Predicted Odds vs Implied Odds: Which Is Better for Trading?

Compare predicted odds to executable implied odds—de‑vig books, use bid/ask + fees, and compute the minimum edge required before you trade.

Define the odds

Predicted odds

Predicted odds are your model’s forecast of an outcome’s probability, expressed as a number between 0 and 1 (or 0% to 100%). Traders usually convert that model probability into “fair” odds so it can be compared to a market price.

The clean comparison is always probability-to-probability. If your model says an outcome is 0.60 (60%), its fair decimal odds are 1 ÷ 0.60 ≈ 1.67; if a market implies 0.55, your model is calling it underpriced.

Two separate questions get mixed up here: whether your probabilities are calibrated (when you say 60%, it happens about 60% of the time) and whether a trade is profitable after execution costs. You can be well-calibrated and still lose money if you can’t buy or sell at prices that leave room for costs.

Implied odds

Implied odds (implied probability) are the probability you back out of a tradable price. In sportsbooks quoted in decimal odds, the implied probability is 1 divided by the decimal odds (for example, 1 ÷ 4.0 = 25%).

In $0–$1 “event-share” markets, the mapping is even more direct. Kalshi event contracts settle to $1 if you’re right and $0 otherwise, and Kalshi explains that a “Yes” price of p cents is typically interpreted as about p% market-implied probability.

One more gotcha: the number you see isn’t always the price you can hit. Polymarket describes its venue as a central limit order book where prices emerge from users trading with each other, and it says the UI shows the bid/ask midpoint—but if the spread is wider than $0.10, it shows the last traded price instead—so the executable implied probability comes from the bid when selling and the ask when buying, not the display.

Two implied pipelines

“Implied odds” only help you trade if they’re backed out from something you can actually execute.

• In sportsbooks, the number you can execute is the offered line—but the probability you back out from that line includes the book’s margin (overround), so you need a de-vig step before comparing to your predicted odds.
• In prediction markets, the contract is often priced $0–$1, but the displayed number may be a midpoint (or even a last trade). The number you can execute is the bid or ask, and your net price is further shaped by maker/taker fees.

Below are the two conversion chains made explicit so you stop comparing predicted odds to non-tradable implied numbers.

Sportsbook implied prob

Conversion chain (sportsbook):

1. Quoted odds → implied probability

For decimal odds, implied probability is 1 ÷ odds (Betfair gives the same rule of thumb, e.g., 1 / 4.0 = 25%).

OddScore’s worked example uses odds of 1.80 and 2.00:

• 1 ÷ 1.80 = 55.56%
• 1 ÷ 2.00 = 50.00%

2. Implied probabilities across outcomes → book % (overround)

Add them up and you get a 105.56% “book.” That extra 5.56% above 100% is the overround (vig): embedded margin implied by the set of prices.

3. Book % → de-vigged (“fair”) baseline

If you compare your predicted odds to the raw 55.56% / 50.00% numbers, you’re comparing to probabilities that include that margin. The usual fix is de-vigging: redistribute the overround so outcomes sum to 100%, producing a “fair” baseline for comparison.

Two important guardrails:

• OddScore separates overround from actual bookmaker profit: overround shows the excess built into quoted prices, not realized profit.
• A University College Dublin working paper argues the standard odds-to-probability extraction can misrepresent real win rates (favorites vs. longshots). So treat de-vigged probabilities as a comparison baseline—not a claim about truth.

Practical takeaway: when your model outputs predicted odds as probabilities, the sportsbook number you want on the other side of the comparison is de-vigged implied probability, not the raw implied probability from a single side of the market.

Prediction market price

Conversion chain (prediction market):

1. Contract structure → price-to-probability mapping (only after you pick a tradable side)

On venues like Kalshi, event contracts settle to $1 if your answer is right and $0 otherwise. Kalshi also explains the common interpretation: a “Yes” price of p cents is typically read as about p% market-implied probability.

That mapping only becomes actionable after you choose which price you can actually trade.

2. Displayed price → executable price (bid/ask), not midpoint/last trade

On an order book, the tradable prices are:

• Ask: what you pay to buy now (so your “implied probability when buying” comes from the ask)
• Bid: what you receive to sell now (so your “implied probability when selling” comes from the bid)

Anything else is a display convenience.

Polymarket is explicit about this UI trap:

• Its documentation says the displayed price is the midpoint of the bid-ask spread.
• If the spread is wider than $0.10, the UI shows the last traded price instead of the midpoint.

Either way, you won’t necessarily trade at the displayed number: buys lift the ask; sells hit the bid. So if you’re comparing your predicted odds to “the market,” you need to compare to the side you’d actually execute.

3. Executable price → fee-adjusted execution (maker vs taker mechanics)

After you’ve chosen bid vs ask, the next break in the pipeline is fees. Fees aren’t “a percentage of your edge”; they’re a rule applied to your trade, and the rule depends on whether you were maker (resting limit order) or taker (immediate match).

• Kalshi: its fee schedule says trading fees are only charged for orders that are immediately matched with orders sitting on the order book. The schedule also provides a concrete example: 100 contracts at a $0.50 price costs $1.75 in trading fees.

• Polymarket: its Help Center gives a specific fee formula: fee = C × feeRate × p × (1 − p), where C is shares traded and p is share price. It also states makers are never charged fees (only takers pay). Separately, it states “Geopolitical and world events markets are fee-free.”

Practical takeaway: on prediction markets, “implied odds” for trading should be read as (bid or ask) + the fee rule that applies to how you’ll get filled. Comparing predicted odds to a midpoint (or a last trade the UI swapped in because the spread exceeded $0.10) is comparing to a number you may not be able to capture.

If you want to test predicted odds against “the market,” pick the right endpoint: de-vigged sportsbook probability, or fee-aware executable bid/ask on the prediction venue—not raw odds, not a midpoint, and not a convenient last print.

When implied misleads

“Implied” only means “backed out of a price.” On sportsbooks that’s odds → implied probability → de‑vig; on prediction markets it’s the executable bid/ask (not midpoint/last) plus the venue’s fee rule. It’s useful for trading only when you’re backing it out of the price you can actually hit; it’s useful for interpretation only when you accept the assumptions that make “price = probability” legitimate.

On sportsbooks, the first trap is treating overround (vig) like a clean, mechanical error term. A University College Dublin working paper argues the common “overround = bookmaker margin” model doesn’t describe how a monopolist bookmaker sets odds, and that the standard odds-to-probability extraction misses real win rates: longshots win less often and favorites more often than that method implies. That’s the line that gets crossed: your predicted odds start “beating the market” on paper because your implied baseline is biased.

The second trap is thinking there’s one correct de-vig. Practitioner calculators don’t just do proportional normalization; they offer alternatives like the Shin method, and those choices can move the resulting “fair” probabilities enough to flip whether your predicted edge survives.

On prediction markets, the mislead is conceptual: even after you use the executable bid/ask, treating that price as the probability is an extra assumption. In Manski’s NBER Working Paper 10359, that assumption can fail badly in theory. There’s also a live disagreement about how big the price‑vs‑belief wedge is in practice: Wolfers & Zitzewitz argue that in a broad class of models the wedge is usually small and prices are typically close to mean beliefs, while treating the Manski-style setup as a worst‑case scenario with practical caveats (especially near $0/$1 or when trading is constrained).

So compare predicted odds to a consistent de-vigged baseline (sportsbooks) or an executable price (prediction markets), and treat “market probability” as a tradable number—not automatic ground truth.

Breakeven after costs

Call a trade +EV (positive expected value) when your probability estimate implies a higher fair price than the market offers after all execution costs. The fastest way to stop “value” from being a vibe is to compute the minimum mispricing your predicted odds must clear.

1. Pick the executable market number (not the pretty one).
   Sportsbook: use the actual decimal odds you can bet. Event-share markets: use the ask if you’re buying “Yes” and the bid if you’re selling.

2. Sportsbook breakeven (vig is already in the line).
   If the book is offering decimal odds O, your breakeven win probability is p* = 1/O.
   If you also de‑vig to a “fair” baseline probability p_fair for that outcome, the line’s built-in tax in probability terms is (1/O − p_fair)—your model’s edge vs the baseline must be bigger than that to be worth taking.

3. Event-share breakeven before fees (spread handled by Step 1).
   For a $0–$1 contract at executable price p, buying 1 share has gross edge q − p, where q is your predicted probability.

4. Subtract venue fees to get the real threshold.
   Polymarket taker fees use fee = C × 0.07 × p × (1 − p) (rounded to 5 decimal places); makers aren’t charged.
   Kalshi fees use round up(M × 0.07 × C × P × (1−P)) for taker matches, or the maker coefficient 0.0175—so your minimum edge is q − p > fee_per_share at your trade size.

5. Add funding friction if it’s unavoidable.
   If you fund via card on Kalshi, the fee schedule lists a maximum 2% deposit fee; treat that as extra cost your edge must cover before you call anything +EV.

Choose your anchor

In practice, you’re choosing which number is your benchmark: your model probability q (converted into a net‑of‑cost fair price) or the market’s executable implied probability p (the side of the bid/ask you can actually trade, fees included). If you’re trying to trade mispricing, anchor on predicted odds; if you’re trying to trade execution, anchor on implied odds (the price you can actually hit).

Rule of thumb: if you can’t point to the exact line (sportsbook) or bid/ask + fee calculation (prediction market) your anchor implies, you don’t have a trading workflow—you have an opinion.

Tooling and tracking

Treat this like an audit log that lets you reconstruct the exact predicted probability you acted on and the executable bid/ask + fee rule you could have traded.

• Predicted odds snapshot: timestamp, event/market ID, your probability, fair-odds conversion, and the exact model version used.
• Executable implied odds snapshot: venue, contract, bid, ask, and size available at those prices (so you can separate “pricing” from “liquidity”).
• Fee schedule archive (by venue): save the fee rules you were operating under that day. Source them from the Kalshi Fee Schedule (PDF) and the Polymarket Help Center.
• Order + fill record: order type, submitted price, fill price(s), fills/partials, and whether you were maker vs taker (fees often differ).
• All-in costs line items: trading fees, plus any funding/deposit fees you incurred, recorded separately from trading P&L.
• Outcome + net P&L: settlement result, gross vs net, and the predicted-vs-executable edge at entry.
• MarketsPrediction: use it to screen where implied odds and liquidity are live across venues, then confirm and log the executable bid/ask and fees on the venue itself.

Anchor on what you can trade

If you’re deciding which number to trust before you click, treat the executable implied odds as the non-negotiable reality—bid when you sell, ask when you buy, with the venue’s fees baked into your threshold—because anything else is a display, not a price. Predicted odds only deserve to be your “better” anchor when they’re calibrated and you’ve converted them into a net-of-cost breakeven that clears vig, spread, and fees at the size you’ll actually trade. Your first move is simple: write down the exact line or bid/ask you can hit and compute the minimum edge your model must show after costs; if you can’t do that, you’re not trading an advantage—you’re trading a narrative. Use a cross-platform odds screen to find where the price and liquidity look best, then verify the executable bid/ask and fee rule on the venue before you place the order.