A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system
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| Format: | Artículo científico |
| Language: | en |
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Editorial Ramón Cantó Alcaraz
2017
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| _version_ | 1876429378222555136 |
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| author | César Soto-Valero |
| author_facet | César Soto-Valero |
| contents | A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system César Soto-Valero Multidisciplinarias (Ciencias Sociales) machine learning Association football principal component analysis Gaussian mixture clustering models classification and regression trees The generation and availability of football data has increased considerably last decades, mostly due to its popularity and also because of technological advances. Gaussian mixture clustering models represents a novel approach to exploring and analyzing performance data in sports. In this paper, we use principal com- ponents analysis in conjunction with a model-based Gaussian clustering method with the purpose of charac- terizing professional football players. Our model approach is tested using 40 attributes from EA Sports' FIFA video game series system, corresponding to 7705 European players. Clustering results reveal a clear dis- tinction among different performance indicators, representing four different roles in the team. Players were labeled according to these roles and a gradient tree boosting model was used for ranking attributes regar- ding to its importance. We found that the dribbling skill is the most discriminating variable among the diffe- rent clustered players’ profiles. 2017 artículo científico 1885-3137 https://www.redalyc.org/articulo.oa?id=71051616004 https://www.redalyc.org/journal/710/71051616004/ https://www.redalyc.org/journal/710/71051616004/html/ https://www.redalyc.org/journal/710/71051616004/71051616004.epub https://www.redalyc.org/journal/710/71051616004/movil en http://www.redalyc.org/revista.oa?id=710 RICYDE. Revista Internacional de Ciencias del Deporte application/pdf Editorial Ramón Cantó Alcaraz RICYDE. Revista Internacional de Ciencias del Deporte (España) Num.49 Vol.XIII |
| format | Artículo científico |
| id | redalyc_71051616004 |
| institution | Redalyc |
| language | en |
| publishDate | 2017 |
| publisher | Editorial Ramón Cantó Alcaraz |
| spellingShingle | A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system César Soto-Valero Multidisciplinarias (Ciencias Sociales) machine learning Association football principal component analysis Gaussian mixture clustering models classification and regression trees A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system César Soto-Valero Multidisciplinarias (Ciencias Sociales) machine learning Association football principal component analysis Gaussian mixture clustering models classification and regression trees The generation and availability of football data has increased considerably last decades, mostly due to its popularity and also because of technological advances. Gaussian mixture clustering models represents a novel approach to exploring and analyzing performance data in sports. In this paper, we use principal com- ponents analysis in conjunction with a model-based Gaussian clustering method with the purpose of charac- terizing professional football players. Our model approach is tested using 40 attributes from EA Sports' FIFA video game series system, corresponding to 7705 European players. Clustering results reveal a clear dis- tinction among different performance indicators, representing four different roles in the team. Players were labeled according to these roles and a gradient tree boosting model was used for ranking attributes regar- ding to its importance. We found that the dribbling skill is the most discriminating variable among the diffe- rent clustered players’ profiles. 2017 artículo científico 1885-3137 https://www.redalyc.org/articulo.oa?id=71051616004 https://www.redalyc.org/journal/710/71051616004/ https://www.redalyc.org/journal/710/71051616004/html/ https://www.redalyc.org/journal/710/71051616004/71051616004.epub https://www.redalyc.org/journal/710/71051616004/movil en http://www.redalyc.org/revista.oa?id=710 RICYDE. Revista Internacional de Ciencias del Deporte application/pdf Editorial Ramón Cantó Alcaraz RICYDE. Revista Internacional de Ciencias del Deporte (España) Num.49 Vol.XIII |
| title | A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system |
| topic | Multidisciplinarias (Ciencias Sociales) machine learning Association football principal component analysis Gaussian mixture clustering models classification and regression trees |
| url | https://www.redalyc.org/articulo.oa?id=71051616004 https://www.redalyc.org/journal/710/71051616004/ https://www.redalyc.org/journal/710/71051616004/html/ https://www.redalyc.org/journal/710/71051616004/71051616004.epub https://www.redalyc.org/journal/710/71051616004/movil |