De-vig methods on one lopsided market, -750 against +475
Answered against 22 years of US equities and 12 years of US options data and published with the query that produced it. This result is stored as of 2026-08-11, from How to De-Vig Betting Odds Into Probabilities.
| method | favourite_pct | underdog_pct |
|---|---|---|
| Raw quote | 88.24 | 17.39 |
| Multiplicative | 83.54 | 16.46 |
| Additive and Shin | 85.42 | 14.58 |
| Power | 86.6 | 13.4 |
- Rows × columns
- 4 × 3
- Computed
- Completeness
- No missing values
- Source
- US exchange, SIP and OPRA market data
- Licence
- Strasmore terms · free, no signup
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
method |
text | 4 distinct values (Additive and Shin, Multiplicative, Power…) | |
favourite_pct |
number | 83.54 to 88.24 | percent |
underdog_pct |
number | 13.4 to 17.39 | percent |
Computed from Strasmore's warehouse of US exchange, SIP and OPRA market data. Equity prices are delayed; options greeks and implied volatility are end-of-day. This result is stored, not recomputed on load — it is exactly the numbers that were returned on , and the query below is what returned them.
Run it yourself
This is the exact query behind the result above. Change a ticker, a date or a column and run it against the warehouse — no account, no key. The no-signup tier is smaller than the one this page was computed on; a query that reaches past it comes back saying which plan runs it.
SELECT
m.2 AS method,
round(100.0 * m.3, 2) AS favourite_pct,
round(100.0 * m.4, 2) AS underdog_pct
FROM
(
WITH
750.0 / 850.0 AS raw_fav,
100.0 / 575.0 AS raw_dog,
raw_fav + raw_dog AS book_sum,
(book_sum - 1.0) / 2.0 AS equal_share,
arrayMap(i -> 1.0 + (i / 10000.0), range(0, 10001)) AS k_grid,
-- The exponent on the grid that pulls the raised pair closest to exactly 1.
arraySort(k -> abs((pow(raw_fav, k) + pow(raw_dog, k)) - 1.0), k_grid)[1] AS k_power
SELECT arrayJoin([
(1, 'Raw quote', raw_fav, raw_dog),
(2, 'Multiplicative', raw_fav / book_sum, raw_dog / book_sum),
(3, 'Additive and Shin', raw_fav - equal_share, raw_dog - equal_share),
(4, 'Power', pow(raw_fav, k_power), pow(raw_dog, k_power))
]) AS m
)
ORDER BY m.1
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.