Metadata & Context

Get rich contextual data to inform betting decisions including DVP rankings, park factors, injury reports, and market trends.

Endpoints

GET `/marketSelections/{id}/metadata`

Access comprehensive metadata for a market selection including DVP rankings, park factors, and historical performance context.

GET /injuries

Retrieve player injury data with filtering and pagination.

GET /trends

Access recent market trends from the last 48 hours.

DVP Rankings

Defense vs Position (DVP) rankings show how well teams defend against specific positions. Available for NFL, NBA, and NHL.

Basic Usage

import requests

base_url = "https://api.sharpsports.io/v1"
headers = {"Authorization": "Token sk_test_your_key_here"}

# Get metadata with DVP
response = requests.get(
    f"{base_url}/marketSelections/MRKT_def456abc789/metadata",
    headers=headers
)

metadata = response.json()

# Access DVP data
if 'dvp' in metadata:
    dvp = metadata['dvp']

    print(f"DVP Rank: {dvp['rank']}/{dvp['total']}")
    print(f"Fantasy Points Allowed: {dvp['fantasyPointsAllowed']:.1f}")

DVP Structure

{
  "dvp": {
    "rank": 5,
    "total": 30,
    "fantasyPointsAllowed": 42.3,
    "position": "WR",
    "opponent": {
      "id": "TEAM_abc123",
      "abbr": "KC",
      "fullName": "Kansas City Chiefs"
    }
  }
}

Interpreting DVP

Rank 1 = Easiest matchup (allows most fantasy points)
Rank 30 = Hardest matchup (allows fewest fantasy points)
Lower ranks = weaker defense = better for offensive props
Higher ranks = stronger defense = tougher for offensive props

Example Analysis

def analyze_matchup(market_selection_id):
    """Analyze matchup difficulty using DVP"""
    metadata = requests.get(
        f"{base_url}/marketSelections/{market_selection_id}/metadata",
        headers=headers
    ).json()

    if 'dvp' in metadata:
        dvp = metadata['dvp']
        rank = dvp['rank']
        total = dvp['total']

        print(f"\nMatchup Analysis:")
        print(f"Opponent: {dvp['opponent']['fullName']}")
        print(f"DVP Rank: {rank}/{total}")

        # Categorize matchup
        if rank <= total * 0.25:  # Top 25%
            print("✅ EXCELLENT matchup (weak defense)")
        elif rank <= total * 0.50:  # Top 50%
            print("👍 GOOD matchup (below average defense)")
        elif rank <= total * 0.75:  # Top 75%
            print("⚠️ TOUGH matchup (above average defense)")
        else:
            print("❌ VERY TOUGH matchup (strong defense)")

        print(f"Avg Fantasy Points Allowed: {dvp['fantasyPointsAllowed']:.1f}")

analyze_matchup("MRKT_def456abc789012345678901234567")

Park Factors

MLB park factors indicate how park conditions affect scoring. Values above 1.0 favor hitters, below 1.0 favor pitchers.

Basic Usage

# Get park factor for MLB market
response = requests.get(
    f"{base_url}/marketSelections/{mrkt_id}/metadata",
    headers=headers
).json()

if 'parkFactor' in response:
    pf = response['parkFactor']

    print(f"Park Factor: {pf:.2f}")

    if pf > 1.05:
        print("⚾ Hitter-friendly park")
    elif pf < 0.95:
        print("⚾ Pitcher-friendly park")
    else:
        print("⚾ Neutral park")

Park Factor Structure

{
  "parkFactor": 1.12,
  "venue": {
    "id": "VENU_abc123",
    "name": "Coors Field",
    "city": "Denver"
  }
}

Example: Adjusting Expectations

def adjust_for_park(market_selection_id, player_avg):
    """Adjust player average based on park factor"""
    metadata = requests.get(
        f"{base_url}/marketSelections/{market_selection_id}/metadata",
        headers=headers
    ).json()

    park_factor = metadata.get('parkFactor', 1.0)

    # Adjust player average
    adjusted_avg = player_avg * park_factor

    print(f"\nPark-Adjusted Analysis:")
    print(f"Player Average: {player_avg:.1f}")
    print(f"Park Factor: {park_factor:.2f}")
    print(f"Adjusted Average: {adjusted_avg:.1f}")
    print(f"Adjustment: {adjusted_avg - player_avg:+.1f}")

    return adjusted_avg

# Example: Player averages 2.5 hits per game
adjust_for_park("MRKT_def456abc789", player_avg=2.5)

Injury Data

Access comprehensive injury reports with player status and game participation data.

Basic Usage

# Get all injuries for a league
response = requests.get(
    f"{base_url}/injuries",
    headers=headers,
    params={"limit": 50}
)

injuries = response.json()

for injury in injuries:
    player = injury['player']['fullName']
    status = injury['status']
    description = injury.get('description', 'N/A')

    print(f"{player}: {status} - {description}")

Filter by Player

# Get injuries for specific player
response = requests.get(
    f"{base_url}/injuries",
    headers=headers,
    params={"player": "PLYR_abc123def456"}
).json()

Filter by Team

# Get injuries for team
response = requests.get(
    f"{base_url}/injuries",
    headers=headers,
    params={"team": "TEAM_lakers"}
).json()

Filter by Event

# Get injuries for an event
response = requests.get(
    f"{base_url}/injuries",
    headers=headers,
    params={"event": "EVNT_xyz789abc012"}
).json()

Injury Status Values

OUT - Player will not play
DOUBTFUL - Player unlikely to play
QUESTIONABLE - Player's status uncertain
PROBABLE - Player likely to play
DAY_TO_DAY - Status being monitored daily

Example: Check Team Health

def check_team_health(team_id):
    """Check injury status for a team"""
    injuries = requests.get(
        f"{base_url}/injuries",
        headers=headers,
        params={"team": team_id}
    ).json()

    print(f"\nInjury Report:")
    print("-" * 60)

    status_counts = {}

    for injury in injuries:
        player = injury['player']['fullName']
        status = injury['status']
        played = injury.get('played', False)

        status_counts[status] = status_counts.get(status, 0) + 1

        status_icon = "🔴" if status == "OUT" else "🟡" if status in ["DOUBTFUL", "QUESTIONABLE"] else "🟢"
        played_text = "(played)" if played else ""

        print(f"{status_icon} {player:25} {status:15} {played_text}")

    print(f"\nSummary:")
    for status, count in status_counts.items():
        print(f"  {status}: {count}")

check_team_health("TEAM_lakers")

Market Trends

Access trending markets from the last 48 hours - markets showing interesting movements or patterns.

Basic Usage

# Get recent trends
response = requests.get(
    f"{base_url}/trends",
    headers=headers,
    params={"limit": 20}
)

trends = response.json()

for trend in trends:
    print(f"{trend['player']['fullName']}: {trend['proposition']}")
    print(f"  Event: {trend['event']['name']}")
    print(f"  Updated: {trend['timeUpdated']}")

Filter by League

# Get NBA trends only
response = requests.get(
    f"{base_url}/trends",
    headers=headers,
    params={"league": "nba"}
).json()

Filter by Sport

# Get all basketball trends
response = requests.get(
    f"{base_url}/trends",
    headers=headers,
    params={"sport": "basketball"}
).json()

Example: Monitor Hot Markets

def monitor_trends():
    """Monitor trending markets"""
    trends = requests.get(
        f"{base_url}/trends",
        headers=headers,
        params={"limit": 10}
    ).json()

    print("\n🔥 Trending Markets (Last 48h):")
    print("-" * 60)

    for i, trend in enumerate(trends, 1):
        player = trend.get('player', {}).get('fullName', 'N/A')
        team = trend.get('team', {}).get('abbr', 'N/A')
        prop = trend['proposition']
        event = trend['event']['name']

        print(f"{i}. {player} ({team}) - {prop}")
        print(f"   {event}")

monitor_trends()

Complete Metadata Analysis

Combine all metadata sources for comprehensive market analysis:

def complete_market_analysis(market_selection_id):
    """Comprehensive market analysis with all metadata"""
    print("\n" + "="*70)
    print("COMPLETE MARKET ANALYSIS")
    print("="*70)

    # Get metadata
    metadata = requests.get(
        f"{base_url}/marketSelections/{market_selection_id}/metadata",
        headers=headers,
        params={
            "playerAggStats": "true",
            "teamAggStats": "true"
        }
    ).json()

    # Basic info
    player = metadata.get('player', {})
    event = metadata.get('event', {})

    print(f"\n📋 Market Info:")
    print(f"  Player: {player.get('fullName', 'N/A')}")
    print(f"  Prop: {metadata['proposition']}")
    print(f"  Event: {event.get('name', 'N/A')}")

    # Player aggregate stats
    if 'playerAggregateStats' in metadata:
        player_stats = metadata['playerAggregateStats']
        print(f"\n📊 Season Stats:")
        print(f"  Average: {player_stats.get('average', 0):.1f}")
        print(f"  Games: {player_stats.get('games', 0)}")

    # DVP analysis
    if 'dvp' in metadata:
        dvp = metadata['dvp']
        print(f"\n🎯 Matchup (DVP):")
        print(f"  Opponent: {dvp['opponent']['fullName']}")
        print(f"  Rank: {dvp['rank']}/{dvp['total']}")
        print(f"  Avg Allowed: {dvp.get('fantasyPointsAllowed', 0):.1f}")

        # Matchup rating
        pct = dvp['rank'] / dvp['total']
        if pct <= 0.33:
            print(f"  Rating: ✅ FAVORABLE")
        elif pct >= 0.67:
            print(f"  Rating: ❌ DIFFICULT")
        else:
            print(f"  Rating: ⚠️ NEUTRAL")

    # Park factor (MLB)
    if 'parkFactor' in metadata:
        pf = metadata['parkFactor']
        print(f"\n⚾ Park Factor:")
        print(f"  Factor: {pf:.2f}")
        print(f"  Type: {'Hitter-friendly' if pf > 1.05 else 'Pitcher-friendly' if pf < 0.95 else 'Neutral'}")

    # Check for injuries
    if player:
        injuries = requests.get(
            f"{base_url}/injuries",
            headers=headers,
            params={"player": player['id']}
        ).json()

        if injuries:
            print(f"\n🏥 Injury Status:")
            for injury in injuries:
                print(f"  Status: {injury['status']}")
                if 'description' in injury:
                    print(f"  Details: {injury['description']}")

    print("\n" + "="*70)

complete_market_analysis("MRKT_def456abc789012345678901234567")

Query Parameters Reference

`/marketSelections/{id}/metadata`

Parameter	Type	Description
`teamAggStats`	boolean	Include team aggregate statistics
`filteredTeamAggStats`	boolean	Include filtered team aggregate stats
`playerAggStats`	boolean	Include player aggregate statistics
`filteredPlayerAggStats`	boolean	Include filtered player aggregate stats
`teamAggStatsSeason`	string	Comma-separated years for team stats
`playerAggStatsSeason`	string	Comma-separated years for player stats

/injuries

Parameter	Type	Description
`player`	string	Filter by player ID or name (comma-separated)
`team`	string	Filter by team ID or name (comma-separated)
`event`	string	Filter by event ID (comma-separated)
`status`	string	Filter by injury status
`played`	boolean	Filter by whether player played (true/false)
`limit`	number	Limit results
`pageNum`	number	Page number for pagination
`pageSize`	number	Results per page

/trends

Parameter	Type	Description
`league`	string	Filter by league ID or abbreviation
`sport`	string	Filter by sport ID or name
`limit`	number	Limit results
`pageNum`	number	Page number for pagination
`pageSize`	number	Results per page

Need Help?

📧 Support: [email protected]
📚 API Reference: /reference
💬 Discord: Join our developer community

Metadata & Context

Endpoints

GET /marketSelections/{id}/metadata

GET /injuries

GET /trends

DVP Rankings

Basic Usage

DVP Structure

Interpreting DVP

Example Analysis

Park Factors

Basic Usage

Park Factor Structure

Example: Adjusting Expectations

Injury Data

Basic Usage

Filter by Player

Filter by Team

Filter by Event

Injury Status Values

Example: Check Team Health

Market Trends

Basic Usage

Filter by League

Filter by Sport

Example: Monitor Hot Markets

Complete Metadata Analysis

Query Parameters Reference

/marketSelections/{id}/metadata

/injuries

/trends

Need Help?

GET `/marketSelections/{id}/metadata`

`/marketSelections/{id}/metadata`