Shin's Method and Polymarket: Building a Fair-Value Benchmark for Sports Prediction Markets

A Polymarket contract priced at 62 cents and a Pinnacle moneyline of -160 are not directly comparable numbers. One is a raw market price that may embed a small platform spread; the other is a bookmaker price that embeds a deliberate margin, typically 2 to 4%, built into both sides of the market. Comparing them without first removing the bookmaker's margin produces a systematically biased read on which venue is offering the better price.

Shin's Method is a de-vigging technique that estimates the proportion of a bookmaker's market attributable to insider or informed money, and uses that estimate to back out a more accurate implied probability than simple proportional margin removal. Applied to Pinnacle's closing line — already the sharpest widely available bookmaker benchmark — Shin's Method produces a fair-value probability estimate that can be directly compared against a prediction market price expressed in the same probability terms. This piece covers the methodology end to end: what Shin's Method actually computes, how to build the comparison pipeline against Polymarket, and how to interpret the resulting divergences.

Why Simple Margin Removal Isn't Good Enough

The most common de-vigging approach is proportional margin removal: divide each outcome's implied probability by the sum of all implied probabilities across the market, so the total sums to exactly 100%. This is simple to compute and reasonably accurate for two-way markets with modest margins, which is why it remains the default approach in most betting analysis.

The limitation is that proportional removal assumes the bookmaker's margin is distributed evenly across outcomes, which is not how bookmakers actually price risk. Favorites and longshots do not carry the same effective margin in most bookmaker pricing models, a pattern well documented in the sports betting literature as the favorite-longshot bias. Proportional removal ignores this and produces a systematically distorted fair-odds estimate, particularly for markets with a strong favorite and a strong underdog rather than two closely matched outcomes.

Shin's Method corrects for this by modeling the market as a mixture of informed traders (who bet based on genuine knowledge of the true outcome) and uninformed public money, then solving for the insider proportion that is consistent with the observed prices. The resulting de-vigged probabilities correct for the favorite-longshot distortion that proportional removal leaves in place, which matters significantly when the benchmark is being used to evaluate whether a prediction market price represents genuine value or is simply reflecting the same bias in a different form.

Computing Shin's Method in Practice

The Shin's Method calculation solves for a parameter representing the estimated proportion of informed money in the market, then uses that parameter to transform raw bookmaker odds into de-vigged probabilities. For a two-outcome market, this involves solving a quadratic equation derived from the raw implied probabilities; for markets with more than two outcomes, the calculation extends to an iterative numerical solution, since a closed-form result does not generally exist beyond the two-way case.

In practice, this means pulling Pinnacle's live or closing odds for a given market, converting to raw implied probabilities, and running the Shin's Method solver to produce a de-vigged probability for each outcome. The output is a fair-value probability estimate that removes both the standard bookmaker margin and the favorite-longshot distortion that simple proportional methods leave behind.

The timing of the snapshot matters as much as the calculation itself. Pinnacle's closing line — the price immediately before match start — has more information incorporated than an early pre-match price, since sharp money has had time to move the market toward its most accurate estimate. Using an early snapshot as the benchmark understates how sharp the true closing consensus is, which can make a prediction market price look artificially more or less efficient than it actually is relative to the final, fully-informed benchmark.

Comparing De-Vigged Pinnacle Against Polymarket Prices

With a de-vigged Pinnacle probability computed, the comparison against Polymarket is direct: Polymarket contract prices already express an implied probability without a traditional bookmaker margin structure, since the platform earns revenue through trading fees rather than a built-in spread on each side. This makes the comparison cleaner than comparing two bookmaker prices against each other, where both would need independent de-vigging.

A meaningful divergence — where the Shin's-adjusted Pinnacle probability and the Polymarket price disagree by a nontrivial margin — can indicate one of three things: a genuine pricing inefficiency in one venue, a timing lag where one market has incorporated recent information faster than the other, or a liquidity effect where Polymarket's price reflects thin order book depth rather than a considered probability estimate. Distinguishing between these three explanations requires looking beyond the price snapshot itself.

The most useful diagnostic is tracking how the divergence resolves over time. If the Polymarket price moves toward the Pinnacle-implied fair value as match start approaches, that's evidence the divergence was a timing lag rather than a persistent inefficiency. If the divergence persists or widens right up to match start, it more plausibly reflects either a genuine information difference between the two markets or a structural liquidity effect specific to that particular contract.

Pre-Game Versus In-Play Sharpness

The comparison between Pinnacle and Polymarket looks meaningfully different pre-game versus in-play, and treating the two regimes identically produces misleading conclusions. Pre-game, Pinnacle benefits from days of accumulated sharp money flow and is generally regarded as the more efficient of the two markets for most sports and leagues, meaning divergences more often resolve toward the Pinnacle price than away from it.

In-play, the picture is less settled. Pinnacle's in-play pricing updates continuously but still relies on a relatively small number of professional traders and automated models reacting to game state changes, while Polymarket's in-play pricing is driven directly by whoever is actively trading the contract at that moment, which during high-attention live events can include a meaningfully larger and faster-reacting pool of participants than Pinnacle's in-play desk.

This means in-play divergences deserve separate tracking from pre-game divergences rather than being pooled into a single sharpness comparison. A benchmark pipeline that logs the timestamp, game-state context, and divergence magnitude separately for pre-game and in-play snapshots produces a much more useful dataset than one that treats all price comparisons as equivalent regardless of when they occurred relative to match start.

Building the Comparison Pipeline

A practical pipeline for this benchmark has four stages. First, continuous odds collection from Pinnacle across the markets of interest, timestamped and stored at a resolution fine enough to distinguish pre-game snapshots from in-play updates. Second, the Shin's Method calculation applied to each snapshot to produce a de-vigged fair-value probability series over time rather than a single static number.

Third, parallel collection of Polymarket contract prices for the corresponding markets, ideally sampled at a matching frequency so that comparisons are made between genuinely contemporaneous snapshots rather than stale prices on one side. Fourth, a divergence calculation and logging layer that records the magnitude and direction of any gap between the two, along with contextual metadata — time to match start, recent line movement, and available liquidity depth on the Polymarket side — needed to interpret the divergence rather than just observe it.

The output of this pipeline is not itself a trading signal; it is a research dataset. Aggregated over enough markets and enough time, it answers the underlying question of interest — whether Polymarket sports prices are, on average, sharper, less sharp, or comparably sharp to a rigorously de-vigged Pinnacle closing line, and whether that answer changes materially between pre-game and in-play conditions.

Interpreting the Results and Their Limits

A persistent, statistically meaningful divergence between the Shin's-adjusted Pinnacle price and the Polymarket price, in a consistent direction across many markets, is the strongest form of evidence this methodology can produce. A single divergent observation on one market is not evidence of anything beyond normal price noise, since both markets have legitimate short-term variation independent of any underlying inefficiency.

It's also worth being explicit about what this benchmark cannot tell you. Shin's Method is itself a model with assumptions — it assumes a specific structure for how informed and uninformed money interacts with bookmaker pricing, and that structure is an approximation rather than a literal description of Pinnacle's internal pricing process. The de-vigged probability it produces is a better estimate of fair value than simple margin removal, but it is not a perfect ground truth, and treating it as one risks over-interpreting small divergences that fall within the model's own margin of error.

Liquidity depth on the Polymarket side is the other major caveat. A large divergence on a market with thin order book depth may simply reflect that the last trade happened at a stale price, rather than reflecting the market's genuine current fair-value estimate. Filtering comparisons to markets with meaningful trading volume and recent price updates on both sides substantially improves the reliability of the resulting dataset.

Conclusion: A Rigorous Benchmark, Not a Trading Signal on Its Own

Shin's Method gives a more defensible fair-value estimate from Pinnacle's odds than simple proportional de-vigging, correcting for the favorite-longshot distortion that would otherwise bias any comparison against Polymarket prices. Building a systematic pipeline that computes this benchmark continuously, separately for pre-game and in-play conditions, and logs divergences with the context needed to interpret them, produces genuinely useful research on relative market efficiency between the two venues.

What this methodology does not do on its own is generate a trading decision. A logged divergence is a candidate worth further investigation — checking liquidity depth, checking whether the divergence resolves over time, and checking whether it recurs consistently across similar market conditions — rather than an automatic signal to act on. The value of the benchmark is in disciplined measurement; the trading decision built on top of it is a separate layer with its own risk controls.