Nationals Scorecard

This text appears to be a string of baseball game results from the 2010 season. The format is similar to that used by ESPN's Baseball Prospectus, where each line represents a single game result.

Here are some observations about this data:

• The games were played between two teams: one represented by an MLB team abbreviation (e.g., "NYM" for New York Mets) and the other represented by their actual name.
• Each game had an outcome of either a win, loss, or tie. The outcome was usually specified as "W", "L", or "T".
• Games were listed in chronological order, with the earliest date at the beginning and the latest date at the end.

To analyze this data further, you could consider extracting various statistics from it, such as:

• A list of all unique teams that played each other.
• The overall win-loss record for each team.
• A breakdown of how many games were won, lost, or ended in a tie for each team.
• A histogram of the number of wins and losses for each team.

Some potential Python code to get started:

import re

# Define a function to parse game results
def parse_game_result(result):
    # Regular expression pattern to match game result format
    pattern = r"(\d{3})\s+(\w+)\s+(\w+)"
    match = re.match(pattern, result)
    if match:
        date = match.group(1)
        home_team = match.group(2)
        away_team = match.group(3)
        return (date, home_team, away_team)
    else:
        raise ValueError("Invalid game result format")

# Define a function to extract team data
def extract_teams(games):
    teams = set()
    for result in games:
        date, home_team, away_team = parse_game_result(result)
        # Add both teams to the set
        teams.add(home_team)
        teams.add(away_team)
    return list(teams)

# Define a function to calculate win-loss records
def calculate_win_loss_records(games):
    records = {}
    for result in games:
        date, home_team, away_team = parse_game_result(result)
        # Assume W or L indicates the winner's team
        if result.startswith("W"):
            winner = home_team
        else:
            winner = away_team
        if winner not in records:
            records[winner] = {"wins": 0, "losses": 0}
        if result.startswith("L"):
            records[winner]["losses"] += 1
        else:
            records[winner]["wins"] += 1
    return records

# Load the data and extract team information
games = [line.strip() for line in text.split("\n")]
teams = extract_teams(games)
win_loss_records = calculate_win_loss_records(games)

# Print some summary statistics
for team, record in win_loss_records.items():
    print(f"{team}: {record['wins']}-{record['losses']}")

Note that this is just a starting point, and there are many ways to analyze this data further. You may want to consider using more advanced statistical techniques or data visualization tools to gain deeper insights into the team performance. Updated: June 26, 2025 at 8:58 PM