Network-based centrality measures and physical demands in football regarding player position: Is there a connection? A preliminary study

Tactical Situations and Playing Styles as Key Performance Indicators in Soccer

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 Soccer Teams' Styles of Play: A Scoping and Critical Review

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 and Pub Med database. 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.

Evaluating Physical and Tactical Performance and Their Connection during Female Soccer Matches Using Global Positioning Systems

The objective of the present study was to evaluate the tactical and physical performance during official matches of a women's soccer league and to correlate both dimensions in periods of 15 min. To do this, eight official matches of a semi-professional soccer team belonging to the Women's Second Division of Spain (Reto Iberdrola) were analysed during the 2020-2021 season. The variables recorded were classified into two dimensions: tactical variables (i.e., Width, Length, Height and Surface Area) and physical variables (i.e., Total Distance Covered (TD), Total Distance Covered in High-Speed Running (HSR) and Total Distance Covered in Sprint). The main results were: (1) there were no differences between the periods in any of the tactical dimension variables; (2) in the physical dimension, a significant decrease in TD and HSR was described at the end of the match (period 60-75); and (3) some positive correlations were found among some variables of the tactical and physical dimension at the beginning and at the end of the match (periods 0-15, 60-75 and 75-90). The findings of the study suggest that connecting the tactical and physical dimension in the interpretation of team performance would allow for a better understanding of player and team performance and during competition.

Match Analysis in Team Ball Sports: An Umbrella Review of Systematic Reviews and Meta-Analyses

Background

Match analysis has evolved exponentially over the past decades in team sports resulting in a significant number of published systematic reviews and meta-analyses. An umbrella review of the available literature is needed to provide an integrated overview of current knowledge and contribute to more robust theoretical explanations of team performance.

Methods

The Web of Science (all databases), PubMed, Cochrane Library (Cochrane Database of Systematic Reviews), Scopus, and SPORTDiscus databases were searched for relevant publications prior to 19 February 2021. Appraisal of the methodological quality of included articles was undertaken using the tool for Assessing the Methodological Quality of Systematic Reviews (AMSTAR-2). Twenty-four studies were reviewed that met the following criteria: (1) contained relevant data from match analyses in team ball sports; (2) were defined as systematic reviews or/and meta-analyses; and (3) were written in the English language.

Results

The overall methodological quality of the 24 included reviews, obtained through the AMSTAR-2, revealed very low confidence ratings (Critically Low, n = 12) for the results of most systematic reviews of match analyses in team ball sports. Additionally, the results showed that research is focused mainly on four levels of analysis: (1) dyadic (microlevel); (2) individual (molecular level; predominant); (3) group (mesolevel), and (4) team dynamics (macrolevel). These levels of analysis included tactical, technical, physical, and psychosocial variables. Team performance was contextualized at two levels, with reference to: (1) match context (e.g. match status, match location, match period, quality of opposition) and (2) sociodemographic and environmental constraints (sex, age groups, competitive level, altitude, temperature, pitch surface).

Conclusions

The evolution of methods for match analysis in team ball sports indicates that: (1) an individual-level performance analysis was predominant; (2) the focus on intermediate levels of analysis, observing performance in dyadic and group interactions, has received less attention from researchers; (3) neglected areas of research include psychosocial aspects of team sports and women’s performance; and (4) analyses of match contexts need greater depth.

Registration: The protocol was registered in the International Platform of Registered Systematic Review and Meta-Analysis Protocols with the number 202080067 and the DOI number https://doi.org/10.37766/inplasy2020.8.0067.

Robustness and efficiency of international pesticide trade networks subject to link removal strategies

The international pesticide trade network (iPTN) is a key factor affecting global food production and food security. The trade relationship is a key component in iPTNs. In a complex international trade environment, we model the impacts of uncertain factors such as trade wars, economic blockades and local wars, as removing vital relationships in the trade network. There are many complex network studies on node centrality, but few on link centrality or link importance. We propose a new method for computing network link centrality. The main innovation of the method is in converting the original network into a dual graph, the nodes in the dual graph corresponding to the links of the original network. Through the dual graph, the node centrality indicators can measure the centrality of the links in the original network. We verify the effectiveness of the network link centrality indicator based on the dual graph in the iPTN, analyze the relationship between the existing network link centrality indicators and the indicator proposed in this paper, and compare their differences. It is found that the trade relationships with larger indicators (hub, outcloseness, outdegree) based on the dual graph have a greater impact on network efficiency than those based on the original pesticide trade networks.

A Longitudinal Exploration of Match Running Performance during a Football Match in the Spanish La Liga: A Four-Season Study

This study aimed to analyze and compare the match running performance during official matches across four seasons (2015/2016-2018/2019) in the top two professional leagues of Spanish football. Match running performance data were collected from all matches in the First Spanish Division (Santander; n = 1520) and Second Spanish Division (Smartbank; n = 1848), using the Mediacoach® System. Total distance and distances of 14-21 km·h-1, 21-24 km·h-1, and more than 24 km·h-1, and the number of sprints between 21 and 24 km·h-1 and more than 24 km·h-1 were analyzed. The results showed higher total distances in the First Spanish Division than in the Second Spanish Division (p < 0.001) in all the variables analyzed. Regarding the evolution of both leagues, physical demands decreased more in the First Spanish Division than in the Second Spanish Division. The results showed a decrease in total distance and an increase in the high-intensity distances and number of sprints performed, although a clearer trend is perceived in the First Spanish Division (p < 0.001; p < 0.01, respectively). Knowledge about the evolution of match running performance allows practitioners to manage the training load according to the competition demands to improve players' performances and reduce the injury rate.

Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior

Pattern analysis is a well-established topic in team sports performance analysis, and is usually centered on the analysis of passing sequences. Taking a Bayesian approach to the study of these interactions, this work presents novel entropy mathematical models for Markov chain-based pattern analysis in team sports networks, with Relative Transition Entropy and Network Transition Entropy applied to both passing and reception patterns. To demonstrate their applicability, these mathematical models were used in a case study in football—the 2016/2017 Champions League Final, where both teams were analyzed. The results show that the winning team, Real Madrid, presented greater values for both individual and team transition entropies, which indicate that greater levels of unpredictability may bring teams closer to victory. In conclusion, these metrics may provide information to game analysts, allowing them to provide coaches with accurate and timely information about the key players of the game.

Play-by-Play Network Analysis in Football

This study identifies dominant and intermediary players in football by applying a play-by-play social network analysis (SNA) on 70 professional matches from the 1. and 2. German Bundesliga during the 2017/2018 season. SNA provides a quantification of the complex interaction patterns between players in team sports. So far, the individual contributions and roles of players in football have only been studied at match-level considering the overall passing of a team. In order to consider the real structure of football, a play-by-play network analysis is needed that reflects actual interplay. Moreover, a distinction between plays of certain characteristics is important to qualify different interaction phases. As it is often impossible to calculate well known network metrics such as betweenness on play-level, new adequate metrics are required. Therefore, flow betweenness is introduced as a new playmaker indicator on play-level and computed alongside flow centrality. The data on passing and the position of players was provided by the Deutsche Fußball Liga (DFL) and gathered through a semi-automatic multiple-camera tracking system. Central defenders are identified as dominant and intermediary players, however, mostly in unsuccessful plays. Offensive midfielders are most involved and defensive midfielders are the main intermediary players in successful plays. Forward are frequently involved in successful plays but show negligible playmaker status. Play-by-play network analysis facilitates a better understanding of the role of players in football interaction.