A machine learning framework for sport result prediction

By Rory Bunker, Fadi A. Thabtah. Applied Computing and Informatics, 2019

302 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/08211e51290e1ec1c508745feb9d97dc48814581

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Citations in the corpus

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review (2019, methodology): “The supervised learning requires input and output data to develop a predictive model whereas the unsupervised learning is based on input data only [7].”
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories (2021, methodology): “For instance, just focussing on the past four years, we can find a large number of conventional studies applying or slightly adapting the CRISP-DM methodology to many different domains: healthcare [18], [19], [20], [21], signal processing [22], engineering [23], [24], education [25], [26], [27], [28”
The explanation game: a formal framework for interpretable machine learning (2020, unknown): “recommendations (Bell and Koren 2007) and sports analytics (Bunker and Thabtah 2019) to genomics (Zou et al.”