DFS Data API: Player Props & Projections for Lineup Tools

A DFS data API delivers the player-level data that daily fantasy tools run on: player props, alternate lines, projections, and graded results, all through one programmatic feed. SportsGameOdds pulls props from 85+ books (sportsbooks plus DFS sites like PrizePicks and Underdog), then exposes a no-vig fairOdds consensus you can convert into implied probabilities and projected stat lines to power lineup optimizers and pick'em tools.

Why use SportsGameOdds for DFS

• Sportsbook and DFS props in one feed. Every player prop nests prices from sportsbooks like Pinnacle, DraftKings, FanDuel, BetMGM, Bet365, and Caesars alongside DFS sites PrizePicks and Underdog under a single byBookmaker object per oddID, across 85+ books, so you compare DFS lines to the wider market without stitching separate feeds together.
• Alternate lines, not just the main number. Pull player props, team props, and alt lines so a pick'em tool can model every threshold a player offers rather than only the headline over/under.
• No-vig fairOdds → implied projections. Each odd ships both fairOdds (vig removed, consensus) and bookOdds (with vig). Convert fairOdds to an implied probability, and pair it with fairOverUnder to derive a projected stat line per player. That is the input your optimizer needs.
• Players and stats data built in. A players object resolves each playerID to a real name and team, and normalized statID values (passing yards, points, rebounds, strikeouts) tie every prop to a box-score stat you can join against results.
• Per-bet score for scoring and settlement. Once a game finalizes, each odd carries a settled score result on every plan tier, so you can grade pick'em entries automatically and backtest how your projections performed.

What you can build

DFS tools all start from the same raw need, accurate and current player numbers, but each product shapes that data differently. Here is how the same feed maps to three common builds.

Lineup optimizers

The problem: an optimizer needs a projection for every player, but sportsbooks publish odds, not projections. The solution: read fairOdds (already de-vigged) for each oddID, convert it to an implied probability, and combine it with fairOverUnder to produce a projected stat line. Because the projection is derived from a no-vig consensus of 85+ books, it reflects sharp money rather than one book's margin. Key fields to lean on: oddID, statID, statEntityID/playerID, fairOdds, and fairOverUnder. Join those to the players object and you have a clean per-player projection table to feed into a solver.

Pick'em and over/under tools

The problem: a pick'em product needs the exact line a DFS site is posting right now, then a way to know which side is soft. The solution: each odd's byBookmaker map includes prizepicks and underdog entries with their overUnder line and price. Compare a DFS site's posted line against the sportsbook-driven fairOverUnder, and the gap surfaces which projections are off-market. That is the same logic an over/under tool uses to flag "easy" picks. Alt lines let you do this at every threshold a player offers, not just the main number.

Player-prop analysis

The problem: you want to know which book, DFS or sportsbook, is mispricing a player. The solution: every oddID aggregates all available books under byBookmaker, so you can compute a consensus line, measure each book's deviation, and rank outliers. That is exactly the workflow in the player props analyzer example, which fetches props for a game and compares lines across books, a drop-in starting point for prop research and projection-validation dashboards.

Example request

Fetch player props for a single event, then read fairOdds and the per-book prices (including a DFS book):

curl "https://api.sportsgameodds.com/v2/events?eventID=NFL_BUF_HOU_2026&oddsAvailable=true" \
  -H "x-api-key: YOUR_API_KEY"

A trimmed player prop in the response (Josh Allen passing yards over) shows the no-vig fairOdds consensus and a PrizePicks line under byBookmaker:

{
  "oddID": "passing_yards-JOSH_ALLEN_1_NFL-game-ou-over",
  "statID": "passing_yards",
  "statEntityID": "JOSH_ALLEN_1_NFL",
  "playerID": "JOSH_ALLEN_1_NFL",
  "betTypeID": "ou",
  "sideID": "over",
  "fairOdds": "+100",
  "bookOdds": "-114",
  "fairOverUnder": "231",
  "bookOverUnder": "224.5",
  "score": null,
  "byBookmaker": {
    "betmgm":     { "odds": "-118", "overUnder": "224.5", "available": true },
    "prizepicks": { "odds": "+100", "overUnder": "224.5", "available": true }
  },
  "marketName": "Josh Allen Passing Yards Over/Under"
}

Here fairOdds of +100 implies a ~50% chance Allen clears fairOverUnder of 231, a projection you can drop straight into an optimizer. PrizePicks is posting the line at 224.5, below the fair line, which a pick'em tool would flag as a soft over. After the game, score fills in with Allen's actual passing yards so you can grade the pick. See the full odds object in the docs.

SportsGameOdds vs sourcing DFS data yourself

Rolling your own gives you full control and no per-object cost, which can make sense at small scale or for a single site. The trade-off is that DFS sites and sportsbooks change markup and markets constantly. The ongoing maintenance, not the first integration, is where most of the cost lands.

Frequently asked questions

What is a DFS data API?

Does it include PrizePicks and Underdog lines?

How do I turn odds into player projections?

Can I get player stats for scoring and settlement?

Is this a DFS contest or lineup platform?

How fresh are the player props?

Related use cases

• Odds Data for Sports Betting Models: feed fair odds and props into ML projection models.
• Sports Odds API for Betting Apps: ship odds and props into a consumer app.
• Odds API for Positive EV Betting: use no-vig fair odds to find +EV plays.
• Player Props Odds API: the broader player-props data overview.
• Pricing: start free with 2,500 objects/month, no credit card.

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