Visualizing Football Team Performance with Pandas

As a Python enthusiast blending creativity and data, I wanted to see how football teams stack up—not just by wins, but by the full story: draws, losses, and overall probability of outcomes.

I started with a clean dictionary that holds each team's season performance: wins, draws, and losses. To make this data usable, I reshaped it using a simple list comprehension to feed a Pandas DataFrame. This gave me the flexibility to calculate new metrics and play with the data intuitively.

Key steps:

• Added a (Games Played) column to reflect total matches.
• Computed (Win), (Draw), and (Loss Probability) for each team to highlight performance trends at a glance.
• Rounded values to 3 decimal places for clarity and neatness.

What I love most: this opens the door for visualizations and storytelling. With just a few lines, we can turn raw stats into insights—whether it's identifying underdogs, consistency kings, or draw-heavy squads.

Would love to hear your thoughts!

Python #Pandas #DataScience #FootballStats

import pandas as pd

# Dictionary with team stats: (wins, draws, losses)
team_stats = {
    "Liverpool": (25, 9, 4),
    "Arsenal": (20, 14, 4),
    "Manchester City": (21, 7, 10),
    "Chelsea": (20, 9, 9),
    "Newcastle United": (20, 6, 12),
    "Aston Villa": (19, 9, 10),
    "Nottingham Forest": (19, 8, 11),
    "Brighton": (16, 13, 9),
    "Bournemouth": (15, 11, 12),
    "Brentford": (16, 8, 14),
    "Fulham": (15, 9, 14),
    "Crystal Palace": (13, 13, 12),
    "Everton": (11, 15, 12),
    "West Ham United": (11, 10, 17),
    "Manchester United": (11, 9, 18),
    "Wolves": (12, 6, 20),
    "Tottenham Hotspur": (11, 5, 22),
    "Leicester City": (6, 7, 25),
    "Ipswich Town": (4, 10, 24),
    "Southampton": (2, 6, 30),
}

# Convert to DataFrame
df = pd.DataFrame([
    {"Team": team, "Wins": w, "Draws": d, "Losses": l}
    for team, (w, d, l) in team_stats.items()
])

# Add a Games Played column
df["Games Played"] = df["Wins"] + df["Draws"] + df["Losses"]

# Optional: Add probability columns
df["Win Probability"] = (df["Wins"] / df["Games Played"]).round(3)
df["Draw Probability"] = (df["Draws"] / df["Games Played"]).round(3)
df["Loss Probability"] = (df["Losses"] / df["Games Played"]).round(3)

print(df.head())  # View first few rows