Background: Identifying key variables that predict sleep quality in youth athletes allows practitioners to monitor the most parsimonious set of variables that can improve athlete buy-in and compliance for athlete self-report measurement. Translating these findings into a decision-making tool could facilitate practitioner willingness to monitor sleep in athletes. Hypothesis: Key predictor variables, identified by feature reduction techniques, will lead to higher predictive accuracy in determining youth athletes with poor sleep quality. Study Design: Cross-sectional study. Level of Evidence: Level 3. Methods: A group (N = 115) of elite youth athletes completed questionnaires consisting of the Pittsburgh Sleep Quality Index and questions on sport participation, training, sleep environment, and sleep hygiene habits. A least absolute shrinkage and selection operator regression model was used for feature reduction and to select factors to train a feature-reduced sleep quality classification model. These were compared with a classification model utilizing the full feature set. Results: Sport type, training before 8 am, training hours per week, presleep computer usage, presleep texting or calling, prebedtime reading, and during-sleep time checks on digital devices were identified as variables of greatest influence on sleep quality and used for the reduced feature set modeling. The reduced feature set model performed better (area under the curve, 0.80; sensitivity, 0.57; specificity, 0.80) than the full feature set models in classifying youth athlete sleep quality. Conclusion: The findings of our study highlight that sleep quality of elite youth athletes is best predicted by specific sport participation, training, and sleep hygiene habits. Clinical Relevance: Education and interventions around the training and sleep hygiene factors that were identified to most influence the sleep quality of youth athletes could be prioritized to optimize their sleep characteristics. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

Short-term forecasting of performance in football is crucial in week-to-week decision making. The current study presented novel contributions regarding the considerations that should be accounted for in the prediction of match actions performed in competitive matches. First, the study examined whether the quantity and recency of training data used to build a prediction model significantly influenced predictive accuracy. Three prediction models were built with the exponential moving weighted average (EMWA) method, each differing in the quantity of training data used (three, five, and seven preceding match days). Next, the study examined if contextual constraints, such as type of match action being predicted, playing position, or player age, significantly influenced predictive accuracy. Match action data from players in the top five European leagues were collected from the 2014/2015 to the 2019/2020 seasons. The model trained using less but more recent data (three preceding match days) demonstrated the greatest accuracy. Next, within the offensive and defensive phases, match actions differed significantly in predictive accuracy. Lastly, significant differences were found in prediction accuracy between playing positions, whereby actions associated with the primary task of the playing position were more accurately predicted. These findings suggest that in the forecasting of individual match actions, practitioners should seek to train the prediction model using more recent data, instead of including as much data as possible. Furthermore, contextual constraints such as the type of action and playing position of the player must be keenly considered.

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Purpose The present study examined the relationship between playing style adaptability and team match performance indicators throughout the season. Three playing style adaptability metrics were analysed, namely, (1) flexibility (i.e., exhibiting a wide range of playing styles), (2) reactivity (i.e., adapting playing style based on opposition) and (3) imposition (i.e., executing predetermined playing style regardless of opposition). Methods Team playing styles were derived through a clustering analysis of 21,708 matches played in the top five male European leagues from 2014/15 to 2019/20. Spearman’s correlation was utilized to assess the association between the three playing style adaptability metrics and four team match performance indicators (e.g., shots taken in opposition penalty box; shots conceded in own penalty box; goals scored; goals conceded; and total wins). Results Playing style flexibility was positively associated with both offensive and defensive match performance indicators and win frequency. Conversely, playing style reactivity and imposition were negatively associated with these team match performance indicators. Conclusions Our results suggest that the capacity to exhibit a wide range of playing styles throughout a season is associated with greater team performance. Furthermore, it is possible that high performing teams are capable of functionally switching between playing style reactivity and imposition, depending on match dynamics.

This systematic review organizes the literature regarding the influence of contextual constraints on football match action profiles, in order to inform better practice when utilized a data-informed approach towards identifying and predicting high performing football players. Furthermore, the validity of examining “on-ball” match actions in competitive matches as an indicator of performance was also investigated. Based on the studies reviewed, task and environmental constraints were highlighted to be significantly influential on match actions performed, which suggests that recruitment strategies may be more successful if there were a greater emphasis on identifying players that best fit the constraints unique to the team, rather than recruiting the “best” player in the position. Additionally, the ability to adapt and successfully produce goal-directed behaviour in a variety of contexts may therefore be indicative of future high performance. Results from existing studies suggest that match actions performed in competitive matches can significantly distinguish between higher and lower performing teams or individuals. However, given the largely retrospective study designs of existing studies, a shift towards prospective study designs utilizing machine learning or statistical modelling is proposed to increase the practical applicability of theoretical findings.