Summary

The web content provides a detailed guide on how to scrape football match fixtures from Flashscore using Python, including code snippets and explanations.

Abstract

The article titled "Easiest Way to Scrape Football Fixtures from Flashscore" outlines a straightforward method for extracting match information such as dates, times, and team names from the Flashscore website using Python. It begins by listing the necessary Python packages and instructing readers on setting up ChromeDriver with the appropriate configurations to mimic human browsing behavior. The guide includes functions to initialize the browser, accept cookies, and clean up the scraped data. It also demonstrates how to locate and extract relevant data elements from the webpage, format them, and store them in a pandas DataFrame. The article concludes by showing the expected output and providing a link to the complete Jupyter Notebook on GitHub for readers to explore further. The author, Paul Conish, encourages feedback and offers his social media handle for support. Additionally, the article promotes an AI service as a cost-effective alternative to ChatGPT Plus.

Opinions

The author believes that the method described is simple and effective for scraping football fixtures.
The use of a Jupyter Notebook is recommended for ease of use and reproducibility.
The author suggests that mimicking human browsing behavior is important to avoid detection when scraping.
Providing a complete code example on GitHub is seen as beneficial for readers to follow along and implement the scraper themselves.
The author values community engagement and support, inviting readers to reach out with questions or issues.
An AI service is endorsed as a more affordable option compared to ChatGPT Plus, implying its potential value to the readers.

Easiest Way to Scrape Football Fixtures from Flashscore

Today we will look at a super simple way to scrape match info from flashscore using python.

Lets dive straight into it.

First import the necessary packages, you can check some of my other tutorials for an actual list of these.

Obviously like before you will need to download chromedriver, make sure the downloaded chromdriver version matches your current chrome version.

We will create a function that holds all of our logic to create a new chromedriver instance. Simply replace the PATH TO CHROMEDRIVER with your path.

def driver_code():
    Capabilities = DesiredCapabilities.CHROME
    Capabilities["pageLoadStrategy"] = "normal"
    options = ChromeOptions()

    useragentarray = [
        "Mozilla/5.0 (Linux; Android 13) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.5672.76 Mobile Safari/537.36"
    ]

    options.add_argument("--disable-blink-features=AutomationControlled")
    options.add_argument("--no-sandbox")
    options.add_argument("--disable-dev-shm-usage")
    # options.add_argument(f"--user-data-dir=./profile{driver_num}")

    options.add_experimental_option("excludeSwitches", ["enable-automation"])
    options.add_experimental_option("useAutomationExtension", False)
    options.add_argument("disable-infobars")
    options.add_argument("disable-blink-features=AutomationControlled")

    driver = webdriver.Chrome(
        'PATH TO CHROMEDRIVER',
        options=options,
        desired_capabilities=Capabilities,
    )
    driver.execute_script(
        "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})"
    )

    driver.execute_cdp_cmd(
        "Network.setUserAgentOverride", {"userAgent": useragentarray[0]}
    )
    driver.set_window_size(390, 844)
    options.add_argument("--disable-popup-blocking")
    #     driver.execute_script(
    #         """setTimeout(() => window.location.href="https://www.bet365.com.au", 100)"""
    #     )
    driver.get("https://www.flashscore.com/football/england/premier-league/fixtures/")
    time.sleep(1)
    return driver

We have two more functions, one to accept cookies on flashscore and the other simply will remove any special characters in strings and convert them to lowercase.

def accept_cookies(driver):
    cookies = driver.find_elements(By.ID, "onetrust-accept-btn-handler")
    if(len(cookies) > 0):
        cookies[0].click()
    else:
        print("No Cookies to Click")

def sort_string(string):
    string = ''.join(e for e in string if e.isalnum())
    string = string.lower()
    return string

We can then initialise our driver instance and accept the cookies. I will create some arrays and search for the elements we will be scraping.

driver = driver_code()
accept_cookies(driver)

home_team_names = []
away_team_names = []
match_dates = []
match_times = []

date_elements = driver.find_elements(By.CSS_SELECTOR,".event__time")
home_teams = driver.find_elements(By.CSS_SELECTOR,".event__participant--home")
away_teams = driver.find_elements(By.CSS_SELECTOR,".event__participant--away")

The below code just simply splits the date and time into individual strings adds the year to the date and does some formatting on both. If for some reason there are issues with the date/time we can instead just append N/A to our arrays

for i in date_elements:
    try:
        date_split_string = (i.text).split()
        date_with_year = date_split_string[0] + "2024"
        match_dates.append(date_with_year)
        split_time = date_split_string[1]
        match_times.append(split_time)
    except:
        match_dates.append("N/A")
        match_times.append("N/A")

The following iterates through our team elements, formatting and adding them to our arrays.

for i in range(len(home_teams)):
    home_team = sort_string(home_teams[i].text)
    home_team_names.append(home_team)
    away_team = sort_string(away_teams[i].text)
    away_team_names.append(away_team)

We now have all of our data so we can create a dataframe to hold this data and we can finally quit our driver instance.

league = ["English Premier League"] * len(home_team_names)
my_columns = ['Match Date','Match Time','Home Team','Away Team','League']
new_dataframe = pd.DataFrame(columns = my_columns)
new_dataframe['Match Date'] = match_dates
new_dataframe['Match Time'] = match_times
new_dataframe['Home Team'] = home_team_names
new_dataframe['Away Team'] = away_team_names
new_dataframe['League'] = league
new_dataframe
driver.quit()

Our final dataframe should look like the below

Full Jupyter Notebook can be found here.

https://github.com/paulc160/Flashscore-Scraper-Football-Fixtures/blob/main/Flashscore%20Football%20Fixtures%20Scraper.ipynb

That was quite easy wasnt it? If you have any issues or questions re this code please reach out. You can find me @PaulConish on X/Twitter.

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