There are generally two approaches to scrapping data from an online source such as Basketball Reference. You can go the general route which would involve using existing python libraries to manually scrap data through websites utilizing the requests library in python. For this section we will be focusing on script based web-scraping. 

1. Your first thing to check is that you have all the necessary libraries installed. 

   1. "pip install requests beautifulsoup4 pandas"

   2. "import pandas"

   3. "import requests"

2. For my project we are looking at data of Jayson Tatum in a specific game in the playoffs that has a lot of cultural significance because of his excellence in that game. Hence, I will be comparing very comparable games from other players that also matched his 50 point-benchmark. So, I scraped all of the shot data from his game on April 15, 2023. 

3. Choose a game url from BasketballReference.com:

   1. Follow this format: https://www.basketball-reference.com/boxscores/shot-chart/YYYYMMDDTEAM.html

4. Open a Python File->File->Create New Python File-> and then follow the follow steps. 

5. Script Steps:

   1. #URL of the game you want to scrape

      1. url = "https://www.basketball-reference.com/.....

      2. response = requests.get(url)

      3. soup = BeautifulSoup(response.text, 'html.parser')

   2. # Find all shots (divs with tooltips)

      1. shots = soup.find_all("div", class_="tooltip")

   3. # Extract shots for a specific player

      1. player_name = "Jayson Tatum"

      2. results = []

      3. for shot in shots:

      4.     tip = shot.get("tip")

      5.     style = shot.get("style")

      6. Continued loop:

         1. if player_name in tip:

        x = int(style.split('left:')[1].split('px')[0].strip())

        y = int(style.split('top:')[1].split('px')[0].strip())

        made = "makes" in tip

        description = tip.replace("<br>", " ")

        results.append({

            "player": player_name,

            "x": x,

            "y": y,

            "result": "made" if made else "missed",

           "description": description

       })

5. 4. #Save to CSV

      1. df = pd.DataFrame(results)

      2. df.to_csv(f"{player_name.replace(' ', '_')}_shots.csv", index=False)