+EV Opportunities

Find positive expected value betting opportunities across all sportsbooks.


GET /api/v1/opportunities/ev

This endpoint replaces the former /positive-ev and /value-bets endpoints. All scoring fields (confidence_score, kelly_fraction, fair_probability) are now included in every response.

Authentication

Requires API key. Pro tier or higher required. Your account must have the ev feature enabled.

Query Parameters

Parameter	Type	Default	Description
`sport`	string	all	Filter by sport(s), comma-separated (e.g. `basketball`, `football`, `ice_hockey`)
`league`	string	all	Filter by league(s), comma-separated (e.g. `nba`, `nfl`, `nhl`)
`sportsbook`	string	tier-allowed	Filter by sportsbook(s), comma-separated. Tier limits enforced.
`market`	string	all	Filter by market type(s), comma-separated. Supports categories (`spread`, `total`, `moneyline`, `props`) or exact types (`point_spread`, `player_points`).
`event`	string	all	Filter by game/event ID(s), comma-separated
`min_ev`	number	0	Minimum EV percentage threshold
`max_ev`	number	—	Maximum EV percentage threshold
`live`	boolean	—	`true` = live only, `false` = prematch only, omit = both
`min_odds`	number	—	Minimum American odds (e.g., `-200`)
`max_odds`	number	—	Maximum American odds (e.g., `+500`)
`min_market_width`	number	—	Minimum market width (vig indicator). Lower width = sharper market.
`max_market_width`	number	—	Maximum market width
`max_odds_age`	number	—	Maximum odds age in seconds. Filters out stale opportunities where the underlying odds are older than this threshold.
`date_range`	string	—	Filter by event date: `today`, `tomorrow`, or `week`. Dates are evaluated in US Eastern Time (ET).
`sort`	string	`-ev`	Sort field. Options: `ev`, `confidence`/`confidence_score`, `kelly`/`kelly_fraction`, `time`/`start_time`, `book_count`/`books`. Prefix with `-` for descending.
`limit`	integer	50	Results per page (max 500)
`offset`	integer	0	Pagination offset (max 5000)

Filtering Multiple Values

Use comma-separated values for multi-select filters:


?sportsbook=draftkings,fanduel&league=nba,nfl

Example Requests

cURL


# Basic EV opportunities for NBA
curl -X GET "https://api.sharpapi.io/api/v1/opportunities/ev?league=nba&min_ev=2" \
  -H "X-API-Key: YOUR_API_KEY"

JavaScript


const response = await fetch(
  'https://api.sharpapi.io/api/v1/opportunities/ev?league=nba&min_ev=2',
  { headers: { 'X-API-Key': 'YOUR_API_KEY' } }
);
const { data, meta } = await response.json();
console.log(`Found ${meta.count} +EV opportunities`);
console.log(`Average EV: ${meta.summary.avg_ev}%`);

Python


import requests
 
response = requests.get(
    'https://api.sharpapi.io/api/v1/opportunities/ev',
    params={
        'league': 'nba',
        'min_ev': 2,
    },
    headers={'X-API-Key': 'YOUR_API_KEY'}
)
data = response.json()
for opp in data['data']:
    print(f"{opp['selection']} @ {opp['sportsbook']}: {opp['ev_percent']}% EV")

Response

Success (200)


{
  "success": true,
  "data": [
    {
      "id": "ev_dk_nba_33483153_ml_PHO",
      "game_id": "evt_nba_phi_pho_20260208",
      "ev_percent": 4.2,
      "odds_american": -105,
      "odds_decimal": 1.952,
      "no_vig_odds": -118,
      "fair_probability": 0.541,
      "market_width": 3.2,
      "devig_method": "power",
      "devig_book": "pinnacle",
      "selection": "PHO Suns",
      "market": "moneyline",
      "line": null,
      "sportsbook": "draftkings",
      "game": "PHI 76ers vs PHO Suns",
      "sport": "basketball",
      "league": "nba",
      "home_team": "PHI 76ers",
      "away_team": "PHO Suns",
      "start_time": "2026-02-08T19:00:00Z",
      "is_live": false,
      "confidence_score": 87,
      "kelly_fraction": 0.021,
      "book_count": 5,
      "arb_available": false,
      "arb_profit": null,
      "is_player_prop": false,
      "player_name": null,
      "stat_category": null,
      "possibly_stale": false,
      "oldest_odds_age_seconds": null,
      "warnings": [],
      "detected_at": "2026-02-08T14:22:10.456Z"
    }
  ],
  "pagination": {
    "limit": 50,
    "offset": 0,
    "total": 12,
    "maxOffset": 5000
  },
  "meta": {
    "source": "redis",
    "last_update": "2026-02-08T14:22:10.456Z",
    "summary": {
      "count": 12,
      "avg_ev": 3.8,
      "max_ev": 7.1,
      "by_sportsbook": {
        "draftkings": 5,
        "fanduel": 4,
        "betmgm": 3
      },
      "by_sport": {
        "basketball": 12
      },
      "by_market": {
        "moneyline": 5,
        "point_spread": 4,
        "player_points": 3
      }
    },
    "filters": {
      "sport": null,
      "league": ["nba"],
      "sportsbook": null,
      "market": null,
      "min_ev": 2.0,
      "max_ev": null,
      "live": null,
      "sort": "-ev",
      "include": null
    }
  }
}

Response Headers


X-RateLimit-Limit: 300
X-RateLimit-Remaining: 298
X-RateLimit-Reset: 1707401000
X-Data-Delay: 0
X-Request-Id: req_abc123def456

Error Responses

401 Unauthorized


{
  "error": {
    "code": "unauthorized",
    "message": "Invalid or missing API key",
    "docs": "https://sharpapi.io/docs/authentication"
  }
}

403 Feature Required


{
  "error": {
    "code": "feature_required",
    "message": "The 'ev' feature is required. Upgrade to Pro or higher.",
    "docs": "https://sharpapi.io/docs/pricing"
  }
}

429 Rate Limited


{
  "error": {
    "code": "rate_limited",
    "message": "Rate limit exceeded",
    "docs": "https://sharpapi.io/docs/api-reference/overview"
  }
}

Response Fields

Core Fields

Field	Type	Description
`id`	string	Unique opportunity identifier (hash)
`game_id`	string\|null	Associated game/event identifier
`external_event_id`	string\|null	Sportsbook’s native event ID
`selection_id`	string\|null	Sportsbook’s native outcome/selection ID (for deep linking to the bet slip)
`ev_percent`	number	Expected value as a percentage (e.g., `4.2` = 4.2% EV)
`odds_american`	number	Current American odds at the sportsbook
`odds_decimal`	number	Decimal odds (e.g., `1.952`)
`no_vig_odds`	number\|null	Fair (no-vig) American odds derived from the sharp line
`fair_probability`	number\|null	No-vig (devigged) fair probability (0.0 to 1.0)
`market_width`	number\|null	Width of the market (vig indicator)
`devig_method`	string	De-vig method used (e.g., `power`)
`devig_book`	string	Sharp book used as reference (e.g., `pinnacle`)
`selection`	string	The selection (team name, Over/Under, player, etc.)
`market`	string	Market type (`moneyline`, `point_spread`, `total_points`, etc.)
`line`	number\|null	Spread/total line (e.g., `-3.5`, `220.5`)
`sportsbook`	string	Sportsbook offering these odds
`game`	string	Human-readable game name
`sport`	string	Sport identifier (lowercase)
`league`	string	League identifier
`home_team`	string\|null	Home team name
`away_team`	string\|null	Away team name
`start_time`	string\|null	ISO 8601 event start time
`is_live`	boolean	Whether the event is currently live
`confidence_score`	number	Multi-factor confidence score (0-100)
`kelly_fraction`	number\|null	Kelly criterion optimal bet fraction (0.0 to 1.0)
`book_count`	number	Number of sportsbooks offering this market
`arb_available`	boolean	Whether an arbitrage exists on this market
`arb_profit`	number\|null	Arbitrage profit percentage if available
`is_player_prop`	boolean	Whether this is a player prop market
`player_name`	string\|null	Player name (if player prop)
`stat_category`	string\|null	Stat type (if player prop, e.g., `points`, `rebounds`)
`possibly_stale`	boolean	`true` if underlying odds may have moved since detection
`oldest_odds_age_seconds`	number\|null	Age of the stalest odds used in the EV calculation (seconds)
`warnings`	string[]	Data quality warnings (e.g., `POTENTIALLY_STALE_ODDS`, `LIVE_STALE_ODDS`)
`detected_at`	string	ISO 8601 timestamp when the +EV was first detected

Meta Summary Fields

Field	Type	Description
`summary.count`	number	Total opportunities matching filters
`summary.avg_ev`	number	Average EV across all results
`summary.max_ev`	number	Highest EV found
`summary.by_sportsbook`	object	Opportunity count per sportsbook
`summary.by_sport`	object	Opportunity count per sport
`summary.by_market`	object	Opportunity count per market type

Understanding Expected Value

Expected Value (EV) measures the average profit or loss per bet over time. A positive EV (+EV) bet means you have a mathematical edge over the sportsbook.


EV% = (fair_probability x decimal_odds - 1) x 100

Where:

fair_probability = No-vig probability derived from the sharp book (Pinnacle)
decimal_odds = The decimal odds offered by the soft book

How SharpAPI Calculates EV

SharpAPI uses Pinnacle (a sharp book with efficient odds) as the source of truth for fair probability.

Step 1: Get the sharp book odds


Pinnacle: Team A -115 / Team B +105

Step 2: Remove the vig to find fair probability (Power method)


// Implied probabilities (with vig)
probA = 1 / 1.87 = 0.535  // -115 in decimal = 1.87
probB = 1 / 2.05 = 0.488  // +105 in decimal = 2.05
total = 1.023              // 2.3% vig
 
// Power devig: solve for k where probA^k + probB^k = 1
// k ≈ 1.036 for this market
fairProbA = 0.535^1.036 = 0.522 (52.2%)
fairProbB = 0.488^1.036 = 0.478 (47.8%)

Step 3: Compare to a soft book


DraftKings: Team A -105 (decimal 1.952)
Fair probability: 52.3%

EV% = (0.523 x 1.952 - 1) x 100
EV% = +2.1%

A +2.1% EV means you expect to profit $2.10 for every $100 wagered on this bet over time.

Why This Works

Book Type	Characteristics
Sharp (Pinnacle)	Low vig, efficient odds, accurate probabilities
Soft (DraftKings, FanDuel, BetMGM)	Higher vig, slower to adjust, exploitable

When soft books are slow to update their odds after a market move, their implied probability deviates from reality, creating a +EV opportunity.

EV Thresholds

EV %	Quality	Suggested Action
< 0%	Negative EV	Avoid
0 - 2%	Marginal	Only viable at high volume
2 - 5%	Good	Standard profitable threshold
5%+	Excellent	High confidence opportunities

We recommend setting min_ev=2 for most use cases. Marginal EV (below 2%) can be eroded by line movement before you place the bet.

Kelly Criterion

The kelly_fraction field tells you the optimal fraction of your bankroll to wager according to the Kelly criterion (0.0 to 1.0, e.g. 0.021 = 2.1%):


Kelly% = (edge / odds_to_1) = (fair_prob x decimal_odds - 1) / (decimal_odds - 1)

Kelly Sizing Guide

Kelly %	Risk Level	Recommendation
< 1%	Low	Small edge, consider skipping
1 - 3%	Moderate	Standard bet size
3 - 5%	Aggressive	Strong edge, but size carefully
5%+	Very aggressive	Excellent edge; consider fractional Kelly (half or quarter)

Variance Warning: +EV does not guarantee profit on every bet. Over 100 bets at 5% EV, actual results can range widely. The edge only materializes over hundreds or thousands of bets. Never bet more than you can afford to lose.

Best Practices

Set a minimum EV threshold - Use min_ev=2 or higher to focus on meaningful edges
Use Kelly sizing - Use kelly_fraction to determine bet size (multiply by bankroll)
Filter by confidence - Use confidence_score to prioritize high-confidence opportunities
Monitor market_width - Narrow markets (low width) indicate more efficient pricing and more reliable EV calculations
Act quickly - +EV opportunities are fleeting; lines move fast
Track your results - Log every bet and compare actual ROI to expected EV over time
Use fractional Kelly - Most professionals use quarter or half Kelly to reduce variance

Arbitrage Opportunities - Guaranteed profit across books
Middles - Line discrepancy opportunities
Best Odds - Find the best price across all books
EV Calculation Concepts - Deep dive into the math