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).
