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The use of machine learning in sport outcome prediction: A review

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Abstract The increase in the volume of structured and unstructured data related to more than just sport events leads to the development and increased use of techniques that extract information and employ machine‐learning algorithms in predicting process outcomes based on input but not necessarily output data. Taking sports into consideration, predicting outcomes, and extracting valuable information has become appealing not only to sports workers but also to the wider audience, particularly in the areas of team management and sports betting. The aim of this article is to review the existing machine learning (ML) algorithms in predicting sport outcomes. Over 100 papers were analyzed and only some of these papers were taken into consideration. Almost all of the analyzed papers use some sort of feature selection and feature extraction, most often prior to using the machine‐learning algorithm. As an evaluation method of ML algorithms, researchers, in most cases, use data segmentation with data being chronologically distributed. In addition to data segmentation, researchers also use the k‐cross‐evaluation method. Sport predictions are usually treated as a classification problem with one class being predicted and rare cases being predicted as numerical values. Mostly used ML models are neural networks using data segmentation. This article is categorized under: Technologies > Machine Learning Technologies > Prediction
The types of ML
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Dependence of accuracy to the number of references
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Progress of the ML models related to baseball
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Progress of the ML models related to cricket
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Progress of the ML models related to football
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Progress of the ML models related to soccer
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Progress of the ML models related to basketball
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Progress of the ML algorithms regardless of sport
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Dependence of accuracy vs. time and the number of citations
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Dependence of the number of features and seasons
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Dependence of accuracy to the number of selected features (outliers excluded)
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Dependence of accuracy to the number of selected features
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Dependence of accuracy to the number of seasons
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Median of accuracy, maximal accuracy, and number of occurrences of the proposed ML algorithm group and sport data pair within overall dataset. Data pair relevance is shown by intensity of the color, accuracies are shown by circle size and median is shown by its value as well
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Boxplot of the achieved maximum accuracies by sport
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Dataset segmentation (left) and k‐fold cross‐validation (right) evaluation methods
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Baseball prediction results in relation to other analyzed sports
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Cricket prediction results in relation to other analyzed sports
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Football prediction results in relation to other analyzed sports
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Number of papers using a particular ML algorithm group
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Soccer prediction results in relation to other analyzed sports
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Basketball prediction results in relation to other analyzed sports
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Number of analyzed papers by the publication year
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Technologies > Prediction
Technologies > Machine Learning

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