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How to Scrape Motorsport Data: F1, NASCAR, and MotoGP Stats

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How to Scrape Motorsport Data: F1, NASCAR, and MotoGP Stats

Motorsport data is rich with statistics — lap times, driver standings, pit strategies, and historical records. Let's build scrapers for the three biggest racing series.

Data Sources

  • Ergast API — Free F1 data going back to 1950
  • NASCAR.com — Live and historical results
  • MotoGP.com — Race results and rider stats

Setting Up 

pip install requests beautifulsoup4 pandas matplotlib
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Formula 1: Ergast API

The Ergast API is completely free with no authentication required:

import requests
import pandas as pd

ERGAST_BASE = "https://ergast.com/api/f1"

def get_f1_standings(season="current"):
    url = f"{ERGAST_BASE}/{season}/driverStandings.json"
    resp = requests.get(url)
    data = resp.json()
    standings = []
    for item in data["MRData"]["StandingsTable"]["StandingsLists"][0]["DriverStandings"]:
        driver = item["Driver"]
        standings.append({
            "position": int(item["position"]),
            "driver": f"{driver['givenName']} {driver['familyName']}",
            "nationality": driver["nationality"],
            "team": item["Constructors"][0]["name"],
            "points": float(item["points"]),
            "wins": int(item["wins"])
        })
    return pd.DataFrame(standings)

df = get_f1_standings("2025")
print(df.to_string(index=False))
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F1 Race Results 

def get_race_results(season, round_num):
    url = f"{ERGAST_BASE}/{season}/{round_num}/results.json"
    resp = requests.get(url)
    data = resp.json()
    results = []
    race = data["MRData"]["RaceTable"]["Races"][0]
    for r in race["Results"]:
        results.append({
            "position": r["position"],
            "driver": f"{r['Driver']['givenName']} {r['Driver']['familyName']}",
            "team": r["Constructor"]["name"],
            "grid": r["grid"],
            "status": r["status"],
            "time": r.get("Time", {}).get("time", "DNF"),
        })
    return race["raceName"], pd.DataFrame(results)

name, results = get_race_results("2025", 1)
print(f"\n{name}:")
print(results.head(10).to_string(index=False))
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Historical Win Analysis 

def get_all_winners(start_year=2000, end_year=2025):
    all_winners = []
    for year in range(start_year, end_year + 1):
        url = f"{ERGAST_BASE}/{year}/results/1.json?limit=50"
        resp = requests.get(url)
        races = resp.json()["MRData"]["RaceTable"]["Races"]
        for race in races:
            winner = race["Results"][0]
            all_winners.append({
                "year": year,
                "race": race["raceName"],
                "driver": f"{winner['Driver']['givenName']} {winner['Driver']['familyName']}",
                "team": winner["Constructor"]["name"]
            })
    return pd.DataFrame(all_winners)

winners = get_all_winners(2015, 2025)
print("\nMost wins 2015-2025:")
print(winners["driver"].value_counts().head(10))
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Scraping NASCAR Results

NASCAR doesn't have a public API, so we scrape with ScraperAPI:

from bs4 import BeautifulSoup

def scrape_nascar_results(year=2025):
    params = {
        "api_key": "YOUR_SCRAPERAPI_KEY",
        "url": f"https://www.nascar.com/results/{year}/cup-series/",
        "render": "true"
    }
    resp = requests.get("https://api.scraperapi.com", params=params)
    soup = BeautifulSoup(resp.text, "html.parser")
    races = []
    for row in soup.select(".race-results-row"):
        race_name = row.select_one(".race-name")
        winner = row.select_one(".winner-name")
        if race_name and winner:
            races.append({
                "race": race_name.get_text(strip=True),
                "winner": winner.get_text(strip=True)
            })
    return races
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For MotoGP and other platforms, ThorData residential proxies ensure reliable access. Monitor health with ScrapeOps.

Visualization 

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(12, 6))
for driver in df.head(5)["driver"]:
    driver_data = df[df["driver"] == driver]
    ax.barh(driver, driver_data["points"].values[0])
ax.set_xlabel("Points")
ax.set_title("F1 Championship Standings")
plt.tight_layout()
plt.savefig("f1_standings.png")
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Key Takeaways

  • The Ergast API provides free, structured F1 data back to 1950
  • NASCAR and MotoGP require web scraping with JS rendering
  • Historical analysis reveals dominance patterns and era shifts
  • Combining series data enables cross-motorsport comparisons

The Ergast API is a free community resource. For other sites, respect robots.txt and rate limits.

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