Machine Learning on Soccer Player Positions
During the last few years, sports analytics has been growing rapidly. The main usage of this discipline is the prediction of soccer match results, even if it can be applied with interesting results in different areas, such as analysis based on the player position information. In this paper, the auth...
Published in: | International Journal of Decision Support System Technology |
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Online Access: | https://hdl.handle.net/11695/113027 https://doi.org/10.4018/ijdsst.286678 |
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ftunivmoliseiris:oai:iris.unimol.it:11695/113027 2024-04-14T08:20:30+00:00 Machine Learning on Soccer Player Positions Umberto Di Giacomo Francesco Mercaldo Antonella Santone Giovanni Capobianco DI GIACOMO, UMBERTO ANTONIO Mercaldo, Francesco Santone, Antonella Capobianco, Giovanni 2022 https://hdl.handle.net/11695/113027 https://doi.org/10.4018/ijdsst.286678 eng eng info:eu-repo/semantics/altIdentifier/wos/WOS:000814639400016 volume:14 issue:1 firstpage:1 lastpage:19 numberofpages:19 journal:INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY https://hdl.handle.net/11695/113027 doi:10.4018/ijdsst.286678 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85149665664 Machine Learning Performance Analysi Soccer Analytic Sport Analytics info:eu-repo/semantics/article 2022 ftunivmoliseiris https://doi.org/10.4018/ijdsst.286678 2024-03-21T18:02:31Z During the last few years, sports analytics has been growing rapidly. The main usage of this discipline is the prediction of soccer match results, even if it can be applied with interesting results in different areas, such as analysis based on the player position information. In this paper, the authors propose an approach aimed to recognize the player position in a soccer match, predicting the specific zone in which the player is located in a specific moment. Similar objectives have not yet been considered. The authors consider supervised machine learning techniques by considering a dataset obtained through video capturing and tracking system. The data analyzed refer to several professional soccer games captured at the Alfheim Stadium in Tromso, Norway. The approach can be used in real time in order to verify if a player is playing according to the guidelines of the coach. In the experimental analysis, three different types of classification have been performed (i.e., three different divisions of the field), reaching the best results with random tree algorithm. Article in Journal/Newspaper Tromso Tromso Università degli Studi del Molise: IRIS Alfheim ENVELOPE(12.454,12.454,65.773,65.773) Norway Tromso ENVELOPE(16.546,16.546,68.801,68.801) International Journal of Decision Support System Technology 14 1 1 19 |
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Open Polar |
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Università degli Studi del Molise: IRIS |
op_collection_id |
ftunivmoliseiris |
language |
English |
topic |
Machine Learning Performance Analysi Soccer Analytic Sport Analytics |
spellingShingle |
Machine Learning Performance Analysi Soccer Analytic Sport Analytics Umberto Di Giacomo Francesco Mercaldo Antonella Santone Giovanni Capobianco Machine Learning on Soccer Player Positions |
topic_facet |
Machine Learning Performance Analysi Soccer Analytic Sport Analytics |
description |
During the last few years, sports analytics has been growing rapidly. The main usage of this discipline is the prediction of soccer match results, even if it can be applied with interesting results in different areas, such as analysis based on the player position information. In this paper, the authors propose an approach aimed to recognize the player position in a soccer match, predicting the specific zone in which the player is located in a specific moment. Similar objectives have not yet been considered. The authors consider supervised machine learning techniques by considering a dataset obtained through video capturing and tracking system. The data analyzed refer to several professional soccer games captured at the Alfheim Stadium in Tromso, Norway. The approach can be used in real time in order to verify if a player is playing according to the guidelines of the coach. In the experimental analysis, three different types of classification have been performed (i.e., three different divisions of the field), reaching the best results with random tree algorithm. |
author2 |
DI GIACOMO, UMBERTO ANTONIO Mercaldo, Francesco Santone, Antonella Capobianco, Giovanni |
format |
Article in Journal/Newspaper |
author |
Umberto Di Giacomo Francesco Mercaldo Antonella Santone Giovanni Capobianco |
author_facet |
Umberto Di Giacomo Francesco Mercaldo Antonella Santone Giovanni Capobianco |
author_sort |
Umberto Di Giacomo |
title |
Machine Learning on Soccer Player Positions |
title_short |
Machine Learning on Soccer Player Positions |
title_full |
Machine Learning on Soccer Player Positions |
title_fullStr |
Machine Learning on Soccer Player Positions |
title_full_unstemmed |
Machine Learning on Soccer Player Positions |
title_sort |
machine learning on soccer player positions |
publishDate |
2022 |
url |
https://hdl.handle.net/11695/113027 https://doi.org/10.4018/ijdsst.286678 |
long_lat |
ENVELOPE(12.454,12.454,65.773,65.773) ENVELOPE(16.546,16.546,68.801,68.801) |
geographic |
Alfheim Norway Tromso |
geographic_facet |
Alfheim Norway Tromso |
genre |
Tromso Tromso |
genre_facet |
Tromso Tromso |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000814639400016 volume:14 issue:1 firstpage:1 lastpage:19 numberofpages:19 journal:INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY https://hdl.handle.net/11695/113027 doi:10.4018/ijdsst.286678 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85149665664 |
op_doi |
https://doi.org/10.4018/ijdsst.286678 |
container_title |
International Journal of Decision Support System Technology |
container_volume |
14 |
container_issue |
1 |
container_start_page |
1 |
op_container_end_page |
19 |
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1796298822647808000 |