Prediction Market Vig and Fees Explained

Understanding Prediction Market Vig and Why It Shapes Every Trade

Prediction market vig is the built-in cost of doing business on any Kalshi or Polymarket contract, and if you don't account for it, your edge estimates are wrong before you even place a trade. Vig — short for "vigorish," a term borrowed from sportsbooks — refers to the gap between the true probability of an event and the price you actually pay to take a position. On a coin flip that should trade at 50 cents on each side, you might see 52/50 or worse once fees and spread are baked in. That gap is the house's cut, and it compounds across every contract you hold.

Traders who treat prediction markets like a casual hobby tend to ignore this. Traders who treat it like a structured discipline don't. Before you evaluate any market, you need a clear picture of what you're paying to play, because the vig determines whether your edge is real or illusory.

How Prediction Market Fees Actually Get Charged on Kalshi and Polymarket

Prediction market fees show up differently depending on the platform, and understanding the mechanics matters more than memorizing a number. Kalshi charges a per-trade fee calculated as a function of price and quantity, typically peaking near the middle of the probability curve (around 50 cents) and shrinking as prices approach the extremes (near 1 cent or 99 cents). The formula isn't flat — it's designed so the exchange earns more on contracts where uncertainty, and therefore trading volume, is highest.

Polymarket, running on a different rail entirely, doesn't charge an explicit trading fee in the same way. Instead, its cost structure lives in the spread between bid and ask, in gas costs tied to on-chain settlement, and in the liquidity depth of the order book itself. A thinly traded Polymarket contract can cost you more in slippage than a Kalshi contract costs in stated fees, even though Polymarket's fee schedule looks cleaner on paper.

If you're deciding where to route a given trade, it helps to understand these structural differences at a deeper level — see this Kalshi vs Polymarket 2026 comparison for a side-by-side breakdown of fee structures, liquidity, and settlement mechanics.

Why the Fee Curve Isn't Linear

Both platforms price fees to protect themselves against contracts where outcomes are genuinely uncertain. A market sitting at 50/50 requires more active market-making, more capital at risk for liquidity providers, and more arbitrage activity — so it costs more to trade. A market sitting at 95/5 is closer to resolved, so the fee (or spread) narrows because there's less genuine price discovery left to do.

Calculating Your Real Prediction Market Vig Before You Enter a Position

You can't manage what you don't measure, and prediction market vig has to be calculated on every position, not estimated after the fact. The basic method: take the implied probability of the "yes" side, add the implied probability of the "no" side, and see how far the sum sits above 100%. A perfectly efficient, zero-vig market would sum to exactly 100%. A market summing to 104% means 4 cents of every dollar is going to the house rather than reflecting true probability.

On Kalshi, layer the explicit per-contract fee on top of that spread. On Polymarket, layer in expected slippage based on order book depth — pull up the book and see how much the price moves if you tried to fill your full intended size right now, not just the quoted top-of-book price.

• Sum the yes/no implied probabilities to find the raw spread.
• Add platform-specific trading fees for the exact size you intend to trade.
• Account for slippage on illiquid contracts, especially on Polymarket order books with thin depth.
• Recalculate your required edge threshold after all three costs are combined.

This is the same discipline sportsbook bettors use when adjusting for the standard -110 vig on point spreads, except prediction markets can carry variable vig depending on price level, platform, and liquidity — which makes a single mental shortcut unreliable.

Prediction Market Fees Across Different Contract Types and Time Horizons

Fees don't behave the same way across every category of contract. A same-week sports outcome market on Kalshi behaves differently, cost-wise, than a multi-month economic indicator market, because time horizon changes both liquidity and the number of times you might need to adjust your position before resolution.

Short-duration contracts, especially sports and daily economic releases, tend to have tighter spreads because volume clusters right before the event resolves. Long-duration political or macro contracts often carry wider effective vig early in their life, when few traders have taken a side and the book is thin, then tighten as the resolution date approaches and volume increases.

If you're specifically trading sports-adjacent markets and comparing tools that help model these fee-adjusted probabilities, this Best AI for Sports Betting breakdown covers how automated analysis handles time-decay and liquidity shifts across a season.

Why Rolling Your Position Costs More Than Holding It

Every time you close a position early and re-enter, you pay the vig twice — once on the exit, once on the new entry. This is one of the most underappreciated cost leaks in active prediction market trading. A trader who builds a thesis and holds through resolution pays the vig once. A trader who adjusts the position five times because of headline noise pays it five times, and that compounding cost eats into edge far faster than most people estimate.

How Prediction Market Vig Compares to Traditional Sportsbook Vig

If you've traded traditional sports betting lines, you already have intuition for vig — you just need to translate it. Standard sportsbook vig on a point spread runs close to -110 on both sides, meaning you risk $110 to win $100, which implies roughly 4.5% built-in house edge on a coin-flip proposition. Prediction market vig is often lower on liquid, high-volume contracts, but it's not fixed — it moves with price level, contract type, and platform, which makes it harder to mentally default to a single number the way sportsbook bettors do with -110.

The key difference: sportsbooks set the line and the vig together, and you have limited ability to negotiate either. Prediction markets are peer-to-peer order books, meaning the vig is a function of who else is trading and how tight their quotes are — which means better-informed traders can sometimes find markets where the effective vig is thinner than any sportsbook would ever offer, and worse, markets where it's wider because liquidity hasn't caught up.

Understanding how prices translate to implied probability is the foundation for spotting these gaps — if you need a refresher, this How to Read Prediction Market Odds guide walks through the conversion math step by step.

Strategies for Minimizing Prediction Market Fees Without Sacrificing Edge

Reducing what you pay doesn't mean chasing the lowest-fee platform blindly — it means matching your trade structure to the fee environment. A few approaches experienced traders use:

• Trade near the extremes when your thesis supports it. Kalshi's fee structure charges less on contracts near 5 cents or 95 cents, so if your analysis points to a high-conviction, lopsided outcome, the fee curve rewards you for being right and decisive.
• Size positions to the order book, not the other way around. On Polymarket, check depth before committing size. A position that fits comfortably inside the visible book avoids the slippage that functions as an invisible fee.
• Avoid unnecessary position churn. Every round-trip costs vig twice. If your thesis hasn't changed, holding through volatility is often cheaper than repositioning.
• Compare platforms for the specific contract, not the platform in general. Kalshi might be cheaper for one category and Polymarket cheaper for another, depending on where liquidity concentrates.

None of this replaces having a real edge — fee minimization only matters if the underlying probability read is sound. This is where structured, data-driven analysis earns its keep instead of guesswork.

How PillarLab AI Fits Into This

Manually tracking implied probability spreads, platform-specific fee curves, and order book depth across dozens of live contracts isn't realistic to do by hand on every trade — which is exactly the gap PillarLab AI is built to close. Rather than giving you a single black-box number, PillarLab AI runs every market through a structured 9-pillar analysis that breaks down probability, liquidity, momentum, sentiment, and market microstructure — including the fee and vig dynamics covered in this article — into a transparent framework you can actually inspect and reason about.

Because the tool pulls real-time data directly from Kalshi and Polymarket, the fee-adjusted probability estimates you see reflect current order book depth and platform-specific cost structures, not a stale snapshot. That matters most on contracts where vig shifts quickly — thin markets far from resolution, or high-volume sports contracts where the fee curve peaks near 50 cents. Instead of manually recalculating implied probability sums and slippage estimates for every candidate trade, you get a structured read that's already accounted for where the vig actually sits.

This doesn't replace your judgment — it's built to sharpen it. Traders comparing multiple contracts across both platforms use the 9-pillar breakdown to quickly rule out markets where fees and spread eat too much of the edge, and focus attention on the setups where the structural cost is low enough that a real probability gap can still be captured.

Frequently Asked Questions

What is prediction market vig?

It's the gap between true event probability and the price you pay to trade, similar to sportsbook vig, reflecting platform fees and bid-ask spread combined.

Does Kalshi or Polymarket charge lower fees?

It depends on the contract. Kalshi has explicit, price-based fees; Polymarket's cost lives mostly in spread and slippage, so effective cost varies by liquidity.

How do I calculate the vig on a specific contract?

Sum the yes and no implied probabilities; anything above 100% is the spread, then add platform fees and expected slippage for your size.

Does vig change as a contract nears resolution?

Yes. Spreads and fees typically narrow near resolution and near price extremes, since less genuine uncertainty remains in the market.

Can PillarLab AI account for fees in its analysis?

Yes. The 9-pillar framework incorporates real-time liquidity and pricing data from Kalshi and Polymarket, reflecting fee and spread dynamics in its structured output.

Fee-blind trading is a slow leak on every position you hold, and the traders who consistently find edge are the ones who price the vig in before they ever click buy. Whether you're comparing contract types, platforms, or entire strategies, start from a structured view of the numbers rather than a gut feel — this Best Prediction Market 2026 guide and the How Kalshi Works walkthrough are good next stops if you're still building out your platform-selection framework. When you're ready to apply that discipline to live markets with real-time data behind it, Start free with 10 credits.