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A public data set of spatio-temporal match events in soccer competitions

Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for s...

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Autores principales: Pappalardo, Luca, Cintia, Paolo, Rossi, Alessio, Massucco, Emanuele, Ferragina, Paolo, Pedreschi, Dino, Giannotti, Fosca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6817871/
https://www.ncbi.nlm.nih.gov/pubmed/31659162
http://dx.doi.org/10.1038/s41597-019-0247-7
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author Pappalardo, Luca
Cintia, Paolo
Rossi, Alessio
Massucco, Emanuele
Ferragina, Paolo
Pedreschi, Dino
Giannotti, Fosca
author_facet Pappalardo, Luca
Cintia, Paolo
Rossi, Alessio
Massucco, Emanuele
Ferragina, Paolo
Pedreschi, Dino
Giannotti, Fosca
author_sort Pappalardo, Luca
collection PubMed
description Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.
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spelling pubmed-68178712019-10-30 A public data set of spatio-temporal match events in soccer competitions Pappalardo, Luca Cintia, Paolo Rossi, Alessio Massucco, Emanuele Ferragina, Paolo Pedreschi, Dino Giannotti, Fosca Sci Data Data Descriptor Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure. Nature Publishing Group UK 2019-10-28 /pmc/articles/PMC6817871/ /pubmed/31659162 http://dx.doi.org/10.1038/s41597-019-0247-7 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Pappalardo, Luca
Cintia, Paolo
Rossi, Alessio
Massucco, Emanuele
Ferragina, Paolo
Pedreschi, Dino
Giannotti, Fosca
A public data set of spatio-temporal match events in soccer competitions
title A public data set of spatio-temporal match events in soccer competitions
title_full A public data set of spatio-temporal match events in soccer competitions
title_fullStr A public data set of spatio-temporal match events in soccer competitions
title_full_unstemmed A public data set of spatio-temporal match events in soccer competitions
title_short A public data set of spatio-temporal match events in soccer competitions
title_sort public data set of spatio-temporal match events in soccer competitions
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6817871/
https://www.ncbi.nlm.nih.gov/pubmed/31659162
http://dx.doi.org/10.1038/s41597-019-0247-7
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