Closing Line Value (CLV)

Analyze how well a sportsbook’s closing odds compare to Pinnacle’s devigged (no-vig) probability. Positive CLV means the book closed looser than Pinnacle — a historical proxy for edge.


GET /api/v1/historical/clv

Sharp tier or higher required. Pro and lower tiers receive a 403 tier_restricted error. This endpoint is gated by the closing_lines feature.

Phase 1 data window: Closing lines are captured in real-time as games go live. The Valkey store retains data for 30 days. Historical data beyond 30 days is not yet available.

How Closing Lines Are Captured

When a game transitions from pre-match to live, the server captures each book’s last pre-match odds as that event’s closing line. The closing price is devigged using the Power method (same as EV calculation) to produce a no-vig probability per selection.

CLV is then computed as:


CLV% = (pin_no_vig_prob - book_no_vig_prob) / book_no_vig_prob × 100

Where pin_no_vig_prob is Pinnacle’s devigged probability (the sharp reference) and book_no_vig_prob is the soft book’s devigged probability. A positive CLV% means the soft book closed at a price more favorable than the Pinnacle fair line.

Authentication

Requires API key. Sharp tier or higher required (closing_lines feature).

Query Parameters

Parameter	Type	Default	Description
`from`	string	7 days ago	Start date (`YYYY-MM-DD` or ISO 8601). Clamped to 30-day window.
`to`	string	today	End date (`YYYY-MM-DD` or ISO 8601).
`group_by`	string	`sportsbook`	Aggregation dimension. One of: `sportsbook`, `sport`, `league`, `market`, `day`.
`sport`	string	all	Filter by sport(s), comma-separated (e.g. `basketball,football`).
`league`	string	all	Filter by league(s), comma-separated (e.g. `nba,nfl`).
`sportsbook`	string	all	Filter by sportsbook(s), comma-separated.

Date Window

Sharp tier allows up to 30 days of history (the current retention window for closing line data). Requesting dates outside this window is clamped silently to the earliest available date.

Example Requests

cURL


# CLV by sportsbook for the past 7 days
curl -X GET "https://api.sharpapi.io/api/v1/historical/clv?from=2026-04-10&to=2026-04-17&group_by=sportsbook" \
  -H "X-API-Key: YOUR_API_KEY"
 
# CLV grouped by sport, NBA only
curl -X GET "https://api.sharpapi.io/api/v1/historical/clv?league=nba&group_by=sport" \
  -H "X-API-Key: YOUR_API_KEY"
 
# CLV grouped by day for DraftKings
curl -X GET "https://api.sharpapi.io/api/v1/historical/clv?sportsbook=draftkings&group_by=day" \
  -H "X-API-Key: YOUR_API_KEY"

JavaScript


const params = new URLSearchParams({
  from: '2026-04-10',
  to: '2026-04-17',
  group_by: 'sportsbook',
});
const response = await fetch(
  `https://api.sharpapi.io/api/v1/historical/clv?${params}`,
  { headers: { 'X-API-Key': 'YOUR_API_KEY' } }
);
const { data, meta } = await response.json();
for (const group of data.groups) {
  console.log(`${group.group_key}: avg CLV ${group.avg_clv_percent.toFixed(2)}% (${group.samples} samples)`);
}

Python


import requests
 
response = requests.get(
    'https://api.sharpapi.io/api/v1/historical/clv',
    params={
        'from': '2026-04-10',
        'to': '2026-04-17',
        'group_by': 'sportsbook',
    },
    headers={'X-API-Key': 'YOUR_API_KEY'}
)
data = response.json()
for group in data['data']['groups']:
    print(
        f"{group['group_key']}: "
        f"avg CLV {group['avg_clv_percent']:.2f}% "
        f"({group['samples']} samples)"
    )

Response

Success (200)


{
  "success": true,
  "data": {
    "group_by": "sportsbook",
    "groups": [
      {
        "group_key": "draftkings",
        "samples": 312,
        "avg_clv_percent": 1.42,
        "avg_abs_clv_percent": 2.18,
        "distinct_books": 1,
        "distinct_sports": 4
      },
      {
        "group_key": "fanduel",
        "samples": 289,
        "avg_clv_percent": 0.87,
        "avg_abs_clv_percent": 1.95,
        "distinct_books": 1,
        "distinct_sports": 4
      },
      {
        "group_key": "betmgm",
        "samples": 201,
        "avg_clv_percent": -0.21,
        "avg_abs_clv_percent": 1.74,
        "distinct_books": 1,
        "distinct_sports": 3
      }
    ],
    "date_range": {
      "from": "2026-04-10T00:00:00.000",
      "to": "2026-04-17T00:00:00.000"
    },
    "total_events": 58,
    "comparable_events": 51,
    "sharp_reference": "pinnacle"
  },
  "meta": {
    "source": "valkey:closing_line",
    "tier_window_days": 30,
    "phase": "1",
    "filters": {
      "sport": null,
      "league": null,
      "sportsbook": null,
      "group_by": "sportsbook"
    },
    "updated_at": "2026-04-17T20:00:00.000000000Z"
  }
}

Empty result (no closing line data yet):


{
  "success": true,
  "data": {
    "group_by": "sportsbook",
    "groups": [],
    "date_range": { "from": "...", "to": "..." },
    "total_events": 0,
    "comparable_events": 0,
    "sharp_reference": "pinnacle"
  },
  "meta": { "source": "valkey:closing_line", "phase": "1", ... }
}

An empty groups array is a valid response when no closing line data has been captured for the requested date range — not an error.

Error Responses

403 Tier Restricted


{
  "error": {
    "code": "tier_restricted",
    "message": "The 'closing_lines' feature requires Sharp or higher.",
    "required_tier": "sharp",
    "docs": "https://docs.sharpapi.io/en/pricing"
  }
}

400 Validation Error


{
  "error": {
    "code": "validation_error",
    "message": "Invalid group_by. Must be one of: day, league, market, sport, sportsbook"
  }
}

Response Fields

`data` object

Field	Type	Description
`group_by`	string	The dimension used for aggregation
`groups`	array	Array of group rows, sorted by `samples` descending
`date_range`	object	Actual `from`/`to` used after tier clamping
`total_events`	integer	Total events found in the date range (before Pinnacle filter)
`comparable_events`	integer	Events with a Pinnacle closing line available for comparison
`sharp_reference`	string	Sharp book used as the fair-odds reference (`"pinnacle"`)

Group row fields

Field	Type	Description
`group_key`	string	The group identifier (sportsbook name, sport, league, market type, or YYYY-MM-DD date)
`samples`	integer	Number of closing line comparisons in this group
`avg_clv_percent`	number	Average CLV% across all samples. Positive = book closed looser than Pinnacle.
`avg_abs_clv_percent`	number	Average absolute CLV% (direction-agnostic market efficiency measure)
`distinct_books`	integer	Number of distinct books in this group
`distinct_sports`	integer	Number of distinct sports in this group

`meta` object

Field	Type	Description
`source`	string	Always `"valkey:closing_line"` (Phase 1 backend)
`tier_window_days`	integer	Maximum lookback days for your tier (Sharp = 30)
`phase`	string	`"1"` — real-time capture from live transitions. ClickHouse backfill planned for Phase 2.
`updated_at`	string	ISO 8601 response timestamp

Interpreting CLV

CLV%	Interpretation
> +2%	Book consistently closed significantly looser than sharp lines — high value captured
+0.5% to +2%	Book closed slightly loose — typical for recreational books
-0.5% to +0.5%	Near-market close — efficient or mixed
< -0.5%	Book tightened into close — may indicate steam chasing or sharp-side action

A positive average CLV on your bets is the strongest indicator that you’re betting into inefficient prices. Over a large sample, positive CLV strongly predicts positive ROI.

Closing Odds by Date — Per-event raw closing prices for a given date
+EV Opportunities — Real-time positive expected value bets
Odds Snapshot — Current pre-match and live odds