Sports Analytics and Machine Learning Approaches for Match Outcome Prediction

Abstract

The prediction of match outcomes has become one of the most active areas within sports analytics, drawing equally on the growing availability of detailed performance data and on the maturing field of machine learning. This paper develops and compares a set of machine learning algorithms for predicting the outcome of competitive football matches and identifies the performance indicators that contribute most strongly to accurate prediction. A historical dataset of two thousand two hundred and eighty professional league matches spanning six seasons was assembled and engineered into a set of features describing recent form, attacking and defensive strength, home advantage and head-to-head history. Six algorithms were trained and evaluated: logistic regression, decision tree, support vector machine, random forest, a multilayer artificial neural network and extreme gradient boosting. Model performance was assessed through accuracy, precision, recall, the F1 score and the area under the receiver operating characteristic curve, using stratified cross-validation. The extreme gradient boosting model achieved the strongest performance, with an accuracy of sixty-seven per cent, followed closely by the neural network and the random forest. A Friedman test confirmed that the differences in accuracy across the algorithms were statistically significant. Feature-importance analysis showed that recent team form, average goal difference and home advantage were the most influential predictors, while the draw remained the most difficult outcome to classify. The study concludes that ensemble methods offer a robust and practical basis for match outcome prediction and that careful feature engineering is at least as important as the choice of algorithm.