📊 Sports Betting Lines API (Python Usage Guide)

Unlock structured betting line data for NCAA, NBA, NHL, top European soccer leagues, and more—perfect for trend analysis, predictive modeling, live game dashboards, and visualizations of odds movement. Whether you're building a custom betting app, evaluating public betting sentiment, or creating your own edge through historical line movements, this API gives you direct access to normalized, cleaned betting data from DraftKings Sports across time.

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🔐 1. Authentication

API Endpoint:
https://amazonpricesalesdata.azure-api.net/basketball-betting-line-data/GetBasketballLinesData

Required Headers:

• Ocp-Apim-Subscription-Key: Your API Key
• Content-Type: application/json

📎 2. Request Format

Use a POST request with this JSON body:

{
  "query": "YOUR SQL-STYLE COSMOS DB QUERY",
  "sport": "basketball | soccer | hockey"
}

⚠️ 3. Query Limits

• Must query by individual day using c.Timestamp to stay under Cosmos DB limits.
• Do not query across multiple days at once.
• Recommended pattern:

WHERE c.Timestamp >= 'MM-DD-YYYY 00:00:00'
AND c.Timestamp < 'MM-DD-YYYY 00:00:00'

⚙️ 4. Supported Sports & Leagues

Sport	League
Basketball	NCAA (M)
Basketball	NCAA (W)
Basketball	NBA
Basketball	WNBA
Basketball	Euroleague
Basketball	Brazil - NBB
Basketball	British - SLB
Hockey	NHL
Soccer	English Premier League
Soccer	Germany - Bundesliga
Soccer	Spain - La Liga
Soccer	Italy - Serie A
Soccer	France - Ligue 1
Soccer	Champions League
Soccer	Europa League

📚 5. Queryable Columns

• Sport, League, Game_Date, Game_Time, Game_Status, Game_Period
• Timestamp: Server-side collection timestamp
• Away_Team, Home_Team, Matchup_Id, Game_Link
• Away_Team_Score, Home_Team_Score
• Away_Team_Moneyline, Home_Team_Moneyline
• Away_Team_Spread, Home_Team_Spread (Not available for soccer)
• Away_Team_Spread_Points, Home_Team_Spread_Points (Not available for soccer)
• Over_Under, Over_Points, Under_Points (Not available for soccer)
• Draw_MoneyLine (Soccer only)

Note: Spread and Over/Under values do not exist for soccer matches and will not be returned even if included in your query.

🐍 6. Full Python API Example

import pandas as pd
import requests
from datetime import datetime, timedelta

def fetch_data_for_day(day, league, sport):
    url = "https://amazonpricesalesdata.azure-api.net/basketball-betting-line-data/GetBasketballLinesData"
    headers = {
        "Ocp-Apim-Subscription-Key": "YOUR_API_KEY",
        "Content-Type": "application/json"
    }
    query = f"""
    SELECT c.Sport, c.League, c.Timestamp, c.Game_Date,
           c.Away_Team, c.Home_Team, c.Matchup_Id, c.Game_Link,
           c.Away_Team_Spread, c.Home_Team_Spread,
           c.Away_Team_Moneyline, c.Home_Team_Moneyline
    FROM c
    WHERE c.League = '{league}'
    AND c.Timestamp >= '{day.strftime('%m-%d-%Y')} 00:00:00'
    AND c.Timestamp < '{(day + timedelta(days=1)).strftime('%m-%d-%Y')} 00:00:00'
    """
    payload = {"query": query, "sport": sport.lower()}
    response = requests.post(url, headers=headers, json=payload)
    return pd.DataFrame(response.json())

# Replace this with the latest date you want to collect data from (2/23/2025 being the oldest date)
latest_timestamp = pd.to_datetime('2/23/2025')
today = pd.Timestamp.today().normalize()
latest_day = latest_timestamp.normalize()

day_range = pd.date_range(start=latest_day - timedelta(days=1), end=today, freq='D') if latest_day < today else [latest_day]

new_data_frames = []
for day in day_range:
    try:
        df_day = fetch_data_for_day(day, "NCAA (M)", "Basketball")
        df_day['Timestamp'] = pd.to_datetime(df_day['Timestamp'])
        df_day['unique_id'] = df_day['Timestamp'].astype(str) + df_day['Game_Link']
        new_data_frames.append(df_day)
    except:
        print('No data for day:', day)

df_new = pd.concat(new_data_frames, ignore_index=True) if new_data_frames else pd.DataFrame()
df_new.to_csv('saved_ncaaM.csv', index=False)

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