Sports betting platforms hold a goldmine of data. Real-time odds, historical trends, player statistics, and live event updates can power predictive models, arbitrage strategies, and analytics dashboards.
In this guide, you'll learn how to scrape sports betting sites using Python, bypass anti-bot protections, and build scrapers that actually work in 2026. We'll cover everything from basic HTTP requests to advanced browser automation techniques.
What is Sports Betting Data Scraping?
When you scrape sports betting sites, you extract odds, match details, and betting lines from bookmaker websites automatically. Instead of manually copying data, scripts collect information at scale.
This data powers several use cases. You can build arbitrage calculators that spot profitable odds differences across bookmakers. Predictive models use historical odds to forecast match outcomes. Analytics platforms aggregate odds from dozens of sources into unified dashboards.
The challenge? Betting sites actively fight scrapers with sophisticated anti-bot measures.
Why Scrape Sports Betting Sites?
The sports betting industry generates massive amounts of real-time data. Here's why extracting it matters.
Arbitrage Betting
Price discrepancies exist between bookmakers. When Bet365 offers 2.10 on Team A and another bookie offers 2.05 on Team B, you can bet both outcomes and guarantee profit.
Manually finding these opportunities is nearly impossible. Odds change every second. Automated scraping lets you monitor dozens of bookmakers simultaneously and catch arbitrage windows before they close.
Predictive Modeling
Machine learning models need training data. Historical odds combined with match outcomes help algorithms learn patterns.
Scraping gives you the raw material. You can collect opening odds, closing odds, line movements, and final scores across thousands of matches. This dataset becomes the foundation for prediction systems.
Market Analysis
Odds reflect collective market wisdom. Sharp bettors move lines. Tracking these movements reveals where smart money flows.
You might notice odds on a specific team shortening dramatically. This signals insider information or significant betting activity. Without scraping, you'd miss these signals entirely.
Legal Considerations for 2026
Let's address the elephant in the room. Is it legal to scrape sports betting sites?
The short answer: it depends.
Scraping publicly available data is generally legal. Courts have ruled that accessing publicly displayed information doesn't violate computer fraud laws.
However, several factors complicate this.
Terms of Service violations can lead to account bans and legal threats. Most betting sites explicitly prohibit automated data collection. While ToS violations aren't criminal, they create civil liability.
GDPR and CCPA apply when scraping involves personal data. Odds and match information typically don't qualify as personal data, but user-generated content might.
Rate limiting exists for a reason. Hammering a server with thousands of requests can constitute a denial-of-service attack. Keep your request frequency reasonable.
Best practices:
- Only scrape publicly visible data
- Respect robots.txt directives
- Implement reasonable rate limits
- Don't bypass authentication systems
- Consider using official APIs when available
Some bookmakers offer legitimate API access. FanDuel, DraftKings, and Betfair provide developer APIs. These are always preferable to scraping when available.
Method 1: HTTP Requests with Hidden APIs
The fastest scraping approach bypasses browser rendering entirely. Most betting sites load data through internal APIs. Finding these endpoints lets you grab JSON data directly.
Finding Hidden API Endpoints
Open your browser's Developer Tools. Navigate to the Network tab. Load a betting page and watch the requests fly by.
Filter by XHR or Fetch requests. Look for responses containing JSON with odds data. The URL pattern often reveals the API structure.
For example, many sites use endpoints like:
/api/sports/events?sport=football®ion=europe
/v2/odds/upcoming?market=1x2
Once you identify the endpoint, you can request it directly.
Building a Simple Odds Scraper
Here's how to fetch odds data using Python's requests library:
import requests
import json
from datetime import datetime
class OddsScraper:
def __init__(self):
self.session = requests.Session()
self.session.headers.update({
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
'Accept': 'application/json',
'Accept-Language': 'en-US,en;q=0.9'
})
This creates a session with browser-like headers. The User-Agent string mimics a real Chrome browser.
def fetch_odds(self, api_url):
try:
response = self.session.get(api_url, timeout=10)
response.raise_for_status()
return response.json()
except requests.RequestException as e:
print(f"Request failed: {e}")
return None
The fetch_odds method handles the actual request. It includes timeout handling and error management.
def parse_events(self, raw_data):
events = []
for match in raw_data.get('events', []):
event = {
'home_team': match.get('home'),
'away_team': match.get('away'),
'start_time': match.get('commence_time'),
'odds': {
'home_win': match.get('odds', {}).get('h2h', [])[0] if match.get('odds') else None,
'draw': match.get('odds', {}).get('h2h', [])[1] if len(match.get('odds', {}).get('h2h', [])) > 2 else None,
'away_win': match.get('odds', {}).get('h2h', [])[-1] if match.get('odds') else None
}
}
events.append(event)
return events
Parsing extracts the fields you actually need. API responses often contain excess data. This method filters down to essentials.
Handling Rate Limits
Betting sites track request patterns. Sending 100 requests per second triggers immediate blocking.
import time
import random
def scrape_with_delay(urls, min_delay=1, max_delay=3):
results = []
for url in urls:
data = fetch_odds(url)
if data:
results.append(data)
# Random delay between requests
delay = random.uniform(min_delay, max_delay)
time.sleep(delay)
return results
Random delays make your scraper look more human. Consistent timing patterns scream "bot."
Method 2: Browser Automation with Selenium
Some betting sites don't expose clean APIs. They render everything client-side with JavaScript. For these targets, you need browser automation.
Selenium launches a real browser and controls it programmatically. The site sees a genuine Chrome instance rather than a Python script.
Setting Up Selenium
Install the required packages:
pip install selenium webdriver-manager
The webdriver-manager package automatically downloads the correct ChromeDriver version.
Basic Selenium Scraper
Here's a complete scraper for OddsPortal, a popular odds comparison site:
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from webdriver_manager.chrome import ChromeDriverManager
import json
import time
These imports cover browser control, element location, and waiting for page loads.
def create_driver(headless=True):
options = Options()
if headless:
options.add_argument('--headless')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument('--disable-blink-features=AutomationControlled')
options.add_experimental_option('excludeSwitches', ['enable-automation'])
service = Service(ChromeDriverManager().install())
driver = webdriver.Chrome(service=service, options=options)
return driver
The --disable-blink-features=AutomationControlled flag removes the "controlled by automation" banner. This helps avoid detection.
def scrape_oddsportal(sport='basketball', league='usa/nba'):
driver = create_driver(headless=True)
url = f'https://www.oddsportal.com/{sport}/{league}/'
try:
driver.get(url)
# Wait for dynamic content to load
WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.XPATH, '//div[@class="group flex"]'))
)
# Find all match rows
match_rows = driver.find_elements(By.XPATH, '//div[@class="group flex"]')
matches = []
for row in match_rows:
text_parts = row.text.split('\n')
if len(text_parts) >= 5:
match_data = {
'teams': f"{text_parts[1]} vs {text_parts[3]}",
'odds_home': text_parts[4] if len(text_parts) > 4 else None,
'odds_away': text_parts[5] if len(text_parts) > 5 else None,
'bookmakers': text_parts[6] if len(text_parts) > 6 else None
}
matches.append(match_data)
return matches
finally:
driver.quit()
This function navigates to OddsPortal, waits for JavaScript to render the page, then extracts match data. The finally block ensures the browser closes even if errors occur.
Handling Dynamic Content
Modern sites load content progressively. You can't scrape what hasn't rendered yet.
def wait_for_odds_to_load(driver, timeout=15):
try:
WebDriverWait(driver, timeout).until(
lambda d: len(d.find_elements(By.CSS_SELECTOR, '.odds-value')) > 0
)
return True
except:
return False
This helper function waits until odds elements appear on the page. The lambda checks for element presence repeatedly until timeout.
Method 3: Playwright for Modern Sites
Playwright is Selenium's younger, faster sibling. Developed by Microsoft, it offers better performance and more reliable element detection.
Why Choose Playwright Over Selenium?
Playwright handles modern web frameworks better. Sites built with React, Vue, or Angular render more predictably.
Auto-waiting is built in. Playwright automatically waits for elements before interacting. No more explicit sleep statements.
Multiple browser support includes Chromium, Firefox, and WebKit from a single API.
Installing Playwright
pip install playwright
playwright install
The second command downloads browser binaries.
Playwright Scraper Example
Here's a Playwright scraper targeting BetExplorer:
from playwright.sync_api import sync_playwright
import json
def scrape_betexplorer():
with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
context = browser.new_context(
user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
)
page = context.new_page()
The context manager handles browser lifecycle automatically. Custom user agents help avoid detection.
url = 'https://www.betexplorer.com/football/england/premier-league/'
page.goto(url, wait_until='networkidle')
# Wait for the odds table to appear
page.wait_for_selector('table.table-main')
# Extract all match rows
rows = page.query_selector_all('table.table-main tr')
matches = []
for row in rows:
cells = row.query_selector_all('td')
if len(cells) >= 4:
match = {
'teams': cells[0].inner_text(),
'home_odds': cells[1].inner_text() if len(cells) > 1 else None,
'draw_odds': cells[2].inner_text() if len(cells) > 2 else None,
'away_odds': cells[3].inner_text() if len(cells) > 3 else None
}
matches.append(match)
browser.close()
return matches
The wait_until='networkidle' parameter ensures all AJAX requests complete before scraping begins.
Handling Pop-ups and Consent Dialogs
EU cookie consent dialogs block scraping. Handle them programmatically:
def dismiss_consent_dialog(page):
try:
consent_button = page.query_selector('button[id*="accept"], button[class*="consent"]')
if consent_button:
consent_button.click()
page.wait_for_timeout(500)
except:
pass # No consent dialog present
This attempts to find and click common consent button patterns.
Bypassing Anti-Bot Protection
Betting sites invest heavily in anti-scraping technology. DataDome, Cloudflare, and PerimeterX guard major bookmakers.
Here's how to navigate these defenses.
Rotating User Agents
Static user agents get fingerprinted. Rotate through realistic browser signatures:
import random
USER_AGENTS = [
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/121.0',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.2 Safari/605.1.15'
]
def get_random_user_agent():
return random.choice(USER_AGENTS)
Update this list regularly. Browser versions change monthly.
Using Residential Proxies
Datacenter IPs get blocked immediately. Residential proxies route traffic through real home internet connections.
When you need reliable residential proxy infrastructure, consider services like Roundproxies. They offer residential, datacenter, ISP, and mobile proxies suitable for scraping at scale.
Here's how to configure proxy rotation:
import requests
from itertools import cycle
class ProxyRotator:
def __init__(self, proxy_list):
self.proxies = cycle(proxy_list)
def get_next_proxy(self):
proxy = next(self.proxies)
return {
'http': f'http://{proxy}',
'https': f'http://{proxy}'
}
def fetch_with_proxy(self, url):
proxy = self.get_next_proxy()
try:
response = requests.get(url, proxies=proxy, timeout=10)
return response
except:
return self.fetch_with_proxy(url) # Try next proxy
This class cycles through proxies on each request. Failed requests automatically retry with the next proxy.
Browser Fingerprint Randomization
Anti-bot systems analyze browser fingerprints. Screen resolution, timezone, and installed fonts create unique signatures.
def randomize_browser_context(playwright):
viewports = [
{'width': 1920, 'height': 1080},
{'width': 1366, 'height': 768},
{'width': 1536, 'height': 864},
{'width': 1440, 'height': 900}
]
timezones = ['America/New_York', 'Europe/London', 'America/Los_Angeles']
context = playwright.chromium.launch().new_context(
viewport=random.choice(viewports),
timezone_id=random.choice(timezones),
locale='en-US'
)
return context
Each scraping session appears as a different user.
Handling CAPTCHAs
CAPTCHAs eventually appear. You have three options.
Manual solving works for low-volume scraping. The script pauses, you solve the CAPTCHA, and scraping continues.
CAPTCHA solving services like 2Captcha integrate into your scraper. They charge per solve but handle automation.
The best approach? Avoid triggering CAPTCHAs in the first place. Slow down, use quality proxies, and randomize your fingerprint.
Building an Arbitrage Odds Scraper
Let's build something practical: a scraper that finds arbitrage opportunities across multiple bookmakers.
The Arbitrage Calculator
First, understand the math. Arbitrage exists when:
(1/Odds_A) + (1/Odds_B) < 1
For a two-way market like tennis, if Player A has odds of 2.10 at Bookmaker 1 and Player B has odds of 2.05 at Bookmaker 2:
(1/2.10) + (1/2.05) = 0.476 + 0.488 = 0.964
Since 0.964 < 1, arbitrage exists. The profit margin is 1 - 0.964 = 3.6%.
def calculate_arbitrage(odds_list):
"""
odds_list: [(outcome_name, decimal_odds, bookmaker), ...]
Returns: arbitrage percentage (negative means profit)
"""
if len(odds_list) < 2:
return None
best_odds = {}
for outcome, odds, bookie in odds_list:
if outcome not in best_odds or odds > best_odds[outcome][0]:
best_odds[outcome] = (odds, bookie)
implied_prob_sum = sum(1/odds for odds, _ in best_odds.values())
return implied_prob_sum - 1
def find_arbitrage_bets(matches_data):
"""
matches_data: dict with structure {match_id: {outcome: [(odds, bookie), ...]}}
"""
opportunities = []
for match_id, outcomes in matches_data.items():
flat_odds = []
for outcome, odds_list in outcomes.items():
for odds, bookie in odds_list:
flat_odds.append((outcome, odds, bookie))
arb_percentage = calculate_arbitrage(flat_odds)
if arb_percentage and arb_percentage < 0:
opportunities.append({
'match': match_id,
'profit_margin': abs(arb_percentage) * 100,
'odds': flat_odds
})
return sorted(opportunities, key=lambda x: x['profit_margin'], reverse=True)
Multi-Bookmaker Scraper
Now combine multiple scrapers into one system:
import asyncio
from concurrent.futures import ThreadPoolExecutor
class MultiBookmakerScraper:
def __init__(self):
self.scrapers = {
'oddsportal': self.scrape_oddsportal,
'betexplorer': self.scrape_betexplorer,
'flashscore': self.scrape_flashscore
}
def scrape_all(self, sport='football'):
all_odds = {}
with ThreadPoolExecutor(max_workers=3) as executor:
futures = {
executor.submit(scraper, sport): name
for name, scraper in self.scrapers.items()
}
for future in futures:
source = futures[future]
try:
odds_data = future.result(timeout=30)
all_odds[source] = odds_data
except Exception as e:
print(f"Failed to scrape {source}: {e}")
return self.merge_odds(all_odds)
def merge_odds(self, all_odds):
# Normalize and combine odds from different sources
merged = {}
for source, odds in all_odds.items():
for match in odds:
match_key = self.normalize_match_name(match['teams'])
if match_key not in merged:
merged[match_key] = {}
merged[match_key][source] = match
return merged
Thread pooling scrapes multiple bookmakers simultaneously. The merge function normalizes team names across sources.
Data Storage and Processing
Raw scraped data needs structure. Choose storage based on your use case.
CSV for Simple Analysis
import pandas as pd
def save_to_csv(matches, filename='odds_data.csv'):
df = pd.DataFrame(matches)
df['scraped_at'] = datetime.now().isoformat()
df.to_csv(filename, index=False)
return df
CSV works for one-off analysis. It's human-readable and opens in Excel.
SQLite for Historical Data
import sqlite3
def setup_database():
conn = sqlite3.connect('betting_odds.db')
cursor = conn.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS odds (
id INTEGER PRIMARY KEY AUTOINCREMENT,
match_id TEXT,
home_team TEXT,
away_team TEXT,
bookmaker TEXT,
home_odds REAL,
draw_odds REAL,
away_odds REAL,
scraped_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
''')
conn.commit()
return conn
def store_odds(conn, odds_data):
cursor = conn.cursor()
cursor.executemany('''
INSERT INTO odds (match_id, home_team, away_team, bookmaker, home_odds, draw_odds, away_odds)
VALUES (?, ?, ?, ?, ?, ?, ?)
''', odds_data)
conn.commit()
SQLite handles historical analysis. Query past odds movements, track accuracy, and build backtesting datasets.
JSON for Real-Time Systems
import json
from datetime import datetime
def save_to_json(data, filename='odds_snapshot.json'):
output = {
'timestamp': datetime.now().isoformat(),
'data': data
}
with open(filename, 'w') as f:
json.dump(output, f, indent=2)
JSON integrates easily with web applications and APIs.
Common Mistakes to Avoid
Years of experience trying to scrape sports betting sites reveal common failure patterns. Avoid these when building your own scrapers.
Ignoring Rate Limits
Scraping too fast triggers blocks immediately. Even without explicit rate limits, pounding a server with 1000 requests per minute looks suspicious.
Space your requests. Add random delays. Respect the site's infrastructure.
Hardcoding XPaths
HTML structures change constantly. Hardcoded selectors break when sites update their frontend.
Build flexible selectors. Use multiple fallback patterns. Add error handling for missing elements.
Skipping Error Handling
Network requests fail. Elements don't load. Proxies die. Your scraper must handle every failure gracefully.
def robust_scrape(url, max_retries=3):
for attempt in range(max_retries):
try:
return perform_scrape(url)
except Exception as e:
if attempt == max_retries - 1:
raise
time.sleep(2 ** attempt) # Exponential backoff
Exponential backoff gives temporary issues time to resolve.
Not Validating Data
Scraped odds sometimes contain garbage. Missing values, incorrect formats, and stale data corrupt your analysis.
def validate_odds(odds_value):
try:
odds = float(odds_value)
if 1.01 <= odds <= 100.0:
return odds
except (ValueError, TypeError):
pass
return None
Validation catches bad data before it poisons your dataset.
Advanced Techniques for 2026
The cat-and-mouse game between scrapers and anti-bot systems never ends. Here's what's working in 2026.
WebSocket Monitoring
Many sites push live odds through WebSocket connections. Intercepting these provides real-time updates without page reloading.
from playwright.sync_api import sync_playwright
def capture_websocket_odds():
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
odds_updates = []
def handle_websocket(ws):
ws.on('framereceived', lambda frame: odds_updates.append(frame))
page.on('websocket', handle_websocket)
page.goto('https://betting-site.com/live')
page.wait_for_timeout(10000) # Capture 10 seconds of updates
return odds_updates
This captures raw WebSocket frames containing odds updates.
Stealth Plugins
Detection evasion requires constant adaptation. Stealth libraries patch browser automation tells:
from playwright_stealth import stealth_sync
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
stealth_sync(page) # Apply stealth modifications
page.goto('https://betting-site.com')
These plugins modify JavaScript properties that reveal automation.
Machine Learning for Parsing
Site structures vary wildly. ML models trained on HTML patterns can extract data even when selectors change:
# Conceptual example - requires trained model
from ml_scraper import OddsExtractor
extractor = OddsExtractor.load('betting_model.pkl')
html = page.content()
odds_data = extractor.extract(html)
This approach generalizes across sites rather than requiring site-specific code.
FAQ
Is it legal to scrape sports betting sites?
Scraping publicly available data is generally legal. However, violating Terms of Service can lead to account termination and civil liability. Always check local laws and site policies before you scrape sports betting sites.
Which Python library should I use for scraping betting sites?
Start with requests for sites with exposed APIs. Use Playwright or Selenium for JavaScript-heavy sites. Playwright offers better performance and auto-waiting.
How do I avoid getting blocked while scraping betting sites?
Rotate IP addresses using residential proxies. Randomize user agents and browser fingerprints. Add delays between requests. Respect robots.txt and rate limits.
Can I scrape live betting odds in real-time?
Yes, using WebSocket interception or frequent polling. WebSocket monitoring captures odds updates as they happen. Polling refreshes data at intervals you control.
What data can I extract from betting sites?
Common data points include: match details, decimal/American odds, opening and closing lines, player statistics, historical results, and bookmaker margins.
Conclusion
Successfully scraping sports betting sites in 2026 requires a multi-tool approach. HTTP requests work for exposed APIs. Browser automation handles JavaScript rendering. Anti-bot evasion demands proxies, fingerprint randomization, and human-like behavior.
Start simple with OddsPortal or BetExplorer. Build your skills with basic Selenium scrapers. Graduate to Playwright for better reliability. Add proxy rotation when you hit scale.
The techniques in this guide form a foundation. Betting sites evolve their defenses constantly. Your scrapers must evolve too.
Now pick a target and start building.
