In this project, we created a predictive model to compare different metrics and their importance in predicting the winner of the UEFA Champions League. The data we used to predict future winners of this league was called from fbref.com, a database of soccer statistics. The data collected to build the model comes from the previous four seasons. The specific types of data collected include shooting percentages, goalkeeping percentages, passing percentages, as well as other relevant data. The aim of our project was to predict and explain the features that were most directly correlated with a winner in the champions league. Prior to our construction of the model, we hypothesized that the variables that were most important for predicting winners were wins and losses. However, after actually running the model, we discovered that the most important figures were league rankings, wins, and points scored.
Completed by: Mehlam Saifudeen, Abdul Ahusaini, and Leonardo Astroga. For inquiries please contact [email protected].