Betting Splits

Get public betting splits (handle % and bet %) from DraftKings and Circa Sports.


GET /api/v1/splits

Authentication

Requires API key. Requires Pro tier ($229/mo) or higher.

What are betting splits?

Handle % is the percentage of total money wagered on each side. Bet % is the percentage of total tickets (bets placed) on each side.

The gap between bet % and handle % reveals sharp money. If 30% of tickets carry 60% of the money, sharp bettors are on that side.

Data Sources

Source	Type	Update Frequency
DraftKings	Recreational book (~35% US market share)	Every 5 minutes
Circa Sports	Sharp-friendly book (attracts professionals)	Every 5 minutes

Comparing DraftKings (recreational) vs Circa (sharp) splits reveals where professional money diverges from the public.

Query Parameters

Parameter	Type	Description
`sport`	string	Filter by sport (comma-separated). Example: `basketball`
`league`	string	Filter by league (comma-separated). Example: `nba,ncaab`
`sportsbook`	string	Filter by splits source. Example: `draftkings,circa`
`event_id`	string	Filter by canonical event ID (comma-separated)
`market`	string	Filter to events that carry a given split market (comma-separated). One or more of `spread`, `total`, `moneyline`.
`limit`	integer	Max results (default 100, max 200)
`offset`	integer	Pagination offset (default 0)

Response


{
  "data": [
    {
      "event_id": "mlb_guardians_orioles_2026-04-16",
      "sport": "baseball",
      "league": "mlb",
      "sportsbook": "draftkings",
      "away_team": "Baltimore Orioles",
      "home_team": "Cleveland Guardians",
      "spread": {
        "away_odds": -1.5,
        "home_odds": 1.5,
        "handle_pct": { "away": 0.22, "home": 0.78 },
        "bets_pct":   { "away": 0.20, "home": 0.80 }
      },
      "total": {
        "line": 8,
        "handle_pct": { "over": 0.53, "under": 0.47 },
        "bets_pct":   { "over": 0.57, "under": 0.43 }
      },
      "moneyline": {
        "away_odds": 104,
        "home_odds": -126,
        "handle_pct": { "away": 0.28, "home": 0.72 },
        "bets_pct":   { "away": 0.33, "home": 0.67 }
      },
      "fetched_at": "2026-04-16T19:25:28.363825+00:00"
    }
  ],
  "pagination": {
    "limit": 100,
    "offset": 0,
    "count": 1,
    "total": 41,
    "has_more": false,
    "next_offset": null
  },
  "updated_at": "2026-04-16T19:29:38.920698424Z"
}

In the /splits response the spread carries line values inside the away_odds/home_odds keys (e.g. -1.5 / +1.5) — the field naming is a known inconsistency. The historical endpoint uses away_line/home_line for the same data.

Response Fields

Field	Type	Description
`event_id`	string	Canonical event ID — use this to join with `/odds` data
`sport`	string	Atlas-normalized sport name
`league`	string	Atlas-normalized league name
`sportsbook`	string	Source sportsbook for the splits data (`draftkings` or `circa`)
`away_team`	string	Away team name
`home_team`	string	Home team name
`spread.away_odds`	number	Away spread line value (e.g., `-1.5`) — see callout above
`spread.home_odds`	number	Home spread line value (e.g., `+1.5`)
`spread.handle_pct`	object	Money % on each side (`away`, `home`; 0.0-1.0)
`spread.bets_pct`	object	Ticket % on each side (`away`, `home`; 0.0-1.0)
`total.line`	number	Over/under line (e.g., 225.5)
`total.handle_pct`	object	Money % (`over`, `under`; 0.0-1.0)
`total.bets_pct`	object	Ticket % (`over`, `under`; 0.0-1.0)
`moneyline.away_odds`	number	Away moneyline odds (American format)
`moneyline.home_odds`	number	Home moneyline odds (American format)
`moneyline.handle_pct`	object	Money % on each side (`away`, `home`; 0.0-1.0)
`moneyline.bets_pct`	object	Ticket % on each side (`away`, `home`; 0.0-1.0)
`fetched_at`	string	ISO 8601 timestamp when data was last scraped

The event_id field uses the same canonical ID format as the /odds endpoint, so you can join splits with odds data directly. For example, fetch odds for a specific game and compare with its splits:


GET /api/v1/odds?event_id=nba_thunder_timberwolves_2026-03-15
GET /api/v1/splits?event_id=nba_thunder_timberwolves_2026-03-15

Per-Selection Bet % (BetMGM)

In addition to the game-level splits from this endpoint, BetMGM odds items in the /odds response include an inline public_bet_pct field (0.0-1.0) representing ticket percentage per selection. This is available on NBA (100%), MLB (100%), and NHL (~60%) prematch markets.

Examples

All NBA Splits


curl "https://api.sharpapi.io/api/v1/splits?league=nba" \
  -H "X-API-Key: YOUR_API_KEY"

DraftKings Only


curl "https://api.sharpapi.io/api/v1/splits?sportsbook=draftkings&sport=basketball" \
  -H "X-API-Key: YOUR_API_KEY"

Specific Game


curl "https://api.sharpapi.io/api/v1/splits?event_id=nba_thunder_timberwolves_2026-03-15" \
  -H "X-API-Key: YOUR_API_KEY"

Splits History

Track how splits shift over time for a specific event.


GET /api/v1/splits/history?event_id={event_id}

Query Parameters

Parameter	Type	Required	Description
`event_id`	string	Yes	Canonical event ID
`sportsbook`	string	No	Filter by book (comma-separated). When set, `limit` is applied client-side after fetching all matching entries.
`start_time`	string	No	Lower bound. RFC 3339 (`2026-04-16T13:00:00Z`) or Unix seconds (`1776344602`).
`end_time`	string	No	Upper bound, same formats.
`limit`	integer	No	Max entries (default 100, max 200).

Response

Entries are sorted oldest-first. This endpoint emits the success envelope (success/data/meta) — distinct from /splits which emits data/pagination/updated_at.

The history payload uses book (not sportsbook) and the spread carries away_line/home_line (not away_odds/home_odds). Both are known inconsistencies vs /splits.


{
  "success": true,
  "data": [
    {
      "book": "circa",
      "ts": "2026-04-16T13:03:21.071966+00:00",
      "timestamp": 1776344602.36,
      "spread": {
        "away_line": -1.5,
        "home_line": 1.5,
        "handle_pct": { "away": 0.37, "home": 0.63 },
        "bets_pct":   { "away": 0.27, "home": 0.73 }
      },
      "total": {
        "line": 8,
        "handle_pct": { "over": 0.43, "under": 0.57 },
        "bets_pct":   { "over": 0.55, "under": 0.45 }
      },
      "moneyline": {
        "away_odds": 104,
        "home_odds": -126,
        "handle_pct": { "away": 0.35, "home": 0.65 },
        "bets_pct":   { "away": 0.35, "home": 0.65 }
      }
    }
  ],
  "meta": {
    "event_id": "mlb_guardians_orioles_2026-04-16",
    "total": 3,
    "books": ["circa", "draftkings"],
    "oldest": "2026-04-16T13:03:22.360588312Z",
    "newest": "2026-04-16T13:08:41.199237823Z",
    "updated_at": "2026-04-16T19:28:50.525875452Z"
  }
}

Data is collected every ~5 minutes and retained for 48 hours via a Valkey sorted set (splits_history:{event_id}) scored by Unix timestamp.

Full History


curl "https://api.sharpapi.io/api/v1/splits/history?event_id=nba_thunder_timberwolves_2026-03-15" \
  -H "X-API-Key: YOUR_API_KEY"

Interpreting Splits

Signal	What it means
Bet % high, Handle % low	Public side — lots of small bets
Bet % low, Handle % high	Sharp side — fewer but larger bets
DK and Circa agree	Market consensus — both public and sharp aligned
DK and Circa diverge	Sharp-public split — Circa (sharp) disagrees with DK (public)

Splits data only comes from recreational books (DraftKings) and one sharp-adjacent book (Circa). No sharp book publishes splits. Use splits as one signal alongside line movement and +EV analysis, not in isolation.