A COMPARATIVE STUDY OF THE DIFFERENT CLASSIFICATION ALGORITHMS ON FOOTBALL ANALYTICS

Abstract

Sports analytics is on the rise, with many teams looking to use data science and machine learning algorithms to augment their teams research and boost team performance. This is especially true in the case of Football Clubs. In this work, we have taken the statistics of matches for each team from five major football leagues. These include the English Premier League, La Liga, Serie A, Bundesliga, and Ligue 1. We use this data for two kinds of classification to predict a teams win, loss, or draw. First, we implement Multiclass Classification using Naive Bayes classification, Decision Tree classification, and K-Nearest Neighbours classification. We use f1-score, recall, and precision to evaluate the model. Next, we use Binary Classification to predict if a team wins or does not win, i.e., a loss or a draw. We achieve this by using Support Vector Machines, Logistics Regression, K-Nearest Neighbours classification, Decision Tree classification, and Naive Bayes classification. We evaluate the results using the evaluation metrics mentioned above. Now, we compare the accuracy and efficacy of these algorithms based on the evaluation metrics. This will help standardize the means of classification in sports and football analytics in the future.