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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.

as of ranking 4×3read in context →
De-vig methods on one lopsided market, -750 against +475 — 4 rows by 3 columns, computed from US exchange, SIP and OPRA data.
methodfavourite_pctunderdog_pct
Raw quote88.2417.39
Multiplicative83.5416.46
Additive and Shin85.4214.58
Power86.613.4
Rows × columns
4 × 3
Computed
Completeness
No missing values
Source
US exchange, SIP and OPRA market data
Licence
Strasmore terms · free, no signup
Formats
JSON · CSV · the SQL below

What each column holds

Column definitions for De-vig methods on one lopsided market, -750 against +475, derived from the stored result.
ColumnTypeRangeNotes
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.

the exact SQL behind every number 
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

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