Yankees Scorecard

This text appears to be a large dump of baseball game results from the 2002 season. It's not a structured data format like JSON or CSV, but rather a plain text file containing a long string of information.

If you're looking to parse this data for analysis or processing, I'd suggest converting it into a more structured format like JSON or CSV. Here are some possible steps:

1. Split the text into individual game results: You can use a delimiter like \n or | to split the text into individual lines, each representing a single game result.
2. Extract relevant information from each line: Each line would contain a date, two team names, and the final score (e.g., "August 18, 2002 at 3:05 PM - Seattle Mariners - Yankees 2 - Mariners 5 F"). You can extract these fields using string manipulation techniques like split() or regular expressions.
3. Convert the data into a structured format: Once you have extracted the relevant information from each line, you can convert it into a more structured format like JSON or CSV.

Here's some example Python code to get you started:

import json

# Read the text file into a string
with open('game_results.txt', 'r') as f:
    game_data = f.read()

# Split the text into individual game results
lines = game_data.split('\n')

# Extract relevant information from each line
games = []
for line in lines:
    date, home_team, away_team, score = line.split()
    # Extract the final score and outcome (e.g., "F" for loss)
    final_score, outcome = score.split(' - ')
    games.append({
        'date': date,
        'home_team': home_team,
        'away_team': away_team,
        'final_score': final_score,
        'outcome': outcome
    })

# Convert the data into JSON format
game_data_json = json.dumps(games)

print(game_data_json)

This code reads the text file, splits it into individual game results, extracts the relevant information from each line, and then converts the data into a JSON string. You can then use this JSON string for analysis or processing. Updated: August 3, 2025 at 10:17 AM