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A framework of interpretable match results prediction in football with FIFA ratings and team formation
While forecasting football match results has long been a popular topic, a practical model for football participants, such as coaches and players, has not been considered in great detail. In this study, we propose a generalized and interpretable machine learning model framework that only requires coa...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101499/ https://www.ncbi.nlm.nih.gov/pubmed/37053253 http://dx.doi.org/10.1371/journal.pone.0284318 |
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author | Yeung, Calvin C. K. Bunker, Rory Fujii, Keisuke |
author_facet | Yeung, Calvin C. K. Bunker, Rory Fujii, Keisuke |
author_sort | Yeung, Calvin C. K. |
collection | PubMed |
description | While forecasting football match results has long been a popular topic, a practical model for football participants, such as coaches and players, has not been considered in great detail. In this study, we propose a generalized and interpretable machine learning model framework that only requires coaches’ decisions and player quality features for forecasting. By further allowing the model to embed historical match statistics, features that consist of significant information, during the training process the model was practical and achieved both high performance and interpretability. Using five years of data (over 1,700 matches) from the English Premier League, our results show that our model was able to achieve high performance with an F1-score of 0.47, compared to the baseline betting odds prediction, which had an F1-score of 0.39. Moreover, our framework allows football teams to adapt for tactical decision-making, strength and weakness identification, formation and player selection, and transfer target validation. The framework in this study would have proven the feasibility of building a practical match result forecast framework and may serve to inspire future studies. |
format | Online Article Text |
id | pubmed-10101499 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101014992023-04-14 A framework of interpretable match results prediction in football with FIFA ratings and team formation Yeung, Calvin C. K. Bunker, Rory Fujii, Keisuke PLoS One Research Article While forecasting football match results has long been a popular topic, a practical model for football participants, such as coaches and players, has not been considered in great detail. In this study, we propose a generalized and interpretable machine learning model framework that only requires coaches’ decisions and player quality features for forecasting. By further allowing the model to embed historical match statistics, features that consist of significant information, during the training process the model was practical and achieved both high performance and interpretability. Using five years of data (over 1,700 matches) from the English Premier League, our results show that our model was able to achieve high performance with an F1-score of 0.47, compared to the baseline betting odds prediction, which had an F1-score of 0.39. Moreover, our framework allows football teams to adapt for tactical decision-making, strength and weakness identification, formation and player selection, and transfer target validation. The framework in this study would have proven the feasibility of building a practical match result forecast framework and may serve to inspire future studies. Public Library of Science 2023-04-13 /pmc/articles/PMC10101499/ /pubmed/37053253 http://dx.doi.org/10.1371/journal.pone.0284318 Text en © 2023 Yeung et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Yeung, Calvin C. K. Bunker, Rory Fujii, Keisuke A framework of interpretable match results prediction in football with FIFA ratings and team formation |
title | A framework of interpretable match results prediction in football with FIFA ratings and team formation |
title_full | A framework of interpretable match results prediction in football with FIFA ratings and team formation |
title_fullStr | A framework of interpretable match results prediction in football with FIFA ratings and team formation |
title_full_unstemmed | A framework of interpretable match results prediction in football with FIFA ratings and team formation |
title_short | A framework of interpretable match results prediction in football with FIFA ratings and team formation |
title_sort | framework of interpretable match results prediction in football with fifa ratings and team formation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101499/ https://www.ncbi.nlm.nih.gov/pubmed/37053253 http://dx.doi.org/10.1371/journal.pone.0284318 |
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