data feature

Normalized Odds Schema

Every sportsbook structures odds differently — different field names, different market terminology, different ID systems. BetFlux normalizes all of it into a single consistent schema so you never write a custom parser again.

The raw data problem

The same BOS @ MIA spread looks completely different across books. Same market, three incompatible structures:

FANDUEL

Spread

BOS -3.5

-110 / -110

Total

o/u 224.5

-110 / -110

Moneyline

-165 / +140

DRAFTKINGS

Point Spread

Boston -3.5

-112 / -108

Game Total

o/u 224.5

-110 / -110

Money Line

-170 / +145

BETMGM

Spread

BOS Celtics -3.5

-115 / -105

Total Points

Over/Under 224.5

-108 / -112

To Win

-160 / +135

→

After normalization, all three become the same structure. One query returns all books. Zero parsing logic in your model code.

Normalized output

normalized.json

[
{
operator: "FANDUEL",
marketType: "SPREAD",
period: "FULL_GAME",
odds: -110,
spread: -3.5
},
{
operator: "DRAFTKINGS",
marketType: "SPREAD",
period: "FULL_GAME",
odds: -112,
spread: -3.5
},
{
operator: "BETMGM",
marketType: "SPREAD",
period: "FULL_GAME",
odds: -115,
spread: -3.5
}
]

Schema reference

fieldtypedescriptionexample

operatorSportsbookOperatorCanonical book identifier"FANDUEL"

marketTypeMarketTypeSPREAD | TOTAL | MONEYLINE | OTHER"SPREAD"

periodBetPeriodFULL_GAME | FIRST_HALF | FIRST_QUARTER ..."FULL_GAME"

sideBetSideHOME | AWAY | OVER | UNDER"AWAY"

oddsnumberAmerican odds at latest timestamp-110

spreadnumber | nullPoint spread value-3.5

totalnumber | nullTotal line value224.5

impliedProbabilitynumberRaw implied prob (includes vig)0.5238

early access

Query all books through one API

Normalized odds across FanDuel, DraftKings, BetMGM, and Caesars.

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