PURPOSE:: The influence of preceding load and perceived wellness on the future perceived wellness of professional soccer players is unexamined. This paper simultaneously evaluates the external and internal load for different time frames in combination with pre-session wellness to predict future perceived wellness using machine learning techniques.
METHODS:: Training and match data were collected from a professional soccer team. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE multiplied by duration. Predictive models were constructed using gradient boosted regression trees (GBRT) and one naive baseline method. The individual predictions of future wellness items (i.e., fatigue, sleep quality, general muscle soreness, stress levels, and mood) were based on a set of external and internal load indicators in combination with pre-session wellness. The external and internal load was computed for acute and cumulative time frames. The GBRT model's performance on predicting the reported future wellness was compared to the naive baseline's performance by means of absolute prediction error and effect size.
RESULTS:: The GBRT model outperformed the baseline for the wellness items fatigue, general muscle soreness, stress levels and mood. Additionally, only the combination of external load, internal load, and pre-session perceived wellness resulted in non-trivial effects for predicting future wellness. Including the cumulative load did not improve the predictive performances.
CONCLUSIONS:: The findings may indicate the importance of including both acute load and pre-session perceived wellness in a broad monitoring approach in professional soccer.
|Number of pages||7|
|Journal||International journal of sports physiology and performance|
|Early online date||31-Jan-2019|
|Publication status||Published - Sep-2019|
- global positioning system
- rating of perceived exertion
- athlete monitoring
- predictive modeling
- TRAINING LOAD
- TEAM SPORT