· Universidade Federal de Minas Gerais
· Arsenal Football Club
In football, the incidence of muscle injuries remains high, despite several studies on their aetiology and prevention (Ekstrand et al., 2011). Traditionally, the investigation of risk factors for sports injuries has concentrated on linear and unidirectional causality (Arnason et al., 2004, Gabbe et al., 2006 and Engebretsen et al., 2010). However, injury (and muscle injury included) arises from the complex interaction among a web of determinants. This approach can be useful in an attempt to understand the sports injury aetiology and it may allow mapping of the interactions among potential risk factors and allow the development an athlete's ‘risk profile’ (Bittencourt et al., 2016).
Data analysis will be performed using alternative approaches; (1) Classification and Regression Trees (CART), which captures nonlinear relationships between predictors and produces results easily applied in clinical practice; and (2) Direct acyclic graphs (DAG) that allows systematic representations of causal relationships and validates the CART outcomes.
The aim of this research project is to identify a web of determinants to better understand how and why muscle injuries may occur in elite football players