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dc.contributor.authorVinué Visús, Guillermo
dc.contributor.authorEpifanio López, Irene
dc.date.accessioned2017-06-05T11:35:55Z
dc.date.available2017-06-05T11:35:55Z
dc.date.issued2017-06
dc.identifier.citationVinué, G. & Epifanio, I. Archetypoid analysis for sports analytics. Data Min Knowl Disc (2017). doi:10.1007/s10618-017-0514-1ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/167865
dc.description.abstractWe intend to understand the growing amount of sports performance data by finding extreme data points, which makes human interpretation easier. In archetypoid analysis each datum is expressed as a mixture of actual observations (archetypoids). Therefore, it allows us to identify not only extreme athletes and teams, but also the composition of other athletes (or teams) according to the archetypoid athletes, and to establish a ranking. The utility of archetypoids in sports is illustrated with basketball and soccer data in three scenarios. Firstly, with multivariate data, where they are compared with other alternatives, showing their best results. Secondly, despite the fact that functional data are common in sports (time series or trajectories), functional data analysis has not been exploited until now, due to the sparseness of functions. In the second scenario, we extend archetypoid analysis for sparse functional data, furthermore showing the potential of functional data analysis in sports analytics. Finally, in the third scenario, features are not available, so we use proximities. We extend archetypoid analysis when asymmetric relations are present in data. This study provides information that will provide valuable knowledge about player/team/league performance so that we can analyze athlete’s careers.ca_CA
dc.description.sponsorShipThis work has been partially supported by Grant DPI2013-47279-C2-1-R. The databases and R code (including the web application) to reproduce the results can be freely accessed at www.uv.es/vivigui/software.ca_CA
dc.format.extent34 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringer Verlagca_CA
dc.relation.isPartOfData Min Knowl Disc (2017)ca_CA
dc.rights© 2017 Springer International Publishing AG. Part of Springer Nature.ca_CA
dc.subjectArchetype analysisca_CA
dc.subjectSports data miningca_CA
dc.subjectFunctional data analysisca_CA
dc.subjectExtreme pointca_CA
dc.subjectMultidimensional scalingca_CA
dc.subjectPerformance analysisca_CA
dc.titleArchetypoid analysis for sports analyticsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/ 10.1007/s10618-017-0514-1
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA


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