In the small June 21–23, 2026 MLB capture reported below, both venues had a median quoted spread of 1c while recorded ladder depth differed. The figures describe the original sampled window; they are not a current venue ranking or observed market impact. This update corrects capture and statistical interpretation, without rerunning the historical depth calculations.
The original study used sampled L2 states, at roughly 30-second observation intervals and with a retained-level cap. It did not capture every book change. The window contains ~3 days of Polymarket depth and 1 overlapping Kalshi day on MLB moneylines (June 21–23, 2026). June MLB games are regular-season games, not off-season. Treat the results as a short historical description.
The quoted spread is a decoy
Across the window, the top-of-book spread is a median 1.0¢ on both venues — and not just on average: the 25th and 75th percentiles are also 1¢. If you were shopping on spread alone, you'd flip a coin between the two books — which is why our prediction market platform comparison weighs fees, geography, and API access alongside liquidity.
Now ask the question that actually matters for anyone trading size: how much notional did the recorded ladder place within 1c of the ask? This is a book-walk calculation under static displayed-size assumptions, not a measured fill or price impact.
- Kalshi: a median of roughly $297,000 to move the ask 1¢.
- Polymarket: roughly $15,000–$17,000 to do the same.
The original summary reported a ~15–20× difference and 12+ Kalshi tickers with $400K–$580K median notional by the same calculation. No full-season conclusion, simultaneous-fill claim or inference about the traders supplying that size follows from this short overlapping window.
Polymarket depth varies wildly by sport
The same measurement across Polymarket's sports shows how thin the long tail gets (median USD to move the ask +1¢):
| Market | ~USD to move ask +1¢ |
|---|---|
| FIFA World Cup | ~$52,000 |
| MLB | ~$15,000–$17,000 |
| League of Legends | ~$4,300 |
| WNBA | ~$2,000 |
| Tennis | ~$1,200 |
| CS2 | ~$650 |
| NBA (off-season) | ~$25 |
If you're backtesting an esports or tennis strategy and assuming you can get filled at the quote, this is the table that should worry you. A 1¢ "edge" means nothing in a book that moves 1¢ on a few hundred dollars.
Recorded imbalance correlations in this short window
Order-book imbalance — the ratio of bid size to ask size — is one of the most-cited microstructure signals in equities. The folklore: when bids stack up, price drifts up. We tested it directly here.
At the sampled next-observation horizon, the original analysis reported near-zero linear associations:
- correlation(imbalance, next-snapshot mid move) = +0.007 (MLB), +0.010 (FIFA World Cup), −0.005 (Tennis)
- R² ≈ 0.00005
- ~89% of forward moves are exactly zero to begin with
A near-zero sample correlation does not establish absence of a signal. Short windows, repeated book states, measurement error and a high fraction of zero moves can obscure a relationship. A useful test needs an interval that accounts for dependence, a declared horizon and out-of-sample evaluation; none should be inferred from the point estimates alone.
What we are not claiming
Honesty matters more than a clean story, so the caveats are load-bearing:
- This is ~3 regular-season MLB days of Polymarket depth and 1 overlapping Kalshi day, not a seasonal liquidity estimate. A gap described for another product or table should not be transferred to this study without checking its input manifest.
- The exact Kalshi-vs-Polymarket multiple is day-sensitive — it rests on a single overlapping day of Kalshi depth.
- The book is captured at a ~20-level cap, so any "+5¢ depth" figure is a truncated lower bound.
- These are recorded resting-book states. Fees, stale size, queue position and order impact need their own model; no universal 3–5c crossing cost was measured here.
Quoted spread and recorded depth are different measures. This historical summary does not establish a tradable edge or disprove all imbalance strategies.
See the data
We publish a free, score-synced cross-venue matched-book sample (Polymarket × Kalshi on the same games, one timeline) on Hugging Face and Zenodo (DOI 10.5281/zenodo.20816908). Use it to inspect the matched-quote schema. A single-game top-of-book sample does not contain the full multi-game, per-level input needed to reproduce the depth table above. Consult the product’s matching and capture schema before using an aligned panel for causal inference.
If you need the full historical depth archive — the part that can't be backfilled — start with how to get historical Polymarket order-book data and the data archives, or see our vendor-neutral comparison of where to get historical Polymarket and Kalshi order-book data, which covers competing providers too.