Athletics Scorecard

This text appears to be a large dump of baseball game results from the 2003 season. I'll provide an example of how you might parse this data into something more usable.

Here's an example code snippet in Python that uses regular expressions to extract the relevant information:

import re
from datetime import datetime

# Regular expression pattern to match a single game result
game_result_pattern = r"(.*) (\d{3})-(\d{3}) (\d+) games"
matches = []
for line in text.splitlines():
    match = re.search(game_result_pattern, line)
    if match:
        team1, score1, score2, games = match.groups()
        print(f"{team1} {score1}-{score2} in {games} games")
        matches.append((team1, score1, score2, games))

# Convert the game results into a more usable format
game_results = []
for team1, score1, score2, games in matches:
    game_result = {
        "team": team1,
        "score": f"{score1}-{score2}",
        "games": int(games),
        "datetime": datetime.strptime(line.split()[0], "%b %d %Y").date()
    }
    game_results.append(game_result)

# Print the game results in a more readable format
for game_result in sorted(game_results, key=lambda x: x["datetime"]):
    print(f"{game_result['team']}: {game_result['score']} ({game_result['games']} games)")

This code snippet extracts the team names, scores, and number of games from each line of text. It then converts these values into a more usable format (a dictionary with keys "team", "score", "games", and "datetime"). Finally, it prints the game results in a more readable format.

Please note that this is just one possible way to parse this data, and you may need to adjust the regular expression pattern or the code snippet to suit your specific needs. Updated: July 18, 2025 at 1:08 AM