Analysis of Match Statistics and Team Performances in the Premier League From Season 2015/16 to Season 2019/20
- AUTHOR: TARA NGUYEN
- Final project for the course Introduction to Data Science at UCLA Extension
- Completed in December 2020
Background: The English Premier League (EPL), the top level of competition in English soccer, is one of the most popular and most competitive sport events in the world.
Data and research question: In this project I analyzed match statistics (results, number of goals, passing, shooting, etc.) and team performances in the EPL from the 2015/2016 season to the 2019/2020 season. The main research question was: Which statistics are the most predictive of match results?
Method and findings: The entire project was done in R. Using the random forest algorithm, I found three features that were the highly important in predicting match results: how many goals the home team scored, how many goals the away team scored, and which team scored first. Other sub-questions such as "Is there really a home-team advantage?" and "Which team formation was the most/least effective?" were also answered along the way.
For a complete report, see the wiki page.
Plots - directory for plots created during data visualization
README.md - this document you are currently reading
epldat5seasons_Analysis.R - main R script for data wrangling, visualization, and statistical analyses
epldat5seasons_WebScraping.R - R script for scraping original match statistics from webpages
matchstats.csv - final data set
- IMPORTANT NOTE: If you wish to open the file, please make a copy first and open the copy. Do NOT open the original file because that might distort the format of some of the values in the file.
The dataset and R scripts are free for download and use, provided that proper credit is given.
If you mention or use any part of my research report, please provide a link to this repo.