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Performance Analysis Tool for network analysis on team sports: A case study of FIFA SoccerWorld Cup 2014
Abstract
The study of teammates’ interaction on team sports has been growing in the last few years. Nevertheless, no specific software has been developed so far to do this in a user-friendly manner. Therefore, the aim of this study was to introduce software called the Performance Analysis Tool (PATO) that allows the user to quickly record the teammates’ interaction and automatically generate the outputs in adjacency matrices that can then be imported by social network analysis software such as SocNetV. Moreover, it was also the aim of this study to process the data in a real life scenario, thus the 7 matches of the German national soccer team in the FIFA World Cup 2014 were used to test the software and then compute the network metrics. A dataset of 3,032 passes between teammates in 7 soccer matches was generated with the PATO software, which permitted a study of the network structure. The analysis of variance of centrality metrics between different tactical positions was made. The two-way MANOVA revealed that the strategic position (γ= 1.305; F = 24.394; p = 0.001; η_p^2= 0.652; large effect size) had significant main effects on the centrality measures. No statistical differences were found in the phase of competition (γ= 0.003; F = 0.097; p = 0.907; η_p^2= 0.003; very small effect size). The network approach revealed that the German national soccer team based their attacking process on positional attacks and not in counter-attack, and the midfielders were the prominent players followed by the central defenders. The PATO software allowed the user to quickly identify the teammates’ interactions and extract the network data for process and analysis.
... Sequences of passes between players can be represented as a network with players as the vertices and weighted edges for the frequency of passes between pairs of players and so to quantify the passing performance (Clemente, Martins, Wong, Kalamaras, & Mendes, 2015c;Grund, 2012). The network process can quantify different levels of analysis, from player-team (micro) such as degree centrality, degree prestige, betweenness centrality and closeness centrality (Clemente et al., 2016b(Clemente et al., , 2016aClemente, Silva, Martins, Kalamaras, & Mendes, 2016c); dependence between players (meso) such as clustering coefficient or scaled connectivity (Clemente et al., 2015b;Gama, Couceiro, Dias, & Vas, 2015;Peña & Touchette, 2012); and, the general properties of a team (macro) such as density, heterogeneity or network diameter (Clemente, Couceiro, Martins, & Mendes, 2015a;Clemente, Martins, Couceiro, Mendes, & Figueiredo, 2014), allowing to characterize the teammates' interaction during the offensive phase, providing specific information about passes connections that can be useful for match analysis . ...
... This kind of results can be justified by the more unstructured game (e.g., usually one of the teams is losing and takes more risks in defence) in second halves and by greater spaces to exploit counter-attacking (Clemente et al., 2016b) but also substitutions, the need to defend the current score or player fatigue could influence these data. Furthermore, networks have been used for comparing playing positions (Clemente et al., 2015c(Clemente et al., , 2016cPeña & Touchette, 2012). Probably, midfielders are the most prominent players in football (Clemente et al., 2015b;Peña & Touchette, 2012). ...
... The simplest centrality measure is the number of individual ball possessions that each player had in the match, which is the number of edges incident to a vertex. In some cases (Clemente et al., 2016c) both in-degree and outdegree centrality can be distinguished. Regarding this, the out-degree centrality is simply referred to as degree centrality while the in-degree centrality is usually called the prestige of a player. ...
This study’s main objective is to analyse the relationship between network-based centrality measures and physical demands in elite football players. Thirty-six matches from La Liga, the Spanish league, were analysed in the 2017/18 season. The analysis of networks formed by team players passing the ball included: degree-prestige (DP), degree-centrality (DC), betweenness-centrality (BC), page-rank (PRP) and closeness-centrality (IRCC). A video-based system was used for analysing total distance (TDpos) and distance run >21Km/h (TD21pos) when the team was in possession of the ball. A magnitude-based inference and correlation analysis were applied. There were different styles of play, team-A was characterized by greater ball circulation (e.g. higher values of DP, DC, BC and IRCC) while team-B used a more direct game (lower values in centrality-metrics except with PRP). Furthermore, TDpos was higher in team-A than in team-B, but those differences disappeared for TD21pos between teams with the exception of the forwards. Finally, the correlation among centrality measures and physical performance were higher in team-B. Coaches could identify the key opponents and players who are linked to them, allowing to adjust performance strategies. Furthermore, interaction patterns between teammates can be used to identify preferential paths of cooperation and to take decisions regarding these relations in order to optimize team performance.
... The network process can quantify the centrality level of a player (individual values per player), dependence between players (meso-level of analysis) and the general properties of a graph (that quantify a value of a specific network property of a team). General network properties have been studied in association with team performance variables such as shots, goals and successful outcomes in competition [30,71,72]. High passing rates were related to an increase in team performance and greater centralization was associated with a decrease, defined by the number of goals scored in an analysis of 760 matches from the English Premier League [30]. ...
... Variance of centrality measures between playing positions has also been analyzed [32,72]. Midfielders were classified as the most prominent players after observations of 64 official matches from the 2014 FIFA World Cup, independent of the specific team and tactical format used [32]. ...
... Midfielders were classified as the most prominent players after observations of 64 official matches from the 2014 FIFA World Cup, independent of the specific team and tactical format used [32]. The specific analysis conducted on the German team revealed that midfielders had greater levels of intermediation (capacity of a player to link two or more teammates to each other during play on field) and dominance (capacity to be the player who most often participates in team networks) [72]. ...
Background
Evolving patterns of match analysis research need to be systematically reviewed regularly since this area of work is burgeoning rapidly and studies can offer new insights to performance analysts if theoretically and coherently organized. Objective
The purpose of this paper was to conduct a systematic review of published articles on match analysis in adult male football, identify and organize common research topics, and synthesize the emerging patterns of work between 2012 and 2016, according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Methods
The Web of Science database was searched for relevant published studies using the following keywords: ‘football’ and ‘soccer’, each one associated with the terms ‘match analysis’, ‘performance analysis’, ‘notational analysis’, ‘game analysis’, ‘tactical analysis’ and ‘patterns of play’. ResultsOf 483 studies initially identified, 77 were fully reviewed and their outcome measures extracted and analyzed. Results showed that research mainly focused on (1) performance at set pieces, i.e. corner kicks, free kicks, penalty kicks; (2) collective system behaviours, captured by established variables such as team centroid (geometrical centre of a set of players) and team dispersion (quantification of how far players are apart), as well as tendencies for team communication (establishing networks based on passing sequences), sequential patterns (predicting future passing sequences), and group outcomes (relationships between match-related statistics and final match scores); and (3) activity profile of players, i.e. playing roles, effects of fatigue, substitutions during matches, and the effects of environmental constraints on performance, such as heat and altitude. Conclusion
From the previous review, novel variables were identified that require new measurement techniques. It is evident that the complexity engendered during performance in competitive soccer requires an integrated approach that considers multiple aspects. A challenge for researchers is to align these new measures with the needs of the coaches through a more integrated relationship between coaches and researchers, to produce practical and usable information that improves player performance and coach activity.
... Clemente et al. [26] Seven matches of the German national football team at the 2014 FIFA World Cup. ...
... In this way, network analyses allow us to identify the location of players with values of density, total links and grouping coefficients between the connections made [34], the influence of ball possession [30,44], determining the center of mass of the team as a key player and the positional average of all players [26], establishing movement predictability, type of organization and dynamic assemblies based on the needs of the team's tactical behavior, involving the shooting zones in relation to contextual variables within the offensive sequence to score a goal and explore goal opportunities [27,33,47]. ...
The aim of this study is to examine the most significant literature on network analyses and factors associated with tactical action in football. A systematic review was conducted on Web of Science, taking into account the PRISMA guidelines using the keyword "network", associated with "football" or "soccer". The search yielded 162 articles, 24 of which met the inclusion criteria. Significant results: (a) 50% of the studies ratify the importance of network structures, quantifying and comparing properties to determine the applicability of the results instead of analyzing them separately; (b) 12.5% analyze the process of offensive sequences and communication between teammates by means of goals scored; (c) the studies mainly identify a balance in the processes of passing networks; (d) the variables allowed for the interpretation of analyses of grouping metrics, centralization, density and heterogeneity in connections between players of the same team. Finally, a systematic analysis provides a functional understanding of knowledge that will help improve the performance of players and choose the most appropriate response within the circumstances of the game.
... Other studies examined team collective structures through ball circulation patterns (Clemente, Silva, Martins, Kalamaras& Mendes, 2016;Gonçalves, et al., 2017;Oliveira, Clemente&Martins, 2016;Oliveira & Clemente, 2018;Seabra, 2010). Some of these analysis focused on the pass action, a main communication element between team players (Clemente, Silva, Martins, Kalamaras& Mendes, 2016;Gonçalves, et al., 2017;Oliveira, Clemente &Martins, 2016;Oliveira & Clemente, 2018), providinga quantifiable view of ball circulation (Oh, Keshri&Iyengar,2015). ...
... Other studies examined team collective structures through ball circulation patterns (Clemente, Silva, Martins, Kalamaras& Mendes, 2016;Gonçalves, et al., 2017;Oliveira, Clemente&Martins, 2016;Oliveira & Clemente, 2018;Seabra, 2010). Some of these analysis focused on the pass action, a main communication element between team players (Clemente, Silva, Martins, Kalamaras& Mendes, 2016;Gonçalves, et al., 2017;Oliveira, Clemente &Martins, 2016;Oliveira & Clemente, 2018), providinga quantifiable view of ball circulation (Oh, Keshri&Iyengar,2015). In complement, other studyassessed ball circulation on the pitch by considering its degree of penetration in the defensive system and the relation to offensive success. ...
Introduction:This study aims to develop a method for characterizingthe offensive playing style of soccer teams, in terms of its ball circulation profile, and respective efficiency, considering the ball circulation outcomes.Methods:Ball circulation dynamics were categorized based on a descriptive representation of ball possession parts (start, ball path and end) and encompassing four variables: team; action or event; pitch partition; opponent defensive penetration degree. The resultant set of ball circulation dynamics encompassed all possibilities of ball circulation on the pitch and were categorized considering the degree of success of the ball circulation lead to a penetration in the defensive system. Hence, there were defined two main classes-incomplete and complete penetration dynamics. Incomplete penetration dynamics are those that do not reach the last defensive line of the opponent defensive system. Complete penetration dynamics are those in which the offense successfully penetrates the ball until the last defensive line or overcomes the last defensive line. Complete penetration dynamics were divided in vertical penetration, indirect penetration and start in penetration. We applied the set of ball circulation related variables to assess nine games from the finalists of UEFA Champions League season 2008-2009, Barcelona and Manchester United (final game, four semifinal games and four quarter final games). An assessment was performed through an automatic identification of game events using a finite state machine (FSM) software that selectively searched for particular classes of coding sequences in the data of ball circulation classes manually acquired from video footage. Results:We identified significant differences between Barcelona and Manchester United in terms ofthe ball circulation style in the classes: i) incomplete penetration dynamics in defensive pitch; ii) long ball kick on incomplete penetration dynamics in defensive pitch; iii) a back-circulation pass on incomplete penetration dynamics in offensive pitch. No differences were found for penetration styles.Discussion:In regards to ball circulation, more than one third of the dynamics from both teams reached the penetration zone of the opponent defense or ended with an effective offensive action (i.e. shot on goal or a cross, without a penetration). Barcelona and Manchester presented significantly more incomplete dynamics, respectively, in the offensive pitch (33.6% BAR and 25.2% MUN) and in defensive pitch (43.5% MUN and 34.9% BAR).Conclusions:These findings provide meaningful variables of ball circulation in soccer that may be used by coaches simply gathering data from video footages.
... Moreover, a distinction between dominant and intermediary players on play-level is provided. Building on Clemente et al. (2016b), dominant players on matchlevel are frequently involved in interplay while intermediary players link other teammates during a match. ...
... While our analysis presents CDs as the most involved and intermediary playing position, most studies traditionally ascribe midfielders the most dominant and intermediary role in football (Cotta et al., 2013;Clemente et al., 2015Clemente et al., , 2016b. There is also literature that positions forward (Clemente et al., 2016a) and EDs (Gama et al., 2014) as intermediary players. ...
This study identifies dominant and intermediary players in football by applying a play-by-play social network analysis on 70 professional matches from the 1. and 2. German Bundesliga during the 2017/2018 season. Social network analysis 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. Forwards 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.
... In a different study, the frequencies of passing interactions within the same team in 5-min intervals have been analyzed and found that when a player who touched the ball many times changes the player to whom he was connected by passes and this is called hub-switching behavior (Yamamoto and Yokoyama, 2011). Another research suggests that network in the second half of the game network density decreases, heterogeneity increases and centralization decreases (Clemente et al., 2015). However, we know that if all players have the same centrality, the homogeneity level will be high. ...
... Also another study shows that, high levels of interaction between teammates (density) led to increased team performance" (Clemente et al., 2015). We have also conflicting relations between density and success, "…which eventually leads to victory…" where in our findings the density of the pass network decreases with time (Cotta et al., 2013). ...
Network science is an emerging field. The purpose of this study is to investigate soccer attacks by using network science. In this study, by applying network science approach, four Turkish National Football Team's attacks analyzed with an open-source NodeXL program. We have focused on two types of attacks: the attacks that end with goals and the ones that don't. Our main aim is to see whether there is a difference between the network metrics of these two types of attacks? Using network metrics, for attacks in a same match we couldn't find important differences but we have found real differences for networks' metrics when opponent team changes. Our findings also support that micro measures can be used for new line-up's. First of all, it should be mentioned that our study is a case study and the results of this study should not be generalized. However, our findings can be the start point for further researches with larger samples sizes. With the help of network science approach, the most effective players could be found, the most compatible line-up for the future games could be chosen and the opponent team's key players could be analyzed.
... Some of the authors, such as Tromp and Holmes [25], Monteiro et al. [26], Franchini et al. [27], Gimenez-Egido et al. [12], García-de Alcaraz et al. [28] and Ortega-Toro et al. [29], attempt to provide a solution for estimating the impact on performance after rule changes, process model analysis by presenting a solution as an example. The approaches to team analysis are discussed in detail by a number of authors.Young et al. [30], Ortega et al. [31], Travassos et al. [32], Korte and Lames [33], Clemente et al. [34] and Laporta et al. [35] discussed a social network analysis. Paulo et al. [36], Kusmakar et al. [37], Woods et al. [38], Araújo and Davids [39] and Vilar et al. [40] offer the ecological dynamics approach for team analysis. ...
Athletes, both professional and amateur, are always looking for ways to improve their performance. With the introduction and increasing availability of modern technologies and smart devices arose the need to measure and analyze performance, but likewise, the use of these innovations as a competitive advantage also arose. Scientific publications reflect the wide range of available approaches and technologies, as well as the growing interest in various sports. As a result, we concentrated on a systematic review of publications that presented performance analysis tools and methods in all sports, with a final focus on racket sports. Clarivate Analytics’ Web of Science (WoS) and Elsevier Inc.’s SCOPUS databases were searched for 1147 studies that conducted performance analysis and sports research and were published in English. The data in the systematic review are current, up until 18 May 2021. A general review was performed on 759 items, and then 65 racket sports publications were thoroughly scrutinized. We concentrated on performance data, data collection and analysis tools, performance analysis methods, and software. We also talked about performance prediction. In performance research, we have identified specific approaches for specific sports as well as key countries. We are also considering expanding performance analysis in to E-sports in the future.
... In sports analysis the out-degree centrality is simply referred to as centrality while the in-degree centrality is usually called the prestige of a player. Some papers do consider both centrality and prestige, see for example Clemente et al. [24], but most of the literature has focused on centrality. Fewell et al. [28] considered a transition graph on basketball games where the vertices represented the five traditional player positions (point guard, shooting guard, small forward, power forward, and center), possession origins and possession outcomes. ...
Team-based invasion sports such as football, basketball and hockey are similar in the sense that the players are able to move freely around the playing area; and that player and team performance cannot be fully analysed without considering the movements and interactions of all players as a group. State of the art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across many disciplines, to extract insight from the trajectories. We survey recent research efforts that use spatio-temporal data from team sports as input, and involve non-trivial computation. This article categorises the research efforts in a coherent framework and identifies a number of open research questions.
... Because no significant difference was observed in the centrality metrics between these two positions, both guard positions were treated as central positions. Furthermore, in soccer, the (left, central, and right) midfielders were the prominent players followed by the central defenders [30]. Again, because no significant differences emerged between the centrality metrics of the two positions, we defined both the midfielder positions and the central defender position as central positions. ...
Research aims. The present article provides a comprehensive examination of the relationship between playing position and leadership in sport. More particularly, it explores links between leadership and a player’s interactional centrality — defined as the degree to which their playing position provides opportunities for interaction with other team members. This article examines this relationship across different leadership roles, team sex, and performance levels.
Results. Study 1 (N = 4443) shows that athlete leaders (and the task and motivational leader in particular) are more likely than other team members to occupy interactionally central positions in a team. Players with high interactional centrality were also perceived to be better leaders than those with low interactional centrality. Study 2 (N = 308) established this link for leadership in general, while Study 3 (N = 267) and Study 4 (N = 776) revealed that the same was true for task, motivational, and external leadership. This relationship is attenuated in sports where an interactionally central position confers limited interactional advantages. In other words, the observed patterns were strongest in sports that are played on a large field with relatively fixed positions (e.g., soccer), while being weaker in sports that are played on a smaller field where players switch positions dynamically (e.g., basketball, ice hockey). Beyond this, the pattern is broadly consistent across different sports, different sexes, and different levels of skill.
Conclusions. The observed patterns are consistent with the idea that positions that are interactionally central afford players greater opportunities to do leadership — either through communication or through action. Significantly too, they also provide a basis for them to be seen to do leadership by others on their team. Thus while it is often stated that “leadership is an action, not a position,” it is nevertheless the case that, when it comes to performing that action, some positions are more advantageous than others.
... The importance of midfielders for the attacking process (they received and played the most passes) was shown for the Swiss national team (Clemente et al. 2015a) as well as for Portugal (Mendes et al. 2015) and Germany (Clemente et al. 2015d). Additionally, it was possible to characterize that the German national soccer team's attacking process as being based on positional attack instead of counter-attack, using short passes and involving almost every player in the passing process. ...
Purpose: Tactical analyses to distinguish between football teams that were more or less successful have been conducted up to now only by means of linear methods (like discriminant analysis). Concerning the non-linear relationships between performance related conditions, performance and success in sports games, a non-linear method could be more appropriate.
Methods & Results: Therefore, all knockout matches played during FIFA World Cup 2014 were analysed using tactical metrics. Results lead to 4 different dimensions (Transition play, Creating scoring opportunities, Defense and Scoring) from which especially the latter was essential to differentiate between winners, drawers and losers. Linear discriminant analysis identified 43.30% of the cases correctly whereas a non-linear artificial neural network (ANN) lead to a successful classification of 57.85% all together.
Conclusion: Considering that the differences concerning the tactical behaviour between more and less successful teams were small due to the homogeneous level of performance, the results of the discrimination by means of artificial neural networks indicate non-linear to be more adequate compared to linear methods for future analyses of sports games.
Practical Implications: For sport scientists: The results of our study indicate a superior classification by means of the non-linear model of Artificial Neural Networks compared to the linear model of Discriminant Analysis. Therefore, it is suggested that these kind of analyses may be suited better to model the complex relations between performance indicators and match success in sports games.
For sport practitioners: It is shown that the behaviour during scoring is by far most important for differentiating between more or less successful teams in FIFA World Cup 2014. Different teams used different playing styles, that were equally successful, to create scoring opportunities. But there is no way around a good ratio between goals and scoring opportunities. This implies that either successful teams take shots only from situations that are more promising or they are more successful in scoring in equally promising opportunities.
... We recorded in weighted adjacency matrices the interactions between colleagues, codified as number of passes. The network measurements have been processed with the Performance Analysis Tool that is a software that allow to visualize the graphs and compute the network measurements (Clemente, Silva, Martins, Kalamaras & Mendes, 2016). Closeness centrality was used to check the proximity of how close is such player to its peers. ...
Children, when playing, are communicating, since this is one of the forms of disinhibition and interaction with the group in which they are inserted (Lima, 2014). Ten children, five boys (n = 5; 5.8 ± 0.4 years of age) and five girls (n = 5; 5.6 ± 0.5 years of age) from a primary school were observed and video recorded during classes on motor expression. These classes took place once a week for a month, lasting 1 hour for each session. It was counted as an interaction when a child passed a ball to another child. The results suggest that it was in the " Free Game " that there were significant differences between the types of games, since it was found in this one that it was easier for a player to be connected with his colleagues, being later the most requested, at the moment they had to pass the ball. Between the type of game and the gender, no significant interactions were found, as children who were the ones most requested by their classmates when they had to pass the ball. However, no significant interactions were found, as children who were crucial to maintaining cross-pass connections. With regard to gender, no significant differences were found because no child, either male or female, had become important in sustaining the connections between the passes and because no boys and/or girls were the most requested to make passes with the ball. Finally, among the game types, no significant differences were found, since the children did not become essential to maintain the connection of passes between colleagues, in any of the matches played. The main objective of this study is to compare the interaction between female and male children in cooperative-oposition games through Social Network Analysis.
... The reliability is also proper for scientific proposal considering that such did not have significant differences in any distance covered or speed between sessions in the same independent study (University of Brighton, School of Sport and Service Management, 2016). The raw position data was then extracted from the units and imported in the software Ultimate Performance Analysis Tool (uPATO) (Clemente, Silva, Martins, Kalamaras, & Mendes, 2016). The uPATO is a dedicated software to import position data and to compute specific tactical measures based on such bidimensional information. ...
The purpose of this study was to analyze the effects of two different field sizes (full and half of an official
size field) on the tactical behaviors measured by position data of players. Ten amateur soccer players
(age = 23.39 ± 3.91 years old) were tracked with GPS units during two situations of 11 vs. 11, one
in each field size. The position data was treated and the centroid and stretch index of the team were
calculated with the Ultimate Performance Analysis Tool. Significantly greater values of centroid in goalto-
goal axis (p = 0.001; ES = 3.794), centroid in lateral-to-lateral axis (p = 0.001; ES = 0.729) and
total stretch index (p = 0.001; ES = 1.185) were found in the full-size game. The full-size of the field
increased the distances between teammates and the distances to the centroid. Moreover, the position of
geometrical center of the team was beyond of the middle line in the full size.
... The PATO software was initially developed to create easily the adjacency matrices for two teams to be analyzed by the same observer in a very user-friendly way [16]. The adjacency matrices codify our graphs, which in case of football they are weighted digraphs. ...
Network analysis has been used to classify the
interactions between teammates in team sports. However, no
dedicated software or application was specifically developed to
import, compute and export data in the specific case of sports, as
far as we know. Based on that, we intend to propose a new
application to visualize and analyze networks in soccer. The
Ultimate Performance Analysis Tool (uPATO) allows observing,
codifying, importing, visualizing, computing measures and
exporting data from the observed games. The user may use a
single application to work in the visualization and analysis of the
match only considering the network that emerges from the game.
In this paper it will be possible to observe the steps to visualize
and import data and it will also be described the development of
some network measures to characterize the centralities and
general properties of unweighted and weighted graphs and
digraphs. Finally, data from a real game will be used to test the
network measures implemented and to show the values that can
be exported and interpreted.
Keywords— Graph Theory; Network Analysis; Football; Match
analysis
... The method of the multidimensional analysis is also used in other kinds of physical training and sport: for network analysis of competitive sports [33,34]; forecasting of player's success [35] and forecast of the maximum HR at children and teenagers [36]; forecasting of injuries at athletes in track and field events [37], American soccer [38], snowboard [39]. ...
Purpose : to develop structure of training process creation in a year cycle of qualified athletes’ preparation in aerobics (woman). Material : qualified athletes in aerobics participated in a research (n=46, age 20-22 years, height is 168±2, 4 cm, body weight is 62±4, 2 kg). Athletes were divided into experimental group (n=24) and control group (n=22). It was determined the level of special preparation: performance of standard combinations of basic aerobic steps. Results . The general principles of training programs preparation are developed. Programs include basic and variable components. The basic component of programs represents the standardized structure of means and methods selection of athletes’ preparation. The variable component of programs contained special means and methods differ on character and volume. One of the main parts of a variable component is the psychophysical training. It is based on performance of special sets of exercises in combination with mental figurative representations of motor nature. Conclusions . It is appropriate to carry out planning of training process in a year cycle of preparation considering specific features of factorial structure of athletes’ preparation.
... The importance of midfielders for the attacking process (they received and played the most passes) was shown for the Swiss national team (Clemente, Martins, Kalamaras, Oliveira et al., 2015) as well as for Portugal and Germany (Clemente, Silva, Martins, Kalamaras & Mendes, 2015). Additionally, it was possible to characterize that the German national soccer team's attacking process as being based on positional attack instead of counter-attack, using short passes and involving almost every player in the passing process. ...
„Das Spiel stellt ein vielfältiges, widersprüchliches und in unterschiedlichsten Erscheinungsformen auftretendes Phänomen dar, das die Menschheit seit jeher fasziniert und beschäftigt hat. […] Und nicht zuletzt gibt es noch die Sportspiele […], die die halbe Menschheit in ihren Bann ziehen können“ (Kolb, 2005, S 17).
Die spannende Sportartengruppe der Sportspiele bildet die inhaltliche Klammer der vorliegenden Arbeit. Seit jeher ist es eine Kunst, das Geschehen auf dem Spielfeld zu verstehen und zugrundeliegende Ideen und Gedanken von Spielern oder Trainern zu rekonstruieren. Durch die weltweite Verbreitung der Sportspiele gepaart mit technologischen Fortschritten ergaben sich in den vergangenen Jahren einige interessante Entwicklungen im Bereich der Spielanalyse.
Versucht man allerdings, sich dem Phänomen der Spielanalyse aus einer sportwissenschaftlichen – oder etwas konkreter einer trainingswissenschaftlichen – Perspektive zu nähern, ist zu konstatieren, dass die entsprechende Grundlagenliteratur nicht mit der technischen Progression der jüngeren Vergangenheit schrittgehalten hat. Im deutschen Sprachraum sind viele wichtige theoretische Arbeiten auf diesem Gebiet im Zeitraum zwischen 1980 und 2005 veröffentlicht worden, auf welche aktuelle Werke und Buchkapitel nach wie vor rekurrieren. Im englischen Sprachraum hingegen sind gerade im Zeitraum 2010 bis 2019 viele Ideen und Anregungen zu aktuellen Vorgehensweisen bei einer Spielanalyse zu finden. Durch den starken Praxisbezug fehlt allerdings oftmals eine grundlegende theoretische Fundierung entsprechender Ausführungen. Ein expliziter Vergleich beider Sprachräume ist jedoch von Vornherein zum Scheitern verurteilt, da es international keine wissenschaftliche Disziplin gibt, die der deutschen Trainingswissenschaft entspricht.
Durch eine Zusammenführung der Grundlagenliteratur aus beiden Sprachräumen zur Spielanalyse wird im Rahmen dieser Arbeit ein aktueller Überblick der trainingswissenschaftlichen Perspektive auf die Analyse von Sportspielen gegeben. So wird der gesamte Prozess einer fundierten Spielanalyse rekonstruiert und sukzessive reflektiert.
The last 20 years has seen an exponential increase in the use of novel technologies in sports science. This chapter aims to assess how these technologies are applied by researches and practitioners in their respective fields. This chapter also provides examples of how science and applied practice is linked and how technology has facilitated this process. Furthermore, coach learning is best understood in terms that recognize the interests and subjectivities of individuals, within a context shaped by the physical, social and educational provisions. Other applications such as positional data and network analysis are revealed in order to enhance the knowledge about current technologies in sport. Indeed, a better understanding of new technologies will ultimately assist in improving athletic performance.
The purpose of this chapter is to analyze how position data have been used in the aim of match analysis. A brief related work will present the main measures and results that come from soccer analysis based on georeferencing. Individual measures that characterize the time-motion profile, tactical behavior, predictability, stability and spatial exploration of players will be discussed. Collective measures that represent the Geometrical Center and team’s dispersion will be also presented during this chapter. The main evidences that resulted from these measures will be briefly discussed.
The purpose of this chapter is to present the individual measures that can be computed in the uPATO software. Each measure will be presented with a definition and case-studies to discuss the data and how results can be interpreted. Time-motion profile (including distances at different speeds), Shannon Entropy, Longitudinal and Lateral Displacements to the goal and variability, Kolmogorov Entropy and Spatial Exploration Index will be presented and discussed in this chapter. The case studies presented involve two five-player teams in an SSG considering only the space of half pitch (68 m goal-to-goal and 52 m side-to-side) and another eleven-player team in a match considering the space of the entire field (106.744 m goal-to-goal and 66.611 m side-to-side) even though only playing in half pitch.
Analysis of the physical, technical and physiological variations induced through the use of different soccer game formats have been widely discussed. However, the coaching justification for the specific use of certain game formats based on individual and collective spatial awareness is unclear. As a result, the purpose of this study was to analyze 11 vs. 11 game formats conducted across two pitch sizes (half-size: 5468 m vs. full-size: 10868 m) to identify effects of time-motion profiles, individual exploration behavior and collective organization. Ten amateur soccer players from the same team (23.393.91 years old) participated in this study. Data position of the players was used to calculate the spatial exploration index and the surface area. Distances covered in different speeds were used to observe the time-motion profile. The full-size pitch dimensions significantly contributed to greater distances covered via running (3.86 to 5.52 m.s-1) and sprinting (>5.52 m.s-1). Total distance and number of sprints were also significantly greater in the full-size pitch as compared to the half-size pitch. The surface area covered by the team (half-size pitch: 431.83 m2 vs. full-size pitch: 589.14 m2) was significantly larger in the full-size pitch condition. However, the reduced half-size pitch significantly contributed to a greater individual spatial exploration. Results of this study suggest that running and sprinting activities increase when large, full-size pitch dimensions are utilized. Smaller surface area half-size pitch contributes to a better exploration of the pitch measured by spatial exploration index while maintaining adequate surface area coverage by the team. In conclusion, the authors suggest that the small half-size pitch is more appropriate for low-intensity training sessions and field exploration for players in different positions. Alternatively, the large full-size pitch is more appropriate for greater physically demanding training sessions with players focused on positional tactical behavior.
Key-words: geolocation, computational metrics, football, positional mapping, behavior, positional demands, pitch size, soccer tactical
This chapter presents an overview of uPATO application, which was designed mainly for network analysis applied to team sports. However, this tool can be used for any network that can be represented by an adjacency matrix (e.g., a computer network, a telecommunication network, or even a social network, etc.). Thus, a first module was developed to allow codify the network, which, in the case of team sports, is given by a matrix with the sequences of interactions between teammates (i.e., a digraph). But this tool was designed to support graphs and digraphs, weighted and unweighted. Team sports are a good example of the necessity for calculating metrics on weighted networks. uPATO was developed with the main objective of analyzing team sports, where weights represent the frequency of the interactions between players, providing fundamental information on the analysis of the team factor. It calculates metrics in both weighted and unweighted networks and separates metrics into three major categories: individual metrics, subgroup metrics, and team metrics. Beyond metrics that uPATO allows calculating, a representation module that allows visualizing the network was also developed (i.e., the digraph or graph, weighted or not) and some charts for the data were calculated. Besides, the uPATO tool has an additional module for processing geolocation data. Currently, some teams use GPS devices to have the position of the players during the match (e.g., FieldWiz and TraXports formats are supported). Thus, uPATO has a set of metrics based on geolocation data of the players. This new functionality extends the uPATO capacities for team sports analysis but also for other activities where GPS data is available. But, it does not consider yet the possibility of the ball with a GPS device. However, this additional module is out of the scope of this book but the metrics implemented are described in a previous publication [3].
Team-based invasion sports such as football, basketball, and hockey are similar in the sense that the players are able to move freely around the playing area and that player and team performance cannot be fully analysed without considering the movements and interactions of all players as a group. State-of-the-art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across many disciplines, to extract insight from the trajectories. We survey recent research efforts that use spatio-temporal data from team sports as input and involve non-trivial computation. This article categorises the research efforts in a coherent framework and identifies a number of open research questions.
The objective of this review was to systematically describe the traditional and contemporary data capture and analytic methods employed in performance analysis research in team invasion sports, evaluate the practicality of these methods, and formulate practical recommendations on methods for analysing tactics and strategies in team invasion sports. A systematic search of the databases SPORTDiscus, Web of Science, Scopus, MEDLINE and PubMed was performed. Keywords addressed performance analysis methods and team invasion sports, with all other disciplines of sports science excluded. A total of 537 articles were included in the review and six main themes of research identified. Themes included game actions, dynamic game actions, movement patterns, collective team behaviours, social network analysis and game styles. Performance analysis research has predominantly focused on identifying key performance indicators related to success by analysing differences in game actions between successful and less successful teams. However, these measures are outcome-focused and only provide limited insight into winning team’s strategy. Team invasion sports are now viewed as dynamic, complex systems with opposing teams as interacting parts. Strategies and tactics should be analysed using a holistic process-orientated approach by recording dynamic actions, collective team behaviours and passing networks, and viewing them in game styles.
The aim of the present study was to analyze the temporal structure and activity patterns of elite paddle games. 9 Padel Pro Tour® 2012 tournaments were analyzed and a total of 28 matches and 59 sets (32 male games vs. 27 female games) were considered. The games were scored by using the software LongMatch V.0.20.1®, which compare the items based on temporal aspects and activity patterns between males and females. The results show significant differences overall for the temporal variables and the duration of rallies in males and females. Additionally, the variable ‘effort ratio’ was significantly higher in females than males. Activity variables such as lob per rally and shots per rally were also significantly higher in females than males. We can define the elite paddle as a sport characterized by short-term rallies interspersed with short recovery periods, which imply a high work to rest ratio and gender significantly effects the temporal variables and activity patterns. © 2016, Universidad Catolica San Antonio Murcia. All rights reserved.
Aims:
To introduce a platform called “InteractiveLab” (ILab) for collecting and analyzing ball passing networks during soccer games.
Methods:
The software was organized to collect data through a mobile interface and touch screen and simultaneously access that data from a remote database, allowing the automated acquisition, storage, and processing of data during games through an application from the web. The analysis is based on the concept of social networks, characterized by the interaction of players through passing exchanges.
Results:
This descriptive study presents the construction architecture and functioning of the developed software. It also presents the results of intra- and inter-rater reliability and a comparison with the manual collection method. Data were extracted and viewed according to the attacking unit classifications, with the following four outcomes: (a) interception, (b) lost ball, (c) incompletion, and (d) completion. This classification allows for the configuration of the data for a more precise analysis. Some limitations were highlighted, as well as future projections for the improvement of applications and analysis of the interactions network in the context of soccer.
Conclusion:
It is concluded that the InteractiveLab platform is a viable and beneficial tool that offers new possibilities for analysing performance in soccer. Moreover, given the lack of solutions that work similarly, this product also has market potential.
Team sports lead to permanent interactions between teammates. For that reason, the specific structure of interactions and team’s dynamics must be carefully analysed in order to improve the sports training and strategy used for the match. For that reason, the social network analysis has been used in the last few years to identify the properties of graphs and to measure the centrality levels of players and tactical positions in the collective organization and dynamics of team sports. Nevertheless, the match analysis based on social network analysis must follow some specific requirements. Therefore, this chapter aims to describe the required observational procedures for network analysis in team sports and to show some software to process the analysis and to extract the data to measure the network properties.
New, intelligent systems have been developed recently to improve the quality of match analysis. These systems analyze the tactical behavior of the teams. However, the existing methods leave room for improvement. Thus, the main goal of this study is to refine the team centroid metric by considering all of the players on the team and the ball position. Furthermore, this study analyzes the relation-ship between the centroids of the two opposing teams. One 11-on-11 soccer match was analyzed to test the new centroid algorithm. The results provided strong evidence of the positive relation between the centroids of the two teams over time in the -ݔaxis (ݎ ௦ = 0.781) and the -ݕaxis (ݎ ௦ = 0.707). This study confirmed the results of previous studies that analyzed the relationship between team centroids. Furthermore, it was possible to prove the effectiveness of the new tactical metric and its relevance for adding information during a match.
The aim of this case study was to apply a set of network metrics in order to characterize the teammates' cooperation in a football team. Metrics were applied in three levels of analysis: i) micro (individual analysis); ii) meso (players' contribution for the team); and iii) macro (global inter-action of the team). One-single case study match was observed and from such procedure were analysed 131 attacking plays. Results: from the macro analysis showed a moderate heterogeneity between teammates, thus suggesting the emergence of clusters within the team. players with highest connections with their teammates were the right defender, central defender from the left side, defensive mid right mid and the forward player. Finally, in the micro analysis was observed that right defender, cen-tral defender, right mid and the forward can be considered the cen-troid players during attacking plays, thus being the most prominent in the attacking building. In sum, the network metrics allowed to characterize the teammates' interaction during the attacking plays, providing an important and different information that can be useful for the future of match analysis.
The aim of this pilot study was propose a set of network methods to measure the specific properties of football teams. These metrics were organized on "meso" and "micro" analysis levels. Five official matches of the same team on the First Portuguese Football League were analyzed. An overall of 577 offensive plays were analyzed from the five matches. From the adjacency matrices developed per each offensive play it were computed the scaled connectivity, the clustering coefficient and the centroid significance and centroid conformity. Results showed that the highest values of scaled connectivity were found in lateral defenders and central and midfielder players and the lowest values were found in the striker and goalkeeper. The highest values of clustering coefficient were generally found in midfielders and forwards. In addition, the centroid results showed that lateral and central defenders tend to be the centroid players in the attacking process. In sum, this study showed that network metrics can be a powerful tool to help coaches to understanding the specific team's properties, thus supporting decision-making and improving sports training based on match analysis.
The purpose of this paper was to report the development and preliminary validation of tactical assessment system in Soccer and highlight its advantages. The validation process followed five perspectives of the concept of validity that consider the value of heuristic methods and the importance of the description of behavior performed in playing situations. Thus, the process of validation was focused on four points: i) acceptability and reasonableness of the test perceived by players; ii) analysis of content of assessment tool through a panel of experts; iii) potential of the assessment tool to discriminate the quality of the performance of players; iv) observation reliability. The results displayed values higher than 0.63 for correlation between the evaluations of coaches and the system. It shows the potential of this system to distinguish the performances of players based on the evaluations of coaches. The players who performed the field test agreed with its physical demands and spatial and normative configurations. All experts endorsed the categories and variables of this system. The reliabilities showed values higher than 0.79 for intra and inter-observers. Therefore, it is possible to conclude that the system is valid and reliable for the assessment of the tactical behavior of soccer players.
Abstract The main focus of this paper was to review the available literature on match analysis in adult male football. The most common research topics were identified, their methodologies described and the evolutionary tendencies of this research area systematised. A systematic review of Institute for Scientific Information (ISI) Web of Knowledge database was performed according to PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) guidelines. The following keywords were used: football and soccer, each one associated with the terms: match analysis, performance analysis, notational analysis, game analysis, tactical analysis and patterns of play. Of 2732 studies initially identified, only 53 were fully reviewed, and their outcome measures abstracted and analysed. Studies that fit all inclusion criteria were organised according to their research design as descriptive, comparative or predictive. Results showed that 10 studies focused predominantly on a description of technical, tactical and physical performance variables. From all comparative studies, the dependent variables more frequently used were "playing position" and "competitive level". Even though the literature stresses the importance of developing predictive models of sports performance, only few studies (n = 8) have focused on modelling football performance. Situational variables like game location, quality of opposing teams, match status and match half have been progressively included as object of research, since they seem to work as effective covariables of football performance. Taking into account the limitations of the reviewed studies, future research should provide comprehensive operational definitions for the studied variables, use standardised categories and description of activities and participants, and consider integrating the situational and interactional contexts into the analysis of football performance.
Este trabalho teve como objetivo caraterizar a transição defesa-ataque de uma equipa de futebol com recurso ao método de análise de redes. Foram analisados quatro jogos oficiais da 1ª Liga Portuguesa, registando-se 52 sequências de padrão de jogo. Utilizando o software SocNetV 0.81 foram calculadas as variáveis centralidade de intermediação (% BC) e centralidade de entrada e saída (IDC% e ODC%). Os resultados sugerem que a equipa em análise tem dois padrões preferenciais para este momento de jogo: i) o jogo indireto, tendo o Médio Defensivo como principal elemento para receber bolas na zona central defensiva do campo, apresentando a maior influência sobre a rede de passes, e ii) o jogo direto, tendo como referência o Ponta de Lança para bolas mais longas, sobre a primeira zona central ofensiva, ou na primeira zona ofensiva sobre o corredor lateral direito. Os resultados também sugerem que o número de jogadores que cercam a bola influencia a decisão do tipo de passe utilizado (curto ou longo). Usando este tipo de metodologia é possível identificar e quantificar os padrões de jogo de uma equipa, fornecendo dados objetivos que podem ajudar os treinadores a melhorar o desempenho das suas equipas.
In soccer it seems relevant to understand the relationship between the ball recovering in different pitch zones and the subsequent success or failure of attacking play. However, few studies have considered the links between the type of ball recovery in different pitch zones, the competition stages and the overall teams success.
The present study aims to analyze the attacks (n = 1619) carried out by the semi-finalist teams in the 2010 FIFA World Cup (Spain, Germany, The Netherlands and Uruguay) in order to explore ball recovery patterns as a performance indicator. SoccerEye observational instrument, SoccerEye recording software, SDIS-GSEQ and SPSS analytic software—One-way ANOVA, Two-way ANOVA and Regressions—were applied.
Direct ball recovery, in specific by interception and by defensive behavior followed by a pass, was the mostly frequent behavior, with the later inducing attacking play efficacy (p < 0.017). Differences were detected between the group and play-off stages as regards the types of direct ball recovery. Interceptions (p = 0.000) and tackles (p < 0.006) were the most recurrent defensive behaviors in the play-off matches, while the defensive behavior followed by a pass was the most common during the group stage (p < 0.006). The ball was most often regained in defensive and mid-defensive central zones, evidencing differences to all other pitch zones (p ≤ 0.001). Throw-ins were the only type of ball recovery that differentiated the semi-finalists, namely Germany and Spain (p < 0.009).
It was found that recovering directly the ball possession in mid-defensive central zones increase attacking efficacy. Consequently coaches should consider this tactical determinant in order to organize the training process. Specifically, it is fundamental to improve the collective defensive organization protecting central strip zones and simultaneously performing high-pitched pressure to constrain the ball carrier.
In soccer, the need for direct observation of tactical behaviour has led to continuous technological advances in
motion recording software. Here we present SoccerEye, a sports-specific software tool to observe and record the behaviour of soccer players in their natural setting and in real time. The software was written in Visual Basic Express 2010 and includes the following features: computerised coding, improved-quality recording, episodic sampling, the measurement of time, and diachronic analysis. Its configuration is well defined but allows for incorporation of ad hoc categories. Data can be exported in multiple generic formats, including the SDIS format for the analysis of interaction sequences with GSEQ software. However, by considering time and sequential decisions, SoccerEye itself tracks activity profiles and the dynamics of play. The greatest advantage of SoccerEye is the possibility to conduct diachronic analysis, which regards an event or multi event sequence in terms of change over time. This type of analysis takes into account the behaviour of a player and his or her team when facing the opponent, the space (pitch area) and time (starting time and duration) of each event, and other factors such as match status, match time, and competition stage. SoccerEye is a free access user-friendly application that can be used to observe a single player or an entire team while controlling over the environment in which the observation takes place. This tool will hopefully contribute to the better understanding of the dynamics of soccer play.
Accurately retrieving the position of football players over time may lay the foundations for a whole series of possible new performance metrics for coaches and assistants. Despite the recent developments of automatic tracking systems, the misclassification problem (i.e., misleading a given player by another) still exists and requires human operators as final evaluators. This paper proposes an adaptive fractional calculus (FC) approach to improve the accuracy of tracking methods by estimating the position of players based on their trajectory so far. One half-time of an official football match was used to evaluate the accuracy of the pro-posed approach under different sampling periods of 250, 500 and 1000 ms. Moreover, the performance of the FC approach was compared with position-based and velocity-based methods. The experimental evaluation shows that the FC method presents a high classification accuracy for small sampling periods. Such results suggest that fractional dynamics may fit the trajectory of football players, thus being useful to increase the autonomy of tracking systems.
Capacity to produce data for performance analysis in sports has been enhanced in the last decade with substantial technological advances. However, current performance analysis methods have been criticised for the lack of a viable theoretical framework to assist on the development of fundamental principles that regulate performance achievement. Our aim in this paper is to discuss ecological dynamics as an explanatory framework for improving analysis and understanding of competitive performance behaviours. We argue that integration of ideas from ecological dynamics into previous approaches to performance analysis advances current understanding of how sport performance emerges from continuous interactions between individual players and teams. Exemplar data from previous studies in association football are presented to illustrate this novel perspective on performance analysis. Limitations of current ecological dynamics research and challenges for future research are discussed in order to improve the meaningfulness of information presented to coaches and managers.
In soccer, the ability to retain possession of the ball for prolonged periods of time has been suggested to be linked
to success. The accuracy of this assertion was investigated by examining 380 matches involving Spanish League
First Division teams during the 2008‐2009 season. Possession of the ball, according to the status of the match (winning,
drawing and losing), was recorded during the different matches using a multiple‐camera match analysis system
(Gecasport®). The results suggest that the best classified teams maintained a higher percentage of ball possession
and that their pattern of play was more stable. The coefficient of variation, with respect to ball possession per
match, was smaller for the best placed teams. Indeed, first placed F.C. Barcelona had the smallest coefficient of variation
for possession time (8.4%), while bottom placed Recreativo showed the highest values with 17.1%. Linear regression
analysis showed that possession strategies were influenced by situation variables. Team possession was
greater when losing than when winning (p<0.01) or drawing (p<0.01), home teams enjoyed greater possession than
visiting teams (p<0.01), and playing against strong opposition was associated with a reduction in time spent in possession
(p<0.01). The findings indicate that strategies in soccer are influenced by situational variables and that
teams alter their playing style accordingly during the match.
Key words: match analysis; possession strategies; soccer; team performance; tactical component
In collective sports, the match analysis is fundamental in order to improve the quality of coaches' intervention. Nevertheless, the generality of the systems are based on notational analysis which does not allow a deep understanding about the collective behaviour of the team. Therefore, the main goal of this study is to update and design new tactical metrics that allows an improved online knowledge about the teams' behaviour. Tactical metrics such as the teams' centroid, teams' stretch index and teams' effective play area will be presented throughout this study, validated by means of a single match experimental case study. Results suggest the potential of the herein proposed tactical metrics, providing relevant and online information to the coaches over time, thus allowing new opportunities to improve the quality of their intervention.
This study investigated team coordination in basketball using the course-of-action theoretical framework. The focus was on how the players were connected with their teammates’ activities. The activity of five basketball players (17.60 ± 0.89 years) was studied during an official match. The data were collected and processed according to a procedure defined for course-of-action analysis. The results were used to characterize the coordination modes among players and the team coordination network that was built. The processes that underlie team coordination are discussed (i.e., mutuality and awareness), and some directions for practical applications are addressed
The present study examines the associations between tactical performance indexes with quality of tactical behaviours and birth-date quarters of youth Soccer players. The sample comprised a total of 534 youth players classified into four seasons of 3 months (January-March; April-June; July-September; October-December). A system of tactical assessment in Soccer (FUT-SAT) was used to collect data. Descriptive statistics and multinomial logistic regression were applied. The tactical performance indexes were divided into tercis (low, moderate and high) in order to evaluate the influences of relative age effects and quality of tactical behaviours. The quality in the “penetration” and “offensive coverage” principles were positively related to moderate performance indexes. Players with the highest quality in the “depth mobility” and “unity defensive” principles were more likely to present higher performance indexes. Regarding the defensive phase, those with better qualities in the “delay”, “concentration” and “defensive unity” principles were more likely to have moderate performance index. Additionally, better quality in the “defensive coverage” and “balance” principles corresponded to a higher likelihood of having a superior performance index. Relative age effects were observed only in high defensive performance index. The present results revealed a positive correlation between tactical performance indexes and quality of tactical behaviours.
Significant criticisms have emerged on the way that collective behaviours in team sports have been traditionally evaluated. A major recommendation has been for future research and practice to focus on the interpersonal relationships developed between team players during performance. Most research has typically investigated team game performance in subunits (attack or defence), rather than considering the interactions of performers within the whole team. In this paper, we offer the view that team performance analysis could benefit from the adoption of biological models used to explain how repeated interactions between grouping individuals scale to emergent social collective behaviours. We highlight the advantages of conceptualizing sports teams as functional integrated 'super-organisms' and discuss innovative measurement tools, which might be used to capture the superorganismic properties of sports teams. These tools are suitable for revealing the idiosyncratic collective behaviours underlying the cooperative and competitive tendencies of different sports teams, particularly their coordination of labour and the most frequent channels of communication and patterns of interaction between team players. The principles and tools presented here can serve as the basis for novel approaches and applications of performance analysis devoted to understanding sports teams as cohesive, functioning, high-order organisms exhibiting their own peculiar behavioural patterns.
We analyze the pass network among the players of the Spanish team (the world
champion in the FIFA World Cup 2010), with the objective of explaining the
results obtained from the behavior at the complex network level. The team is
considered a network with players as nodes and passes as (directed) edges, and
a temporal analysis of the resulting passes network is done, looking at the
number of passes, length of the chain of passes, and the centrality of players
in the turf. Results of the last three matches indicate that the clustering
coefficient of the pass network increases with time, and stays high, indicating
possession by Spanish players, which eventually leads to victory, even as the
density of the pass network decreases with time.
Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.
Teamwork is a fundamental aspect of many human activities, from business to art and from sports to science. Recent research suggest that team work is of crucial importance to cutting-edge scientific research, but little is known about how teamwork leads to greater creativity. Indeed, for many team activities, it is not even clear how to assign credit to individual team members. Remarkably, at least in the context of sports, there is usually a broad consensus on who are the top performers and on what qualifies as an outstanding performance.
In order to determine how individual features can be quantified, and as a test bed for other team-based human activities, we analyze the performance of players in the European Cup 2008 soccer tournament. We develop a network approach that provides a powerful quantification of the contributions of individual players and of overall team performance.
We hypothesize that generalizations of our approach could be useful in other contexts where quantification of the contributions of individual team members is important.
The aim of this study was to examine the contribution of the systemic approach to the analysis of play in team sports. We first focus on the theory of dynamical systems and consider the interactions between the main variables of the different components of systems and subsystems in soccer. In team sports, these variables represent fluctuating conditions, which momentarily constrain the organization of action for the players. Thus changes in the momentary configuration of the game have to be examined in the light of previous configurations, the outline of the defensive strategy and the tactical choices involved. To study this problem, we analyse the antecedents of goals in soccer. A procedure is proposed which analyses transitions between configurations of play, thus allowing time to be taken into consideration when studying the evolution of a match. To illustrate the use and benefit of the analytic procedure, two goals are described in terms of dynamic configurations of play and opportunity of choices made by attackers.
Novel estimation, detection and identification techniques have been recently applied on sports, providing the cartesian positional information of players over time. This information has been seen as vital within sports science’s literature, so as to propose new computational tactical metrics that may allow to inspect the spatio-temporal relationship between teammates. Such technological approaches can improve the understanding of the collective match, providing to coaches and analysts a real-time augmented perception of the game. In spite of this, this study aims to identify, and computational describe, the most promising tactical metrics developed over the last few years and characterize their practical applications. Moreover, a technological approach that integrates these metrics will be discussed, focusing on the data retrieval, processing and visualization. Therefore, concepts such as Augmented Reality, Cloud Computing and Human-computer Interaction are considered to give the first steps toward a football game analysis system.
Statistics for Sport and Exercise Studies guides the student through the full research process, from selecting the most appropriate statistical procedure, to analysing data, to the presentation of results, illustrating every key step in the process with clear examples, case-studies and data taken from real sport and exercise settings. Every chapter includes a range of features designed to help the student grasp the underlying concepts and relate each statistical procedure to their own research project, including definitions of key terms, practical exercises, worked examples and clear summaries. The book also offers an in-depth and practical guide to using SPSS in sport and exercise research, the most commonly used data analysis software in sport and exercise departments. In addition, a companion website includes more than 100 downloadable data sets and work sheets for use in or out of the classroom, full solutions to exercises contained in the book, plus over 1,300 PowerPoint slides for use by tutors and lecturers. Statistics for Sport and Exercise Studies is a complete, user-friendly introduction to the use of statistical tests, techniques and procedures in sport, exercise and related subjects. Visit the companion website at: www.routledge.com/cw/odonoghue.
The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game, especially due to the increased physical demands of match-play. The monitoring of players’ work rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university-based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role that motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, and identify practical implications of the established body of knowledge, while highlighting areas that require further exploration.
A defining feature of a work group is how its individual members interact. Building on a dataset of 283,259 passes between professional soccer players, this study applies mixed-effects modeling to 76 repeated observations of the interaction networks and performance of 23 soccer teams. Controlling for unobserved characteristics, such as the quality of the teams, the study confirms previous findings with panel data: networks characterized by high intensity (controlling for interaction opportunities) and low centralization are indeed associated with better team performance.
New tactical metrics have been introduced in the last years to allow an understanding on the collective behaviour behind the results provided by notational analysis. Thus, the aim of this article was to propose a new application developed on MATLAB to understand the territorial domain of teams during the game based on players’ position and using such data analyse sectors with more variability considering the advantage or disadvantage of team players during a single match as proof of concept. As methodological approach, a total of 1508 instants of a 7-a-side soccer game were analysed. The herein proposed MATLAB application allows obtaining the differences between players from Team A and Team B. Moreover, the approximate entropy is used to assess the variability of each of the 12 field sectors. The results show that the sector with higher variability is the central offensive midfield (1.114), closely followed by the central defensive midfield (1.033). A more dynamic rapport of strength within the central sectors may be the explanation for such a higher variability. In conclusion, this study presented a new and effective application to analyse the territorial domain that can be matched with automatic tracking systems to improve their online potential.
We compared the accuracy of 2 GPS systems with different sampling rates for the determination of distances covered at high-speed and metabolic power derived from a combination of running speed and acceleration. 8 participants performed 56 bouts of shuttle intermittent running wearing 2 portable GPS devices (SPI-Pro, GPS-5 Hz and MinimaxX, GPS-10 Hz). The GPS systems were compared with a radar system as a criterion measure. The variables investigated were: total distance (TD), high-speed distance (HSR>4.17 m·s(-1)), very high-speed distance (VHSR>5.56 m·s(-1)), mean power (Pmean), high metabolic power (HMP>20 W·kg(-1)) and very high metabolic power (VHMP>25 W·kg(-1)). GPS-5 Hz had low error for TD (2.8%) and Pmean (4.5%), while the errors for the other variables ranged from moderate to high (7.5-23.2%). GPS-10 Hz demonstrated a low error for TD (1.9%), HSR (4.7%), Pmean (2.4%) and HMP (4.5%), whereas the errors for VHSR (10.5%) and VHMP (6.2%) were moderate. In general, GPS accuracy increased with a higher sampling rate, but decreased with increasing speed of movement. Both systems could be used for calculating TD and Pmean, but they cannot be used interchangeably. Only GPS-10 Hz demonstrated a sufficient level of accuracy for quantifying distance covered at higher speeds or time spent at very high power.
Novel estimation, detection and identification techniques have been recently applied on sports, providing the cartesian positional information of players over time. This information has been seen as vital within sports science’s literature, so as to propose new computational tactical metrics that may allow to inspect the spatio-temporal relationship between teammates. Such technological approaches can improve the understanding of the collective match, providing to coaches and analysts a real-time augmented perception of the game. In spite of this, this study aims to identify, and computational describe, the most promising tactical metrics developed over the last few years and characterize their practical applications. Moreover, a technological approach that integrates these metrics will be dis-cussed, focusing on the data retrieval, processing and visualization. Therefore, concepts such as Augmented Reality, Cloud Computing and Human-computer Interaction are considered to give the first steps toward a football game analysis system.
The aim of this study was to propose a new tactical metric that characterises teammates’ organisation within a tactical sector. This metric was developed based on the Cartesian information of football players’ location at each second of three official matches. From the tracking procedures, 9218 moments were collected which were then organised into defensive (without possession of the ball) and attacking (with possession of the ball) instants. Significant differences were found between the two statuses of the possession of the ball for the defensive line (F
(1, 9216) = 44.520; p-value = 0.001; η
2 = 0.005; Power = 1.000) and forward line (F
(1, 9216) = 26.175; p-value = 0.001; η
2 = 0.000; Power = 0.108). From the specific results of this case study, it was possible to propose a new concept to help coaches observe a match with some tactical parameters that can allow a quicker identification of team properties.
In football, the tactical behaviour of a team is related to the state of ball possession, i.e., the defensive and offensive phases. The aim of this study was to measure the tactical responses of two opposing teams in the moments with and without ball possession, thus trying to identify differences in results arising from tactical metrics such as weighted centroid position, weighted stretch index, surface area and effective area of play. The herein presented results show statistical differences in both teams, either with or without the ball possession, for the x-axis centroid (p-value ≤ 0.001), y-axis centroid (p-value ≤ 0.001), stretch index (p-value ≤ 0.001), surface area (p-value ≤ 0.001) and effective area of play (p-value ≤ 0.001). Such results confirm that teams react depending upon ball’s possession, respecting the tactical principles of width and length, as well as the unit in the offensive phase with ball possession, and also the concentration and defensive unit in the moments without ball possession.
Key-words: Match Analysis; Tactics; Performance; Team Sports; Football.
Significant criticisms have emerged on the way that collective behaviours in team sports have been traditionally evaluated. A major recommendation has been for future research and practice to focus on the interpersonal relationships developed between team players during performance. Most research has typically investigated team game performance in subunits (attack or defence), rather than considering the interactions of performers within the whole team. In this paper, we offer the view that team performance analysis could benefit from the adoption of biological models used to explain how repeated interactions between grouping individuals scale to emergent social collective behaviours. We highlight the advantages of conceptualizing sports teams as functional integrated ‘super-organisms’ and discuss innovative measurement tools, which might be used to capture the superorganismic properties of sports teams. These tools are suitable for revealing the idiosyncratic collective behaviours underlying the cooperative and competitive tendencies of different sports teams, particularly their coordination of labour and the most frequent channels of communication and patterns of interaction between team players. The principles and tools presented here can serve as the basis for novel approaches and applications of performance analysis devoted to understanding sports teams as cohesive, functioning, high-order organisms exhibiting their own peculiar behavioural patterns.
The authors provide a cautionary note on reporting accurate eta-squared values from multifactor analysis of variance (ANOVA) designs. They reinforce the distinction between classical and partial eta-squared as measures of strength of association. They provide examples from articles published in premier psychology journals in which the authors erroneously reported partial eta-squared values as representing classical etasquared values. Finally, they discuss broader impacts of inaccurately reported etasquared values for theory development, meta-analytic reviews, and intervention programs.
Praise for previous editions:. . "This book really is a life saver ... If the mere thought of statistics gives you a headache, then this is the book for you." - Statistics student, UK. . "I just wanted to say how much I value Julie Pallant's SPSS Survival Manual. Its quite the best text in SPSS Ive encountered and I recommend it to anyone whos listening!" - Professor Carolyn Hicks, Birmingham University, UK. . "... one of the most useful functional pieces of instruction I have seen. So, gold star and thanks." - Instructional designer, USA . . "There are several SPSS manuals published and this one really does 'do what it says on the tin' ... Whether you are a beginner doing your BSc or struggling with your PhD research (or beyond!), I wholeheartedly recommend this book." - British Journal of Occupational Therapy, UK. . Praise for the new edition: . . "An excellent introduction to using SPSS for data analysis ... It provides a self-contained resource itself, with more than simply (detailed and clear) step-by-step descriptions of statistical procedures in SPSS. There is also a wealth of tips and advice, and for each statistical technique a brief, but consistently reliable, explanation is provided." - Associate Professor George Dunbar, Department of Psychology, University of Warwick, UK. . In this fully revised edition of her bestselling text, Julie Pallant guides you through the entire research process, helping you choose the right data analysis technique for your project. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic and advanced statistical techniques. She outlines each technique clearly, with step-by-step procedures for performing the analysis, a detailed guide to interpreting SPSS output and an example of how to present the results in a report.. . For both beginners and experienced SPSS users in psychology, sociology, health sciences, medicine, education, business and related disciplines, the SPSS Survival Manual is an essential guide. Illustrated with screen grabs, examples of output and tips, it is supported by a website with sample data and guidelines on report writing. . . In this third edition all chapters have been updated to accommodate changes to SPSS procedures, screens and output in version 15. A new flowchart is included for SPSS procedures, and factor analysis procedures have been streamlined. It also includes more examples and material on syntax. Additional data files are available on the book's supporting website..
The kappa statistic has been used to evaluate the reliability of performance indicators measured using computerised notational analysis systems. However, all disagreements between observers are treated as total disagreements event if neighbouring values of ordinal scale performance indicators are used. Where nominal values are used to represent areas of the playing surface, kappa does not give any credit where the observers record neighbouring cells. Therefore, the purpose of the current paper was to illustrate how the weighted kappa statistic can be used with performance analysis data to give some credit where there are partial agreements between the observers.
We showcase in this paper the use of some tools from network theory to
describe the strategy of football teams. Using passing data made available by
FIFA during the 2010 World Cup, we construct for each team a weighted and
directed network in which nodes correspond to players and arrows to passes. The
resulting network or graph provides a direct visual inspection of a team's
strategy, from which we can identify play pattern, determine hot-spots on the
play and localize potential weaknesses. Using different centrality measures, we
can also determine the relative importance of each player in the game, the
`popularity' of a player, and the effect of removing players from the game.
Part I. Introduction: Networks, Relations, and Structure: 1. Relations and networks in the social and behavioral sciences 2. Social network data: collection and application Part II. Mathematical Representations of Social Networks: 3. Notation 4. Graphs and matrixes Part III. Structural and Locational Properties: 5. Centrality, prestige, and related actor and group measures 6. Structural balance, clusterability, and transitivity 7. Cohesive subgroups 8. Affiliations, co-memberships, and overlapping subgroups Part IV. Roles and Positions: 9. Structural equivalence 10. Blockmodels 11. Relational algebras 12. Network positions and roles Part V. Dyadic and Triadic Methods: 13. Dyads 14. Triads Part VI. Statistical Dyadic Interaction Models: 15. Statistical analysis of single relational networks 16. Stochastic blockmodels and goodness-of-fit indices Part VII. Epilogue: 17. Future directions.
This paper describes and evaluates the novel utility of network methods for understanding human interpersonal interactions within social neurobiological systems such as sports teams. We show how collective system networks are supported by the sum of interpersonal interactions that emerge from the activity of system agents (such as players in a sports team). To test this idea we trialled the methodology in analyses of intra-team collective behaviours in the team sport of water polo. We observed that the number of interactions between team members resulted in varied intra-team coordination patterns of play, differentiating between successful and unsuccessful performance outcomes. Future research on small-world networks methodologies needs to formalize measures of node connections in analyses of collective behaviours in sports teams, to verify whether a high frequency of interactions is needed between players in order to achieve competitive performance outcomes.
Limited data is available on accuracy and validity of video-based, GPS and electronic tracking systems, particularly with reference to curved courses and short high intensity running activities. The main goal of this study was to assess soccer-specific accuracy and validity of the radio-frequency based local position measurement (LPM) system (1000Hz) for measuring distance and speed during walking and sprinting. Three males walked and sprinted 4 soccer-specific courses 10 times each. Distance and speed recorded by LPM were compared to actual distance and speed measured by measuring tape and timing gates. In addition, accuracy was assessed. The static accuracy (SD of the mean) is 1cm for devices put on the pitch and 2-3 cm when worn by participants. LPM underestimates actual distance (mean difference at most -1.6%). Coefficient of variation becomes larger at higher speed and increased turning angle. With regard to speed, validity correlations are high (range: 0.71-0.97). The LPM speed is significantly and systematically lower, although absolute and relative differences are small, between -0.1 km h⁻¹ (-1.3%) and -0.6 km h⁻¹ (-3.9%). The typical error of the estimate increases with increased speed, but does not increase with increased turning angle. Because the reported differences are small, we conclude that the LPM-system produces highly accurate position and speed data in static and dynamic conditions and is a valid tool for player tracking in soccer and ball team sports in general.
DESCRIPTION This book addresses and appropriately explains the soccer match analysis, looks at the very latest in match analysis research, and at the innovative technologies used by professional clubs. This handbook is also bridging the gap between research, theory and practice. The methods in it can be used by coaches, sport scientists and fitness coaches to improve: styles of play, technical ability and physical fitness; objective feedback to players; the development of specific training routines; use of available notation software, video analysis and manual systems; and understanding of current academic research in soccer notational analysis. PURPOSE The aim is to provide a prepared manual on soccer match analysis in general for coaches and sport scientists. Thus, the professionals in this field would gather objective data on the players and the team, which in turn could be used by coaches and players to learn more about performance as a whole and gain a competitive advantage as a result. The book efficiently meets these objectives. AUDIENCE The book is targeted the athlete, the coach, the sports scientist professional or any sport conscious person who wishes to analyze relevant soccer performance. The editors and the contributors are authorities in their respective fields and this handbook depend on their extensive experience and knowledge accumulated over the years. FEATURES The book demonstrates how a notation system can be established to produce data to analyze and improve performance in soccer. It is composed of 9 chapters which present the information in an order that is considered logical and progressive as in most texts. Chapter headings are: 1. Introduction to Soccer Match Analysis, 2. Developing a Manual Notation System, 3. Video and Computerized Match Analysis Technology, 4. General Advice on Analyzing Match Performance, 5. Analysis and Presentation of the Results, 6. Motion Analysis and Consequences for Training, 7. What Match Analysis Tells Us about Successful Strategy and Tactics in Soccer, 8. From Technical and Tactical Performance Analysis to Training Drills, 9. The Future of Soccer Match Analysis. ASSESSMENT The authors have assembled an essential reading for all who are interested in understanding and doing better coaching and improving the performance in soccer. To this purpose, there is a strong practical approach in the book by giving plenty of examples along with a satisfactory scientific analysis of the subject area. It is concise and well organized in its presentation, creating an effective textbook. I believe, therefore, the book will serve as a first-rate teaching tool and reference for coaches, athletes and professionals in the human performance sciences.
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