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Estadística multivariante aplicada al análisis y predicción de partidos de fútbol en las principales ligas europeas

dc.contributor.authorChocano Feito, Pedro José
dc.contributor.authorCastilla González, Elena María
dc.date.accessioned2023-06-16T14:27:14Z
dc.date.available2023-06-16T14:27:14Z
dc.date.issued2021-10-01
dc.description.abstractEl propósito de este estudio es analizar las estadísticas de juego en las principales ligas europeas y ver qué factores son más determinantes a la hora de predecir el resultado de un partido. Para ello usaremos técnicas de estadística multivariante incluyendo análisis de componentes principales y regresión logística. Las dos primeras componentes principales explican alrededor del 70 % de precisión obtenida cuando se predicen victorias fuera de casa tomando como variables predictivas las propias componentes. Este estudio también demuestra que en la liga inglesa los partidos son menos equilibrados.
dc.description.abstractThe purpose of this study is to analyse main game-related statistics differences between the main European leagues and which factors are more determinant when predicting a match score, by means of multivariate statistical techniques, including principal component analysis and logistic regression. The first two principal components explain around the 70 % of variance, and over a 70 % of accuracy is obtained when predicting away-team wins, with these two principal components as predictive variables. This study also shows that in English Premier League, games are less equilibrated.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.departmentDepto. de Álgebra, Geometría y Topología
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/77811
dc.identifier.issn2174-0410
dc.identifier.officialurlhttps://revista.giepm.com/wp-content/uploads/investigacion_estadistica.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.14352/5070
dc.issue.number2
dc.journal.titlePensamiento Matemático
dc.language.isospa
dc.page.final30
dc.page.initial21
dc.publisherUniversidad Politécnica de Madrid
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.subject.cdu519.22
dc.subject.keywordComponentes principales
dc.subject.keywordDesarrollo del juego
dc.subject.keywordRepresión logística
dc.subject.keywordNotational analysis
dc.subject.keywordMatch performance
dc.subject.keywordPrincipal components
dc.subject.keywordLogistic regression
dc.subject.ucmEstadística matemática (Matemáticas)
dc.subject.unesco1209 Estadística
dc.titleEstadística multivariante aplicada al análisis y predicción de partidos de fútbol en las principales ligas europeas
dc.title.alternativeMultivariate statistical techniques applied to the analysis and prediction of football matches in the main European leagues
dc.typejournal article
dc.volume.numberXI
dcterms.references[1] ARAYA, J., & LARKIN, P. Key performance variables between the top 10 and bottom 10 teams in the English premier league 2012/13 season. Hum Mov Health Coach Edu, 1, 17–29, 2014. [2] BARROS, R.,CUNHA, S., MAGALHAES, W., GUIMARAES, M., et al. Representation and analysis of soccer players’ actions using principal components. Journal of Human Movement Studies, 2006. [3] BROICH, H., MESTER, J., SEIFRIZ, F., & YUE, Z. Statistical analysis for the first Bundesliga in the current soccer season. Progress in Applied Mathematics, 7(2), 1–8, 2014. [4] DOBSON, S., & GODDARD, J. Modelling and forecasting match results in the English premier league and football league. In Economics, management and optimization in sports (pp. 59–77). Springer, 2004. [5] ELYAKIM, E., MORGULEV, E., LIDOR, R., MECKEL, Y., ARNON, M., & BEN-SIRA, D. Comparative analysis of game parameters between Italian league and Israeli league football matches. International Journal of Performance Analysis in Sport, 20(2), 165–179, 2020. [6] HARRELL JR, F. E., LEE, K. L., CALIFF, R. M., PRYOR, D. B., & ROSATI, R. A. Regression modelling strategies for improved prognostic prediction. Statistics in medicine, 3(2), 143–152, 1984. [7] LAGO-PEÑAS, C., LAGO-BALLESTEROS, J., DELLAL, A., & GÓMEZ, M. Game-related statistics that discriminated winning, drawing and losing teams from the Spanish soccer league. Journal of sports science & medicine, 9(2), 288, 2010. [8] LEPSCHY, H., WÄSCHE, H., & WOLL, A. How to be successful in football: a systematic review. The Open Sports Sciences Journal, 11(1), 2018. [9] LEPSCHY, H., WÄSCHE, H., & WOLL, A. Success factors in football: an analysis of the German Bundesliga. International Journal of Performance Analysis in Sport, 1–15, 2020. [10] MOURA, F. A., MARTINS, L. E. B., & CUNHA, S. A. Analysis of football game-related statistics using multivariate techniques. Journal of sports sciences, 32(20), 1881–1887, 2014. [11] PEDUZZI, P., CONCATO, J., KEMPER, E., HOLFORD, T. R., & FEINSTEIN, A. R. A simulation study of the number of events per variable in logistic regression analysis. Journal of clinical epidemiology, 49(12), 1373–1379, 1996. [12] PEÑA, D. Análisis de datos multivariante. Mc Graw Hill, 2002. [13] PEREZ-SÁNCHEZ, J. M., GÓMEZ-DENIZ, E., & DAVILA-CÁRDENES, N. A comparative study of logistic models using an asymmetric link: Modelling the away victories in football. Symmetry, 10(6), 224, 2018. [14] REILLY, T. A motion analysis of work-rate in different positional roles in professional football match-play. J. Human Movement Studies, 2, 87–97, 1976. [15] UEFA. Association club coefficients, 2019. [16] WILLOUGHBY, K. A. Winning games in canadian football: A logistic regression analysis. The College Mathematics Journal, 33(3), 215–220, 2002.
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