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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Expected goals in football: Improving model performance and demonstrating value
Author(s): Mead, James
O’Hare, Anthony
McMenemy, Paul
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Issue Date: 2023
Date Deposited: 23-May-2023
Citation: Mead J, O’Hare A & McMenemy P (2023) Expected goals in football: Improving model performance and demonstrating value. Muazu Musa R (Editor) <i>PLOS ONE</i>, 18 (4), Art. No.: e0282295.
Abstract: Recently, football has seen the creation of various novel, ubiquitous metrics used throughout clubs’ analytics departments. These can influence many of their day-to-day operations ranging from financial decisions on player transfers, to evaluation of team performance. At the forefront of this scientific movement is the metric expected goals, a measure which allows analysts to quantify how likely a given shot is to result in a goal however, xG models have not until this point considered using important features, e.g., player/team ability and psychological effects, and is not widely trusted by everyone in the wider football community. This study aims to solve both these issues through the implementation of machine learning techniques by, modelling expected goals values using previously untested features and comparing the predictive ability of traditional statistics against this newly developed metric. Error values from the expected goals models built in this work were shown to be competitive with optimal values from other papers, and some of the features added in this study were revealed to have a significant impact on expected goals model outputs. Secondly, not only was expected goals found to be a superior predictor of a football team’s future success when compared to traditional statistics, but also our results outperformed those collected from an industry leader in the same area.
DOI Link: 10.1371/journal.pone.0282295
Rights: © 2023 Mead et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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