A machine learning framework for sport result prediction

TLDR

Machine learning has shown promise in classification and prediction, and its application to sport results is increasingly important for betting, club strategy, and performance analysis, relying on historical match data, player metrics, and opponent information. This study critically reviews the literature on artificial neural networks for sport result prediction, identifies learning methods, data sources, evaluation practices, and challenges, and proposes a novel predictive framework. The proposed framework employs machine‑learning techniques, particularly artificial neural networks, to learn from historical match outcomes, player performance indicators, and opponent data, and evaluates model performance using established metrics.

Abstract

Machine learning (ML) is one of the intelligent methodologies that have shown promising results in the domains of classification and prediction. One of the expanding areas necessitating good predictive accuracy is sport prediction, due to the large monetary amounts involved in betting. In addition, club managers and owners are striving for classification models so that they can understand and formulate strategies needed to win matches. These models are based on numerous factors involved in the games, such as the results of historical matches, player performance indicators, and opposition information. This paper provides a critical analysis of the literature in ML, focusing on the application of Artificial Neural Network (ANN) to sport results prediction. In doing so, we identify the learning methodologies utilised, data sources, appropriate means of model evaluation, and specific challenges of predicting sport results. This then leads us to propose a novel sport prediction framework through which ML can be used as a learning strategy. Our research will hopefully be informative and of use to those performing future research in this application area.