst can map the strategic landscape that the contract is only compressing into one number.
Frequently Asked Questions
Is Prediction Market Analyst free?
Yes. You can use the AI analyst on the free tier with core features and limited monthly usage. Paid plans raise usage (Plus is $20/month for 30× free usage; higher tiers scale from there), add custom model selection, and remove watermarks. Usage resets on your billing date with no daily caps. No credit card is required to start.
How is an AI prediction market analyst different from just reading Kalshi or Polymarket prices?
A platform price is a blend of probability, liquidity, fees, and whoever showed up to trade. This workflow builds a fair value first, then compares it with that price. It also reads settlement rules, splits probability from confidence, and will say "no edge" when it cannot name a specific market miss. Market-based forecasts can be excellent aggregators; they are not a substitute for checking whether the contract in front of you is the event you think it is.
Can it analyze sports and crypto event contracts?
Yes. Sports is the volume leader on Kalshi, and crypto is a much larger share of Polymarket activity, per Pew's category breakdown. The same process applies: resolution first, base rates, then fee-adjusted edge. Sports and crypto tapes move fast, so the analyst treats minute-scale information as something to note, not chase, and puts more weight on durable structure such as series rules, injury windows, or protocol governance calendars.
Does Prediction Market Analyst work on mobile?
Yes. Sessions run with full feature parity on web, iOS, and Android, including speech-to-text. Assessed contracts and reference notes persist across devices, which matters when you start a Fed or election note at a desk and reopen it from your phone after a data release. Live exchange prices still belong on the venue itself; use the analyst for the research layer, then confirm the current quote before you send an order.
Is this accurate enough to trade on?
Treat every output as research, not a broker ticket. Even a well-calibrated 75% call loses a quarter of the time, and CFTC materials are explicit that speculation can lose money. The analyst will pass when books look efficient, data is unverified, or settlement risk dominates. It cannot see your fills, your bankroll, or a true real-time order book. You make the decision.
How much does Prediction Market Analyst cost?
The product is available at $0 on the free plan. Plus ($20/month), Premium ($50), Pro ($100), Max ($200), Ultra ($500), and Enterprise ($1,000) increase monthly usage allowances. Choose a tier based on how many deep contract notes you run, not on a promise of returns. Event contracts can go to zero.
Make Probability Your Working Language
Event contracts are no longer a niche experiment. Monthly volume on the two leading venues has already rivaled, and at times surpassed, the U.S. sportsbook tape, while federal rulemaking tries to catch up. That combination — deeper books, mixed crowds, and unfinished rules — rewards people who can separate a story from a base rate, a price from a probability, and an event from a settlement clause.
Prediction Market Analyst is built for that job: independent fair value, fee-aware edge, resolution risk in plain language, and a clear pass when there is nothing to do. Try it on the next contract you were about to judge from the last print. Explore more at Jenova.
For Developers: Prediction Market Analyst is available programmatically via the Jenova API — integrate probability assessments and event-contract edge analysis into your application with a single API call. Full documentation →