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Purpose The primary purpose of this study is to show and guide how to construct a large variety of behaviors in systems dynamic for sport science. Methods By applying a simple algorithm to networked agents, which could, conceivably, offer a straightforward way out of the complexity, computational mechanism which along with its dynamics (payoff, failure, and strategy) involved. Results The model starts with the probability that systemic risk potential such as failure spreads. Even in a very random structure commonly used in sports field, propagation of the potential is guaranteed by an arbitrary network property of a set of elements. Despite the intensive systemic potential, the opposite type of potential like absence of failure could also be driven when there has been a strong investment in protection through a heuristically evolved protection level. Conclusion The implementation still needs to be polished against what happens in the real sports world, but in general, applied conceptual principle and methodological techniques behind the network-agent based mechanisms could be useful for researchers those who need to use systemes dynamic to guard in sport science.

Network-Agent systems dynamic modelling: A guide for sport science