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Over the years, there has been a growing interest in the study of issues related to the psychophysiological processes underlying sports performance. A relatively recent perspective is supported by the concept that the brain acts as a central regulator of performance during exercise. This phenomenon is called pacing and is based on the premise that prior knowledge about the activity plays a fundamental role for individuals to self-regulate their efforts throughout the exercise. However, knowledge regarding this topic remains scarce, and further clarification is needed. This chapter reports new perspectives in relation to the existing evidence regarding the role of the brain as a central regulator of performance, questioning the complex interdependencies and interrelations between fatigue and physical exercise in the light of a psychophysiological perspective. A broader understanding of the cognitive basis of the psychophysiological phenomenon during the exercise is needed, bringing together concepts such as pacing behavior, decision-making, self-regulation of effort, prior knowledge of the duration of the task, and perception of effort.

Complex and dynamic systems are characterised by emergent behaviour, self-similarity, self-organisation and a chaotic component. In team sports as football, complexity and non-linear dynamics includes understanding the mechanisms underlying human movement and collective behaviour. Linear systems approaches in this kind of sports may limit performance understanding due to the fact that small changes in the inputs may not represent proportional and quantifiable changes in the output. Thus, non-linear approaches have been applied to assess training and match outcomes in football. The increasing access to wearable and tracking technology provides large datasets, enabling the analyses of time-series related to different performance indicators such as physiological and positional parameters. However, it is important to frame the theoretical concepts, mathematical models and procedures to determine metrics with physiological and behavioural significance. Additionally, physiological and behavioural data should be considered to determine the complexity and non-linearity of the system in football. Thus, the current chapter summarises the main methodological procedures to extract positional data using non-linear analyses such as entropy scales, relative phase transforms, non-linear indexes, cross correlation, fractals and clustering methods. This research was supported by Portuguese Foundation for Science and Technology, I.P. (project UIDB04045/2021). info:eu-repo/semantics/publishedVersion