Stress Drives Soccer Athletes' Wellness and Movement: Using Convergent Cross-Mapping to Identify Causal Relationships...

Area of Science:

• Sports Science
• Biomechanics
• Data Analysis

Background:

• Predicting athlete wellness is challenging due to the complex, dynamic interplay of variables.
• Traditional data collection and statistical methods often fail to capture causal effects in athlete wellness.
• Existing approaches focus on correlational relationships rather than dynamic interactions.

Purpose of the Study:

• To explore novel methods for predicting athlete wellness by examining dynamic, nonlinear relationships.
• To investigate potential causal links between external training load, stress, and other wellness indicators.
• To move beyond correlational analysis in sport science for a deeper understanding of athlete well-being.

Main Methods:

• Analysis of data from two soccer teams using attractor reconstruction methods.
• Examination of potential causal relationships between GPS/accelerometer-measured external training load and wellness variables.
• Focus on nonlinear dynamics to uncover complex interactions.

Main Results:

• Player self-rated stress was identified as a fundamental driving variable in athlete wellness.
• Stress was found to predict other components of athlete wellness.
• Player load data was shown to predict self-rated stress, indicating a feedback loop.

Conclusions:

• Nonlinear methods can effectively identify inter-variable interactions for predicting future athlete stress.
• The identified relationships suggest underlying causal mechanisms influencing athlete wellness.
• This approach offers a more dynamic and predictive understanding of athlete well-being throughout a season.