Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.fieldfunded.com/llms.txt

Use this file to discover all available pages before exploring further.

Web Scraping Sports Odds vs Using an API

If you need sports odds data, you have two options: scrape betting sites or use a dedicated API. This article compares both with real code, real numbers, and real trade-offs — so you can pick the right approach for your project. Short answer: Scraping works for one-off research. For anything production-grade — bots, apps, dashboards — an API saves you weeks of maintenance.

Side-by-Side Comparison

FactorWeb ScrapingOdds API
Setup time2-8 hours5 minutes
MaintenanceWeekly fixes (selectors break)Zero
Data formatRaw HTML → parse yourselfStructured JSON
Coverage1 site at a time30+ sports, 1,000+ markets
Reliability~70% uptime (CAPTCHAs, bans)99.9% uptime SLA
Legal riskTOS violations, possible lawsuitsLicensed, legal
Latency2-10 seconds per page<300ms per request
CostFree (but your time)Free tier available
Settlement dataNot availableAutomatic (won/lost/refund)

The Scraping Approach

Here is what a basic odds scraper looks like with Python and BeautifulSoup:
# odds_scraper.py — Scrape odds from a public site
# WARNING: This may violate the site's TOS

import requests
from bs4 import BeautifulSoup

url = "https://example-odds-site.com/soccer/premier-league"
headers = {"User-Agent": "Mozilla/5.0"}

response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")

# These selectors WILL break when the site updates
matches = soup.select(".match-row")

for match in matches:
    home = match.select_one(".team-home").text.strip()
    away = match.select_one(".team-away").text.strip()
    odds_1 = match.select_one(".odds-home").text.strip()
    odds_x = match.select_one(".odds-draw").text.strip()
    odds_2 = match.select_one(".odds-away").text.strip()

    print(f"{home} vs {away}: {odds_1} / {odds_x} / {odds_2}")

Problems You Will Hit

  1. Selectors break: The site redesigns, and .match-row becomes .event-card. Your scraper is down until you fix it.
  2. JavaScript rendering: Many odds sites use React/Angular. requests.get() returns an empty page. You need Selenium or Playwright, which are 10x slower.
  3. CAPTCHAs and rate limits: After 50-100 requests, you get blocked. Now you need proxies ($20-50/month).
  4. IP bans: Rotate proxies, solve CAPTCHAs, handle cookies — your 20-line script becomes 500 lines of infrastructure code.
  5. No settlement data: Scrapers can tell you what the odds ARE, but not whether a bet WON. You need a separate data source for results.

The API Approach

The same data, structured and reliable:
# odds_api.py — Get odds via FieldFunded API
import requests

API_KEY = "your_api_key_here"
BASE = "https://api.fieldfunded.com/v1"
H = {"X-API-Key": API_KEY}

# Get all Premier League events
events = requests.get(
    f"{BASE}/events",
    headers=H,
    params={"sport": "soccer", "league": "england_epl"}
).json()

for event in events["events"]:
    # Get full odds (all markets)
    odds = requests.get(
        f"{BASE}/events/{event['id']}/odds",
        headers=H
    ).json()

    print(f"\n{event['home_team']} vs {event['away_team']}")
    print(f"  {len(odds['markets'])} markets available")

    # Show match winner
    for market in odds["markets"]:
        if "1x2" in market["name"].lower():
            for sel in market["selections"]:
                print(f"  {sel['name']}: {sel['odds']}")

What You Get

  • 30+ sports (soccer, basketball, tennis, NFL, NBA, esports)
  • 1,000+ markets per event (match winner, over/under, player props, corners, cards)
  • Live scores, fixtures, and team logos included
  • Automatic bet settlement — send a bet, get won/lost/refund back
  • No proxies, no CAPTCHAs, no selector maintenance

Cost Comparison

ApproachMonthly costYour time
Scraping (no proxies)$04-8 hours/month fixing breakage
Scraping (with proxies)$20-502-4 hours/month
FieldFunded Free tier$00 hours
FieldFunded Starter$290 hours
The free tier includes 10,000 requests/month — enough for most personal projects, bots, and prototypes. See the full pricing comparison.

When Scraping Still Makes Sense

Be honest — scraping is the right choice in some cases:
  • One-off research: You need odds data once for an analysis, and you do not need reliability
  • Niche sites: The data you need is not available in any API (e.g., prop betting on obscure regional leagues)
  • Learning: You want to practice web scraping as a skill
For everything else — bots, apps, dashboards, production systems — use an API.

Hybrid Approach

Some developers use both: an API for core data and scraping for supplementary data:
import requests

API_KEY = "your_api_key_here"
BASE = "https://api.fieldfunded.com/v1"
H = {"X-API-Key": API_KEY}

# Primary: structured API data
odds = requests.get(
    f"{BASE}/events/{event_id}/odds",
    headers=H
).json()

# Use API odds as ground truth
match_winner = None
for m in odds["markets"]:
    if "winner" in m["name"].lower():
        match_winner = {s["name"]: s["odds"] for s in m["selections"]}

print("Match Winner Odds:", match_winner)

Migration Checklist (Scraper → API)

If you currently have a scraper and want to switch:
  • Sign up for a free API key
  • Map your scraped fields to API response fields
  • Replace BeautifulSoup selectors with API calls
  • Remove proxy infrastructure
  • Add settlement checks (new capability you did not have before)
  • Delete your CAPTCHA-solving code

Get Your Free API Key

Replace your scraper in 5 minutes — 10,000 free requests/month

See Pricing

All plans compared side by side