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Archetypoid analysis for sports analytics
dc.contributor.author | Vinué Visús, Guillermo | |
dc.contributor.author | Epifanio, Irene | |
dc.date.accessioned | 2017-06-05T11:35:55Z | |
dc.date.available | 2017-06-05T11:35:55Z | |
dc.date.issued | 2017-06 | |
dc.identifier.citation | Vinué, G. & Epifanio, I. Archetypoid analysis for sports analytics. Data Min Knowl Disc (2017). doi:10.1007/s10618-017-0514-1 | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/167865 | |
dc.description.abstract | We 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.sponsorShip | This 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.extent | 34 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer Verlag | ca_CA |
dc.relation.isPartOf | Data Min Knowl Disc (2017) | ca_CA |
dc.rights | © 2017 Springer International Publishing AG. Part of Springer Nature. | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Archetype analysis | ca_CA |
dc.subject | Sports data mining | ca_CA |
dc.subject | Functional data analysis | ca_CA |
dc.subject | Extreme point | ca_CA |
dc.subject | Multidimensional scaling | ca_CA |
dc.subject | Performance analysis | ca_CA |
dc.title | Archetypoid analysis for sports analytics | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/ 10.1007/s10618-017-0514-1 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/sumittedVersion |
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