Welcome to the Liga Historical Data Analysis project! This project aims to leverage the power of Python to collect, clean, and analyze historical data of football teams in the Liga. The dataset spans from the year 2020 to the present, providing a comprehensive look at team performance, player statistics, and various aspects of the game.
Web Scraping Notebook:
The web scraping notebook focuses on the extraction of historical football data from online sources using Python. It utilizes web scraping tools and libraries to gather information about matches, team statistics, and player performances. The data collected is then processed and saved for further analysis.
Analysis Notebook:
The analysis notebook, accessible from the GitHub repository, showcases the power of Python for data analysis. It employs popular libraries such as Pandas, Matplotlib, Seaborn, and Plotly to clean, visualize, and interpret the Liga data. Various charts and plots provide insights into team performance, defensive and offensive statistics, player indiscipline, and more.
Key Highlights:
Data Collection: Python web scraping tools are employed to collect comprehensive historical data, ensuring an up-to-date and detailed dataset.
Data Cleaning: The collected data undergoes a thorough cleaning process to handle missing values, format inconsistencies, and outliers, ensuring the integrity of the analysis.
Exploratory Data Analysis (EDA): Through powerful Python libraries, the analysis notebook explores key trends, patterns, and insights in the Liga data. Visualizations help in understanding team dynamics, player statistics, and other relevant aspects.
Explore the Analysis:
Navigate to the notebook below to access the analysis notebook and visualize interactive charts that provide a deep understanding of Liga teams' performance.
Gain insights into defensive capabilities with box plots illustrating tackles and interceptions.
Assess offensive strengths by exploring goal-scoring trends and expected goals (xG).
Delve into player indiscipline with a comprehensive overview of fouls, yellow cards, and red cards.
GitHub : Link to the project repository