Predicting the Outcome of Indian Super League Football Matches Using Machine Learning

Abstract

The study is an extensive examination that aims to forecast the critical factors impacting football match outcomes, with a particular focus on win/loss determination in the Indian Super League (ISL). The study makes use of three different machine learning algorithms: AdaBoost, Support Vector Machine (SVM), and Classification and Regression Trees (CART), using data from a total of 377 matches. The main goal of the study is to identify and select the most influential features that influence a match's outcome. Through the use of these algorithms, we want to increase forecast accuracy and offer insights into the crucial elements that may be strategically controlled to boost team performance.
The study assesses each algorithm's prediction power and analyzes how well it performs in feature selection. The findings show notable differences in feature importance between the models, highlighting the advantages and disadvantages of each model. This work contributes to the wider use of machine learning in sports analytics while also deepening our understanding of football performance drivers.