Explore the vast range of titles available.

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.

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.

The aim of this investigation was to study the technical and tactical evolution of the offensive team sequences in the Spanish football teams from 2008/09 to 2020/21. A comparative analysis including twelve variables related to the development of offensive sequences in 4940 matches was performed from 2008/09 to 2020/21 seasons of the Spanish professional football league (LaLiga). All match observations were recorded using a validated video tracking system. Multilevel linear mixed models were used to examine the differences across seasons, considering the effects of contextual variables. The number of passes per sequence (2.4 [CI: 2.2-2.5] vs 3.2 [CI: 3.0-3.4]; +33.3%), the passing accuracy (72.1 [CI: 70.6-73.5] vs 76.9 [CI: 75.4-78.3]%; +6.8%) and the average duration of the team sequences (6.4 [CI: 5.9-6.8] vs 8.3 [CI: 7.8-8.7] seconds; +25.76%) showed a small increasing trend over the seasons (P < 0.05). In contrast, variables such as the direct speed of progression (2.2 [CI: 2.1-2.3] vs 1.6 [CI: 1.5-1.7] metres/second; -24.5%), key passes (8.1 [CI: 7.6-8.5] vs 6.8 [CI: 6.3-7.2]; -15.8%), and the sequences that ended in the attacking third (64.8 [CI: 62,7-66.8] vs 57.1 [CI: 55.1-59.2]; -11.7%) or in a shot (13.0 [CI: 12.4-13.6] vs 10.2 [CI: 9.6-10.8]; -21.6%) showed a small decreasing trend from 2008/09 to 2020/21 (P < 0.05). Spanish professional football teams slightly evolved technically and tactically towards a more associative style of play that includes longer passing sequences. This evolution also involved a decreasing speed of progression and fewer technical actions such as through balls, key passes and shots.

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.

The aim of this study was to evaluate the influence of contextual factors on game styles in professional soccer. Interactions between styles and different playing venues, opposition quality, total match goals, and competing styles, were investigated using logistic regression and odds ratios. Game styles were characterised using the moments of play framework where three distinct styles have been identified: Style 1 - moderate strength in defence; Style 2 - dominance in transition, and Style 3 - strength in attacking phases of play. Results revealed that when playing at home against teams identified by Style 1, teams were more likely to play Style 2 (p < 0.05) or Style 3 (p < 0.001). Against top 10 opposition, teams were less likely to play Style 3 compared to either Style 1 (p < 0.001) or Style 2 (p < 0.001). Regardless of venue, teams were more likely to play Style 3 against bottom 10 sides compared to either Style 1 (p < 0.001) or Style 2 (p < 0.001), suggesting a hierarchical order between contextual factors. Competing game styles significantly impacted total match goals scored, whilst match results were also influenced by game style combinations. Overall, this study showed the significant effects of various contextual variables on game styles played by teams in the EPL.

Performance analysis is a rapidly evolving field in football and a subject of extensive international scientific research. Recognizing playing styles is now considered essential for effective performance analysis. This study aimed to create a map of 174 teams from 11 European leagues that could, through visualization, provide practical insights applicable to football teams' daily practice. The t-distributed Stochastic Neighbor Embedding (t-SNE) method was used to reduce the dimensions of 19 tactical situations derived from previous research. The resulting two coordinates were employed to generate a scatter plot, and simultaneous k-means cluster analysis (k = 11) was conducted. Greece (86%) and Scotland (83%) had the highest percentages of teams within the same cluster as their country's average, while Germany (11%) and Croatia (10%) had the lowest percentages. In terms of cluster dispersion, England ranked first with 9 clusters, followed by Spain and Germany with 7 clusters, while Greece and Scotland had the least with 2 clusters. The visualization and clustering of teams led to the following conclusions. a) There are variations in playing styles not only between teams from different countries but also within the same country, particularly when there is a disparity in quality. b) Coaches' philosophies and implemented strategies significantly influence the adoption of playing styles by teams. These findings provide valuable information for coaches, analysts, and team scouts, assisting them in their respective roles. By understanding the diverse playing styles present in European football, practitioners can tailor their approaches to optimize team performance and gain a competitive edge.

Performance analysis is a valuable tool for team coaches and has been the subject of extensive study in international research. A significant portion of the scientific literature in the field of football has been devoted to studying playing styles in recent years. The identification of playing styles is now regarded as crucial for conducting an efficient performance analysis. This study aimed to explore the variances in playing styles among eleven distinct European domestic football leagues. A comprehensive sample of 2996 matches, accounting for 5992 observations, was scrutinized. Nineteen latent variables, representing thirty-eight different game styles previously identified in sports science literature, served as the basis for this investigation. Multivariate analysis of variance (MANOVA) revealed significant differences across countries in ten out of nineteen variables. The variables with the highest effect sizes (partial r\\2) were transition game, effective game, and defending aggressively, implying that these factors contributed to the most substantial differences among countries. To visualize these disparities, the t-distributed stochastic neighbor embedding (t-SNE) method was employed. Subsequently, k-means clustering was applied to the t-SNE results, grouping the eleven participating countries into five distinct clusters. A unique playing style was discerned in the Scottish league (Cluster 4), setting it apart from all other leagues. Other clusters included Austria, Belgium, and Switzerland (Cluster 1); Spain, Turkey, and Croatia (Cluster 2); Greece and Italy (Cluster 3); and Germany and England (Cluster 5). The findings offer valuable insights for coaches, managers, scouts, and sporting directors, potentially guiding the development of effective game styles and enhancing recruitment strategies for both players and coaches.

Via a systematic review of the literature on learning games, this article presents a systematic discussion on the design of intrinsic integration of domain-specific learning in game mechanics and game world design. A total of 69 articles ultimately met the inclusion criteria and were coded for the literature synthesis. Exemplary learning games cited in the articles reviewed and developed by credible institutions were also analyzed. The cumulative findings and propositions of the game-based learning-play integration have been extracted and synthesized into five salient themes to clarify what, how, where, and when learning and content are embedded in and activated by gameplay. These themes highlight: (a) the types of game-based learning action—prior-knowledge activation and novel-knowledge acquisition, (b) the modes in which learning actions are integrated in game actions—representation, simulation, and contextualization, (c) the blended learning spaces contrived by game mechanics and the game world, (d) the occurrence of meta-reflective and iterative learning moments during game play, and (e) the multifaceted in-game learning support (or scaffolding). Future directions for the design and research of learning integration in digital games are then proposed.