How everything is connected: Mapping performance determinants of athletes in personalized networks

Research in the field of sports performance has mostly been conducted (a) at a single point, or at most, a few points in time; (b) on the group level; and (c) as a causal chain of monodisciplinary predictor and outcome variables. In the present study, we argue and demonstrate that the next important step should be to monitor, analyze, and visualize the dynamic and individual-specific interactions of multidisciplinary determinants of sports performance. We, therefore, applied a recently developed analytical method, that is, (time-varying) vector-autoregressive (TV-VAR) modeling, which is particularly suited to capture the intra-individual interactions and changes of multidisciplinary determinants. We first measured critical psychological (e.g., confidence, motivation) and physiological (e.g., load, recovery) variables of youth male players from a professional soccer club on a daily basis across one season. Next, we assessed the temporal dynamics (i.e., autoregressive and cross-lagged effects) of those variables and visualized the findings in changing network graphs. We highlight the results of two players, showing how multidisciplinary key determinants of sports performance dynamically evolve across a season in individual-specific ways. For instance, the results of player 1 revealed a stable network across the season in which self-efficacy was the strongest predictor of other determinants. The network of player 2, however, revealed changing effects over time, more overall relations, and no central determinant. These new insights improve our understanding of how key determinants of sports performance are dynamically related in individual athletes. Accordingly, they may allow practitioners to develop and implement athlete-specific interventions at the right time. Future studies may apply (TV-)VAR models to study patterns in the networks of individual athletes in periods of performance gains and losses, injuries, or health problems. Funding source: ZonMw (Dutch organization for health research and innovation).