Braves Scorecard

Here is the data in a JSON format:

[
  {
    "date": "April 3, 2005",
    "opponent": "Atlanta Braves",
    "score": "1-0",
    "result": "Win"
  },
  {
    "date": "April 7, 2005",
    "opponent": "San Diego Padres",
    "score": "2-4",
    "result": "Loss"
  },
  {
    "date": "April 10, 2005",
    "opponent": "Florida Marlins",
    "score": "1-0",
    "result": "Win"
  },
  {
    "date": "April 13, 2005",
    "opponent": "New York Mets",
    "score": "2-4",
    "result": "Loss"
  }
]

However, the provided data seems to be a large text string with multiple lines of game results. It appears that each line has a date, opponent, score, and result. To process this data in a meaningful way, I would suggest splitting it into individual games and then processing each game separately.

Here is an example of how you can parse the data using Python:

import re

data = """
... (large text string with multiple lines of game results)
"""

games = []
for line in data.splitlines():
    if line.startswith("April"):  # or any other common date format
        continue  # skip this line as it's likely a header
    match = re.match(r"(\d{1,2} \w+ \d{4}) (.+?) (\d+-\d+) (.+)", line)
    if match:
        date = match.group(1)
        opponent = match.group(2)
        score = match.group(3).split("-")
        result = match.group(4).strip()  # strip any leading/trailing whitespace
        games.append({
            "date": date,
            "opponent": opponent,
            "score": f"{score[0]}-{score[1]}",  # format score as x-y
            "result": result
        })

for game in games:
    print(game)

This code uses regular expressions to parse each line of the data and extract the date, opponent, score, and result. The results are then stored in a list of dictionaries, where each dictionary represents a single game.

You can modify this code to suit your specific needs, such as adding additional fields or processing the data in a different way. Updated: July 30, 2025 at 7:48 AM