An analysis of the 2020/21 Premier League Soccer Season to study the impact of the COVID pandemic in the performance of top and bottom finishing teams.
Cleaning, filtering and transforming of data that was scraped from the official Premier League website was done using Python libraries like Pandas, Numpy and Scipy.
This analysis then dives into the statistics from 28 years of soccer matches in the English Premier League, performs several hypothesis tests to understand how the Pandemic affected season differed from the previous years. It discovers important and statistically significant differences between the previous all seasons and pandemic affected season especially in regards to how the Home teams have let their performance levels drop significantly in the absence of Home Team supporters in the stadium due to pandemic-related restrictions.
Various visualization are included in the analysis as a tool to highlight the stark contrast between the 2020/21 season as compared to seasons prior.
The findings of this analysis could help the teams identify and pinpoint what aspect of their games needs to be addressed as a result of the absence of the supporters in the stadium.
- Data Scraping
- Data Cleaning
- Hypothesis Building
- Statistical Tests
- Data Vizualization
- Data Analysis
- Pandas
- Numpy
- Scipy
- Jupyter Notebook
- Matplotlib
- Seaborn
- PandaSQL