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Dynamic Criteria: a Longitudinal Analysis of Professional Basketball Players' Outcomes
Published online by Cambridge University Press: 10 January 2013
Abstract
This paper describes the fluctuations of temporal criteria dynamics in the context of professional sport. Specifically, we try to verify the underlying deterministic patterns in the outcomes of professional basketball players. We use a longitudinal approach based on the analysis of the outcomes of 94 basketball players over ten years, covering practically players' entire career development. Time series were analyzed with techniques derived from nonlinear dynamical systems theory. These techniques analyze the underlying patterns in outcomes without previous shape assumptions (linear or nonlinear). These techniques are capable of detecting an intermediate situation between randomness and determinism, called chaos. So they are very useful for the study of dynamic criteria in organizations. We have found most players (88.30%) have a deterministic pattern in their outcomes, and most cases are chaotic (81.92%). Players with chaotic patterns have higher outcomes than players with linear patterns. Moreover, players with power forward and center positions achieve better results than other players. The high number of chaotic patterns found suggests caution when appraising individual outcomes, when coaches try to find the appropriate combination of players to design a competitive team, and other personnel decisions. Management efforts must be made to assume this uncertainty.
En este artículo describimos las fluctuaciones en el tiempo del rendimiento de jugadores profesionales de baloncesto buscando patrones deterministas y de qué tipo son. Para ello, analizamos los resultados de 94 jugadores profesionales mediante un estudio longitudinal de series temporales de diez años de duración. Analizamos las series temporales utilizando las técnicas que se proponen desde la teoría de sistemas dinámicos no lineales. Mediante estas técnicas podemos descubrir los patrones subyacentes de los resultados sin tener que realizar asunciones previas sobre la linealidad o no linealidad de los datos, ni transformaciones de los mismos para que se ajusten a priori a una distribución. En los resultados encontrados, la mayoría de los jugadores muestran un patrón determinista (88.30%), de los cuales la mayoría son caóticos (81.92%) que obtienen mejores resultados que los lineales. El alto número de patrones caóticos encontrados parece indicar que debemos ser precavidos a la hora de evaluar y tomar decisiones sobre el rendimiento de los jugadores, y que la gestión de equipos debe asumir que la incertidumbre es una parte importante en este contexto.
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- Copyright © Cambridge University Press 2012
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