Archetypoid analysis for sports analytics
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comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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Título
Archetypoid analysis for sports analyticsFecha de publicación
2017-06Editor
Springer VerlagCita bibliográfica
Vinué, G. & Epifanio, I. Archetypoid analysis for sports analytics. Data Min Knowl Disc (2017). doi:10.1007/s10618-017-0514-1Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/sumittedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
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Data Min Knowl Disc (2017)Derechos de acceso
© 2017 Springer International Publishing AG. Part of Springer Nature.
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info:eu-repo/semantics/openAccess
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