Sport competition as a dynamical self-organizing system

Abstract

The existence of structure in sport competition is implicated in the widespread practice of using the information gathered from a past contest to prepare for a future contest. Based on this reasoning, we previously analysed squash match-play for evidence of signature traits from among the stochastic relations between the various types of shot. The mixed findings from these analyses led us to re-analyse squash match-play as a dynamical system. Here, we extend this line of investigation with some suggestions as to how various sports might be described further within this theoretical framework. We offer some examples of dynamical interactions in dyadic (i.e. one vs one) and team (e.g. many vs many) sports, as well as some predictions from a dynamical systems analysis for these types of sports contests. This paper should serve to initiate further research into the complex interactions that occur in sport competition.

Spatiotemporal coordination behaviors in futsal (indoor football) are guided by informational game constraints

“…In both coordinate systems, position of the ball and the geometric center of both teams, obtained from the arithmetic mean of the five players per team, were obtained for all time samples after filtering, thereby yielding measures of lateral and longitudinal displacements (Cartesian coordinates), and angles and radial displacements (polar coordinates). Consistent with the underlying thesis of coupled oscillators being responsible for the selforganizing behaviors produced in sports contests (McGarry et al, 2002), visual inspection of the time series data revealed well-expressed peaks and troughs in approximately periodic fashion, as expected. Subtracting the mean value from the time series data (signals) ensures the resultant trajectory data circle the origin, a necessary step when subjecting data to relative phase analysis using the Hilbert transform (Palut & Zanone, 2005;Rosenblum, Pikovsky, Kurths, Schäfer, & Tass, 2001).…”

Learning design to facilitate interactive behaviours in Team Sports. [Diseños de aprendizaje para favorecer las interacciones en los deportes de equipo].

“…Such sub-groups might represent a defensive unit in association football or an attacking unit of backs in rugby union. The empirical description of collective actions in such sub-groups involves the same principle of identifying coordinative variables that describe players' interactive behaviours, functioning as a system (Kelso & Engstrom, 2006;McGarry, Anderson, Wallace, Hughes, & Franks, 2002). For that purpose previous research in football (Frencken & Lemmink, 2008;Frencken, Lemmink, Delleman, & Visscher, 2011) and basketball (Bourbousson, Seve, & McGarry, 2010b) have successfully identified coordinative variables such as a team's centroid, stretch index or surface area.…”

The variable and chaotic nature of professional golf performance

“…Dutt-Mazumder, Button, Robins, & Bartlett, 2011). Individual sports can involve a direct interaction with an opponent, as in squash and tennis; these interactions, although less complex than in team sports, have been modelled as self-organising dynamical systems (McGarry, Anderson, Wallace, Hughes, & Franks, 2002;Palut & Zanone, 2005). Golf is unique to these sports in that the players do not react directly to the actions of the opponent.…”