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Published on: March 20, 2012
The role of eco-physical variables for analyzing and modeling goal-directed behavior in sport
Henrique Lopes1, Daniel Carrilho1, Margarida Vaz de Carvalho1
1CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz-Quebrada - Dafundo, Portugal.
Abstract:
This article clarifies the distinctive role of eco-physical variables in analyzing and modeling the dynamics of goal-directed behavior in sports. While behavior in sport is inherently purposeful and context-dependent, most current performance metrics neglect how athletes direct their actions toward task-relevant environmental properties. Although traditional indicators and continuous variables (e.g., collective, compound, and order parameters) have been widely used to quantify performance, they often conflate different constructs and remain neutral to situated intentionality. A conceptual clarification is necessary, as these metrics are frequently used interchangeably in the literature, leading to confusion among researchers and practitioners. Rooted in the theoretical approach of ecological dynamics, the concept of eco-physical variables seeks to address these issues by capturing how performers intentionally perceive and act in their environment to achieve task goals. These variables reflect how athletes act toward task goals, shaped by the socio-material properties of their performance environment. To our knowledge, this is the first article to focus exclusively on eco-physical variables, define them as a distinct class of metrics in sport, and outline their theoretical, methodological, and practical implications. It resolves conceptual ambiguity around similar terms and offers theory-informed guidance for developing variables that preserve dynamics and context. Specifically, this article: (a) reviews existing sports performance metrics; (b) overviews the ecological dynamics approach to sport performance; (c) defines eco-physical variables and reviews their application; (d) provides recommendations for their development and measurement; (e) explores their role in dynamical systems and artificial intelligence modeling; and (f) identifies debates, challenges, and future research.
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