Weekly Programming of Hamstring-Related Training Contents in European Professional Soccer.

Hamstring injuries in soccer continue to be a challenge for professionals who work with soccer players daily. Although its origin is multifactorial, the proper management of neuromuscular fatigue during the training microcycle is a very important factor to consider. There are no clear guidelines regarding the weekly distribution of certain exercises that demand the hamstrings. The main objective of this study was to describe the usual training practices of professional European soccer teams. An international observational survey design was applied to some of the strength and conditioning coaches of professional soccer teams. The survey included different neuromuscular demanding exercises for the hamstrings. For each exercise, the strength and conditioning coaches had to respond in relation to their frequency of use and timepoint depending on the day of the weekly microcycle. Although there is no strong consensus in this regard, there does seem to be a trend when applying certain exercises, especially on the days matchday-4 and matchday-3.

Predictive modeling of the ratings of perceived exertion during training and competition in professional soccer players.

OBJECTIVES: Evaluate the ability of predicting the ratings of perceived exertion from the external load variables in professional soccer players through a chronological perspective (i.e., past features values are considered additional features) through machine learning models by considering the playing position. DESIGN: Prospective cohort study. METHODS: Thirty-eight elite soccer players aged 19-27 years were observed during 151 training sessions, 44 matches across a full season. External load variables (58 derived from Global Positioning System and 30 from accelerometers) and the internal load derived from ratings of perceived exertion were collected for each player and each session and match. Machine learning models (linear regression, K-NN, decision trees, random forest, elastic net regression, XGBoost) were compared and interpreted in order to deepen the relationship between external load variables and ratings of perceived exertion according to the player position in a predictive perspective. RESULTS: Application of the machine learning models on the dataset provided enough predictive power to reduce the Root Mean Squared Error of 60 % from dummy predictions. The most accurate models (Root Mean Squared Error ≈ 1.1 for random forest and = 1 for XGBoost) highlighted a memory effect in subsequent ratings of perceived exertion values. Past ratings of perceived exertion values over one month were the strongest predicting factors of ratings of perceived exertion as compared to various external load indicators. CONCLUSIONS: The tree-based machine learning models showed statistically significant predictive ability, indicating valuable information for understanding the training load responses based on ratings of perceived exertion changes.