Scraping Athlete Performance Stats Across Multiple Sports

Sports analytics is booming. From fantasy leagues to professional scouting, access to comprehensive athlete stats across multiple sports creates valuable insights. Here's how to build a multi-sport stats scraper. The Challenge Sports data is scattered across dozens of sites, each with different structures. ESPN, official league sites, and stats aggregators all organize data differently. A unified scraper abstracts this complexity. Multi-Sport Scraper pip install requests beautifulsoup4 pandas lxml import requests from bs4 import BeautifulSoup import pandas as pd from abc import ABC , abstractmethod class SportScraper ( ABC ): def __init__ ( self , api_key ): self . api_key = api_key self . session = requests . Session () def fetch ( self , url ): proxy = f " http://api.scraperapi.com?api_key= { self . api_key } &url= { url } " return self . session . get ( proxy , timeout = 30 ) @abstractmethod def scrape_stats ( self , url ): pass class BasketballScraper ( SportScraper ): def scrape_s