In this project, we aim to analyze team performance in the English Premier League using data from fbref. We will utilize Pandas to create DataFrames for analysis, and Matplotlib along with Seaborn for visualizations. Specifically, we will focus on the following metrics:

• Possession vs Expected Goals (xG)
• Penalties per 90 minutes
• Progression
• Difference between Expected Goals created and Expected Goals conceded per 90 minutes over the past 3 seasons.

We will gather data from fbref, a reliable source for Premier League statistics.

• Pandas: For data manipulation and creating DataFrames.
• Matplotlib and Seaborn: For creating visualizations to better understand the data.

We will explore the relationship between possession statistics and Expected Goals (xG) to understand how possession influences goal-scoring opportunities.

Analyzing the frequency of penalties per 90 minutes across teams to identify any trends or anomalies.

Investigating the progression metrics to assess how effectively teams move the ball forward during matches.

We will calculate the difference between Expected Goals created and Expected Goals conceded per 90 minutes for each team over the past 3 seasons. This metric will provide insights into offensive and defensive capabilities.

By studying these metrics over multiple seasons, we aim to identify patterns and trends in team performance, providing valuable insights for fans, analysts, and team management.

This repository serves as the basis for my blog post on Hashnode.