TY - CONF
T1 - How everything is connected
T2 - NASPSPA
AU - Neumann, Niklas
AU - Van Yperen, Nico W.
AU - Brauers, Jur
AU - den Hartigh, Ruud
PY - 2023
Y1 - 2023
N2 - 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).
AB - 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).
KW - TV-VAR
KW - soccer
KW - football
KW - Time-varying
KW - performance
KW - sports
KW - time series analysis
KW - dynamic modeling
KW - network
KW - persoanlized approach
KW - resilience
KW - sport psychology
KW - interaction
KW - dynamic systems
UR - https://journals.humankinetics.com/view/journals/jsep/45/S1/article-pS1.xml
M3 - Abstract
Y2 - 1 June 2023 through 3 June 2023
ER -