Background/Objectives: The concepts of trust and distrust in health systems during health crises are significantly related to public adoption of health behaviors and health outcomes. The aim of this review is to examine trust and mistrust during times of health crisis, such as periods of viral pandemics and epidemics, and how these concepts relate to public health behaviors and, by extension, affect health outcomes. Methods: A focused search was conducted in PubMed and Scopus using a Boolean expression to identify relevant articles written in the English language and published between 2003 and 2023. The research methods comprised content analysis, thematic analysis, and a word cloud visualization to identify frequent themes related to trust and mistrust in healthcare systems. Results: The analysis included 57 studies and revealed that trust in healthcare systems, institutions, and professionals plays a significant role in shaping public health behaviors during health crises. The study also highlighted how mistrust leads to negative health outcomes, such as increased belief in conspiracy theories, vaccine hesitancy, and non-compliance with public health measures. Conclusions: The review emphasizes the importance of building and maintaining trust in healthcare systems to ensure better public health outcomes during crises. It calls for further research to better understand the nuances of trust and mistrust, aiming to inform public health strategies and interventions that could lead to more effective crisis management and improved health outcomes.

Understanding the performance indicators that contribute to the final score of a football match is crucial for directing the training process towards specific goals. This paper presents a pipeline for identifying key team-level performance variables in football using explainable ML techniques. The input data includes various team-specific features such as ball possession and pass behaviors, with the target output being the average scoring performance of each team over a season. The pipeline includes data preprocessing, sequential forward feature selection, model training, prediction, and explainability using SHapley Additive exPlanations (SHAP). Results show that 14 variables have the greatest contribution to the outcome of a match, with 12 having a positive effect and 2 having a negative effect. The study also identified the importance of certain performance indicators, such as shots, chances, passing, and ball possession, to the final score. This pipeline provides valuable insights for coaches and sports analysts to understand which aspects of a team’s performance need improvement and enable targeted interventions to improve performance. The use of explainable ML techniques allows for a deeper understanding of the factors contributing to the predicted average team score performance.

Identifying and measuring soccer playing styles is a very important step toward a more effective performance analysis. Exploring the different game styles that a team can adopt to enable a great performance remains under-researched. To address this challenge and identify new directions in future research in the area, this paper conducted a critical review of 40 research articles that met specific criteria. Following the 22-item Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, this scoping review searched for literature on Google Scholar, Web of Science, and Pub Med databases. The descriptive and thematic analysis found that the objectives of the identified papers can be classified into three main categories (recognition and effectiveness of playing styles and contextual variables that affect them). Critically reviewing the studies, the paper concluded that: (i) factor analysis seems to be the best technique among inductive statistics; (ii) artificial intelligence (AI) opens new horizons in performance analysis, and (iii) there is a need for further research on the effectiveness of different playing styles, as well as on the impact of contextual variables on them.

The game of soccer is complex and unpredictable, demanding multifaceted strategies for success. Performance analysis has evolved, focusing on key performance indicators (KPIs) to determine the factors that most significantly influence a team’s success or failure during matches. Traditional performance analysis methods have emphasized quantifiable data like physical exertion and basic play events but often neglected the subtler tactical dimensions that could significantly impact game outcomes. This study aimed to fill the gap in the current literature by creating a comprehensive framework that incorporates tactical situations as KPIs. The objective was to examine whether specific playing styles adopted by teams in various tactical situations and phases of the game could predict the outcome of matches. A dataset comprising all First Division Championship matches from 11 different European countries for the 2021–2022 season was analyzed. Variables representing tactical situations were correlated with match outcomes using a Generalized Estimating Equation framework. The model was specified with a binomial distribution and a logit link function. Statistical significance was determined using Wald χ² tests with a significance level set at p < 0.05. The study’s findings revealed that possession style, counterattacking during offensive transitions, and a balanced aggressive defensive strategy significantly increase a team’s chances of victory. It also showed that successful teams tend to focus on central attacks, minimize crossing, and execute strategic plays that lead to final attempts on goal with minimal ball possession. The above findings demonstrate that adopting certain tactical approaches significantly influences soccer match outcomes, highlighting the importance of considering tactical aspects as KPIs.

Identifying playing styles in football is highly valuable for achieving effective performance analysis. While there is extensive research on team styles, studies on individual player styles are still in their early stages. Thus, the aim of this systematic review was to provide a comprehensive overview of the existing literature on player styles and identify research areas required for further development, offering new directions for future research. Following the PRISMA guidelines for systematic reviews, we conducted a search using a specific strategy across four databases (PubMed, Scopus, Web of Science, and SPORTDiscus). Inclusion and exclusion criteria were applied to the initial search results, ultimately identifying twelve studies suitable for inclusion in this review. Through thematic analysis and qualitative evaluation of these studies, several key findings emerged: (a) a lack of a structured theoretical framework for player styles based on their positions within the team formation, (b) absence of studies investigating the influence of contextual variables on player styles, (c) methodological deficiencies observed in the reviewed studies, and (d) disparity in the objectives of sports science and data science studies. By identifying these gaps in the literature and presenting a structured framework for player styles (based on the compilation of all reported styles from the reviewed studies), this review aims to assist team stakeholders and provide guidance for future research endeavors.

Soccer biomechanics is a field that applies principles of mechanics to improve performance and reduce the risk of injury for soccer players. The purpose of this research is to perform a mapping review of the existing knowledge in the current literature, identify leading researchers and institutions, and explore emerging research topics. After conducting a mini literature review, a bibliometric analysis was carried out using the VOSviewer software. The data imported into VOSviewer was obtained from the Scopus database and included 951 records. The analysis performed included: (a) performance analysis to calculate the number of records per year and the authors with the most citations; (b) science mapping analysis (co-authorship, co-citation, and bibliographic coupling); and (c) clustering analysis through the co-occurrence analysis of author keywords. Our findings highlight a significant growth in research publications on soccer biomechanics over the past few decades, with a notable increase in the number of articles published in high-impact journals. Additionally, the clustering analysis resulted in seven clusters, with each cluster providing valuable insights into different aspects of soccer biomechanics, which clearly reflects the multifaceted nature of the field.