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Young C, Bruce L, Dwyer D, Di Domenico I, Fox A. Understanding passing network characteristics and their link to match outcome in elite Netball. J Sports Sci 2023; 41:1538-1546. [PMID: 37953626 DOI: 10.1080/02640414.2023.2281721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 11/02/2023] [Indexed: 11/14/2023]
Abstract
Player interactions in Netball are critical during attacking phases of play to ensure possession is maintained and scoring opportunities are created. This study aims to analyse the characteristics of the passing networks of elite Netball teams and their association with performance outcomes (i.e., win/loss and final margin). Five network metrics used to represent the characteristics of teamwork were calculated for all team performances (n = 112) from one season of professional Netball in Australia. A two-way ANOVA and multiple linear modelling were used to compare characteristics between teams and match outcomes and to predict score margin, respectively. Pass density (F = 65.09, df = 102, p < .001) and pass centrality (F = 7.61, df = 102, p < .01) differed (were higher) in wins/losses. They were also statistically significant contributors (p ≤ .005) to the linear model that predicted a score margin (R2 = .731). Key player centrality and mutual connectedness were different between teams but did not differ by match outcome. The results suggest that, ideally, Netball teams should maximise the number of connections between player pairings, while also relying on a subset of players to be heavily involved in passing sequences. Team cohesion (via passing) therefore appears to be an important measure of team success in elite Netball.
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Affiliation(s)
- Christopher Young
- Centre for Sport Research, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia
| | - Lyndell Bruce
- Centre for Sport Research, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia
| | - Dan Dwyer
- Centre for Sport Research, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia
| | - Isaiah Di Domenico
- Centre for Sport Research, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia
| | - Aaron Fox
- Centre for Sport Research, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia
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2
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Cao S. Study State Dynamics of Team Passing Networks in Soccer Games. J Sports Sci 2023:1-15. [PMID: 37366331 DOI: 10.1080/02640414.2023.2229154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 06/15/2023] [Indexed: 06/28/2023]
Abstract
Complex networks have been widely used in studying collective behaviours in soccer sports, such as examining tactical strategies, recognizing team characteristics, and discovering topological determinants for high team performance. The passing network of a team evolves and displays different temporal patterns, that are strongly linked to team status, tactical strategies, attacking/defending transitions, etc. Nevertheless, existing research has not illuminated the state dynamics of team passing networks, whereas similar methods have been extensively used in examining the dynamical brain networks constructed from human brain neuroimage data. This study aims to investigate the state dynamics of team passing networks in soccer sports. The introduced method incorporates multiple techniques, including sliding time window, network modeling, graph distance measure, clustering, and cluster validation. The final match of the FIFA World Cup 2018 was taken as an example, and the state dynamics of teams Croatia and France were analyzed respectively. Additionally, the effects of the time windows and graph distance measures on the results were briefly discussed. This study presents a novel outlook on examining the dynamics of team passing networks, as it facilitates the recognition of important team states or state transitions in soccer and other team ball-passing sports for further analysis.
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Affiliation(s)
- Shun Cao
- Department of Information Science Technology, University of Houston, Houston, TX, USA
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3
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Dong J, Huo Q, Ferrari S. A Holistic Approach for Role Inference and Action Anticipation in Human Teams. ACM T INTEL SYST TEC 2022. [DOI: 10.1145/3531230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The ability to anticipate human actions is critical to many cyber-physical systems, such as robots and autonomous vehicles. Computer vision and sensing algorithms to date have focused on extracting and predicting visual features that are explicit in the scene, such as color, appearance, actions, positions, and velocities, using video and physical measurements, such as object depth and motion. Human actions, however, are intrinsically influenced and motivated by many implicit factors such as context, human roles and interactions, past experience, and inner goals or intentions. For example, in a sport team, the team strategy, player role, and dynamic circumstances driven by the behavior of the opponents, all influence the actions of each player. This paper proposes a holistic framework for incorporating visual features, as well as hidden information, such as social roles, and domain knowledge. The approach, relying on a novel dynamic Markov random field (DMRF) model, infers the instantaneous team strategy and, subsequently, the players’ roles that are temporally evolving throughout the game. The results from the DMRF inference stage are then integrated with instantaneous visual features, such as individual actions and position, in order to perform holistic action anticipation using a multi-layer perceptron (MLP). The approach is demonstrated on the team sport of volleyball, by first training the DMRF and MLP offline with past videos, and, then, by applying them to new volleyball videos online. These results show that the method is able to infer the players’ roles with an average accuracy of 86.99%, and anticipate future actions over a sequence of up to 46 frames with an average accuracy of 80.50%. Additionally, the method predicts the onset and duration of each action achieving a mean relative error of 14.57% and 15.67%, respectively.
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Immler S, Rappelsberger P, Baca A, Exel J. Guardiola, Klopp, and Pochettino: The Purveyors of What? The Use of Passing Network Analysis to Identify and Compare Coaching Styles in Professional Football. Front Sports Act Living 2021; 3:725554. [PMID: 34746774 PMCID: PMC8569793 DOI: 10.3389/fspor.2021.725554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/20/2021] [Indexed: 11/13/2022] Open
Abstract
We applied social networks analysis to objectively discriminate and describe interpersonal interaction dynamics of players across different top-coaching styles. The aim was to compare metrics in the passing networks of Jürgen Klopp, Pep Guardiola, and Mauricio Pochettino across the UEFA Champions League seasons from 2017 to 2020. Data on completed passes from 92 games were gathered and average passing networks metrics were computed. We were not only able to find the foundations on which these elite coaches build the passing dynamics in their respective teams, but also to determine important differences that represent their particular coaching signatures. The local cluster coefficient was the only metric not significantly different between coaches. Still, we found higher average shortest-path length for Guardiola's network (mean ± std = 3.00 ± 0.45 a.u.) compared to Klopp's (2.80 ± 0.52 a.u., p = 0.04) and Pochettino's (2.70 ± 0.39 a.u., p = 0.01). Density was higher for Guardiola's (64.16 ± 20.27 a.u.) than for Pochettino's team (51.42 ± 17.28 a.u., p = 0.008). The largest eigenvalue for Guardiola's team (65.95 ± 16.79 a.u.) was higher than for Klopp's (47.06 ± 17.25 a.u., p < 0.001) and Pochettino's (42,62 ± 12.01 a.u., p < 0.001). Centrality dispersion was also higher for Guardiola (0.14 ± 0.02 a.u.) when compared to Klopp (0.12 ± 0.03 a.u., p = 0.008). The local cluster coefficient seems to build the foundation for passing work, however, cohesion characteristics among players in the three teams of the top coaches seems to characterize their own footprint regarding passing dynamics. Guardiola stands out by the high number of passes and the enhanced connection of the most important players in the network. Klopp and Pochettino showed important similarities, which are associated to preferences toward more flexibility of interpersonal linkages synergies.
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Affiliation(s)
- Sebastian Immler
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
| | - Philipp Rappelsberger
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
| | - Arnold Baca
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
| | - Juliana Exel
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
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Abstract
The understanding that sports injury is the result of the interaction among many factors and that specific profiles could increase the risk of the occurrence of a given injury was a significant step in establishing programs for injury prevention. However, injury forecasting is far from being attained. To be able to estimate future states of a complex system (forecasting), it is necessary to understand its nature and comply with the methods usually used to analyze such a system. In this sense, sports injury forecasting must implement the concepts and tools used to study the behavior of self-organizing systems, since it is by self-organizing that systems (i.e., athletes) evolve and adapt (or not) to a constantly changing environment. Instead of concentrating on the identification of factors related to the injury occurrence (i.e., risk factors), a complex systems approach looks for the high-order variables (order parameters) that describe the macroscopic dynamic behavior of the athlete. The time evolution of this order parameter informs on the state of the athlete and may warn about upcoming events, such as injury. In this article, we describe the fundamental concepts related to complexity based on physical principles of self-organization and the consequence of accepting sports injury as a complex phenomenon. In the end, we will present the four steps necessary to formulate a synergetics approach based on self-organization and phase transition to sports injuries. Future studies based on this experimental paradigm may help sports professionals to forecast sports injuries occurrence.
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Cordón-Carmona A, García-Aliaga A, Marquina M, Calvo JL, Mon-López D, Refoyo Roman I. What Is the Relevance in the Passing Action between the Passer and the Receiver in Soccer? Study of Elite Soccer in La Liga. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17249396. [PMID: 33333901 PMCID: PMC7765303 DOI: 10.3390/ijerph17249396] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 12/09/2020] [Accepted: 12/11/2020] [Indexed: 01/04/2023]
Abstract
Soccer is a high-complexity sport in which 22 players interact simultaneously in a common space. The ball-holder interacts with their teammates by passing actions, establishing a unique communication among them in the development of the game in its offensive phase. The main aim of the present study was to analyze the pass action according to the trajectory of the ball receiver and the space for receiving the ball in terms of success at the end of play. Twenty La Liga 2018/2019 matches of two elite teams were analyzed. A system of notational analysis was used to create 11 categories based on context, timing and pass analysis. The data were analyzed using chi-squared analysis. The results showed that the main performance indicators were the efficiency of the pass, the zone of the field, the trajectory of the receiver and the reception space of the ball, which presented a moderate association with the end of play (p < 0.001). We concluded that receiving the ball on approach and in separation increased the probability of success by 5% and 7%, respectively, and a diagonal run increased the probability by 7%. Moreover, the combined analysis of these variables would improve the team performance.
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Affiliation(s)
| | | | | | | | - Daniel Mon-López
- Correspondence: (J.L.C.); (D.M.L.); Tel.: +34-910678023 (J.L.C. & D.M.L.)
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Sasaki K, Sato H, Nakamura A, Yamamoto T, Watanabe I, Katsuta T, Kono I. Clarifying the structure of serious head and spine injury in youth Rugby Union players. PLoS One 2020; 15:e0235035. [PMID: 32667924 PMCID: PMC7363091 DOI: 10.1371/journal.pone.0235035] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 06/08/2020] [Indexed: 12/22/2022] Open
Abstract
This study aimed to clarify the cause of rugby head and spinal cord injuries through a network centrality analysis of 14-year (2004-2018) longitudinal data in Japan. The study hypothesis is that understanding the causal relationship among the occurrence of serious injuries, the quality of player experience and play situation as a network structure could be possible to obtain practical knowledge on injury prevention. In this study, bipartite graphs are used to make it easier to understand the situation of players and injuries. This would also help to elucidate more characteristic subgroup. A network bipartite graph and subgroup (cluster) analyses were performed to clarify the injured players' experience and the cause of injury. We used the algorithm of R program, IGRAPH, clustering edge betweenness. For subgroup extraction, the modularity Q value was used to determine which step to cut. The Japanese rugby population was 93,873 (2014-2018 average), and 27% were high school students. The data showed that careful attention would be particularly needed for groups of inexperienced Japanese high school players. Our study suggests that we should consider introducing rules that prohibit "head-on collisions" in youth rugby.
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Affiliation(s)
- Koh Sasaki
- Research Center of Health, Physical Fitness, and Sports, Nagoya University, Nagoya, Japan
- * E-mail:
| | - Haruhiko Sato
- Department of Neurosurgery, Seirei Mikatahara General Hospital, Shizuoka, Japan
| | - Akihiko Nakamura
- Department of Pediatric Surgery, Nakamura Hospital, Tokyo, Japan
| | - Takumi Yamamoto
- Faculty of Education, National Defense Academy, Yokosuka, Japan
| | - Ichiro Watanabe
- Faculty of Liberal Arts, Tokyo City University, Tokyo, Japan
| | - Takashi Katsuta
- High Performance Sport Center, Japan Sport Council, Tokyo, Japan
| | - Ichiro Kono
- 2019 Rugby World Cup Organizing Committee, Tokyo, Japan
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8
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Ribeiro J, Davids K, Araújo D, Silva P, Ramos J, Lopes R, Garganta J. The Role of Hypernetworks as a Multilevel Methodology for Modelling and Understanding Dynamics of Team Sports Performance. Sports Med 2020; 49:1337-1344. [PMID: 31016547 DOI: 10.1007/s40279-019-01104-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Despite its importance in many academic fields, traditional scientific methodologies struggle to cope with analysis of interactions in many complex adaptive systems, including team sports. Inherent features of such systems (e.g. emergent behaviours) require a more holistic approach to measurement and analysis for understanding system properties. Complexity sciences encompass a holistic approach to research on collective adaptive systems, which integrates concepts and tools from other theories and methods (e.g. ecological dynamics and social network analysis) to explain functioning of such systems in their natural environments. Multilevel networks and hypernetworks comprise novel and potent methodological tools for assessing team dynamics at more sophisticated levels of analysis, increasing their potential to impact on competitive performance in team sports. Here, we discuss how concepts and tools derived from studies of multilevel networks and hypernetworks have the potential for revealing key properties of sports teams as complex, adaptive social systems. This type of analysis can provide valuable information on team performance, which can be used by coaches, sport scientists and performance analysts for enhancing practice and training. We examine the relevance of network sciences, as a sub-discipline of complexity sciences, for studying the dynamics of relational structures of sports teams during practice and competition. Specifically, we explore the benefits of implementing multilevel networks, in contrast to traditional network techniques, highlighting future research possibilities. We conclude by recommending methods for enhancing the applicability of hypernetworks in analysing team dynamics at multiple levels.
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Affiliation(s)
- João Ribeiro
- CIFI2D, Centre of Research, Education, Innovation and Intervention in Sport, Faculdade de Desporto, Universidade do Porto, Rua Dr. Plácido Costa, 91, 4200-450, Porto, Portugal.
| | - Keith Davids
- CSER, Sheffield Hallam University, Broomgrove Teaching Block, Broomgrove Road, Sheffield, S10 2LX, UK
| | - Duarte Araújo
- CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal
| | - Pedro Silva
- CIFI2D, Centre of Research, Education, Innovation and Intervention in Sport, Faculdade de Desporto, Universidade do Porto, Rua Dr. Plácido Costa, 91, 4200-450, Porto, Portugal
| | - João Ramos
- ISCTE-Instituto Universitário de Lisboa, Lisbon, Portugal.,Universidade Europeia, Laureate International Universities, Lisbon, Portugal
| | - Rui Lopes
- Universidade Europeia, Laureate International Universities, Lisbon, Portugal.,Instituto de Telecomunicações, Lisbon, Portugal
| | - Júlio Garganta
- CIFI2D, Centre of Research, Education, Innovation and Intervention in Sport, Faculdade de Desporto, Universidade do Porto, Rua Dr. Plácido Costa, 91, 4200-450, Porto, Portugal
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Dalton-Barron N, Whitehead S, Roe G, Cummins C, Beggs C, Jones B. Time to embrace the complexity when analysing GPS data? A systematic review of contextual factors on match running in rugby league. J Sports Sci 2020; 38:1161-1180. [DOI: 10.1080/02640414.2020.1745446] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Nicholas Dalton-Barron
- Carnegie Applied Rugby Research Centre, Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, UK
- England Performance Unit, Rugby Football League, Leeds, UK
- Catapult Sports, Melbourne, Australia
| | - Sarah Whitehead
- Carnegie Applied Rugby Research Centre, Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, UK
- Leeds Rhinos Rugby League Club, Leeds, UK
| | - Gregory Roe
- Carnegie Applied Rugby Research Centre, Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, UK
- Bath Rugby, Farleigh House, Farleigh Hungerford, Bath, UK
| | - Cloe Cummins
- Carnegie Applied Rugby Research Centre, Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, UK
- School of Science and Technology, University of New England, Armidale, New South Wales, Australia
- National Rugby League, Australia
| | - Clive Beggs
- Carnegie Applied Rugby Research Centre, Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, UK
| | - Ben Jones
- Carnegie Applied Rugby Research Centre, Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, UK
- England Performance Unit, Rugby Football League, Leeds, UK
- Leeds Rhinos Rugby League Club, Leeds, UK
- School of Science and Technology, University of New England, Armidale, New South Wales, Australia
- Yorkshire Carnegie Rugby Union Club, Leeds, UK
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Till K, Baker J. Challenges and [Possible] Solutions to Optimizing Talent Identification and Development in Sport. Front Psychol 2020; 11:664. [PMID: 32351427 PMCID: PMC7174680 DOI: 10.3389/fpsyg.2020.00664] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 03/19/2020] [Indexed: 11/13/2022] Open
Abstract
The modern-day landscape of Olympic and Professional sport is arguably more competitive than ever. One consequence of this is the increased focus on identifying and developing early athletic talent. In this paper, we highlight key challenges associated with talent (athlete) identification and development and propose possible solutions that could be considered by research and practice. The first challenge focuses on clarifying the purposes of talent identification initiatives such as defining what talent is and how its meaning might evolve over time. Challenge two centers on ways to best identify, select and develop talent, including issues with different approaches to identification, the need to understand the impact of development and the need to have appropriate resourcing in the system to support continued development of knowledge. Finally, we discuss two challenges in relation to the 'healthiness' of talent identification and development. The first examines whether a talent identification and development system is 'healthy' for athletes while the second focuses on how sport stakeholders could discourage the apparent trend toward early specialization in youth sport settings. Whilst this paper discusses the research in relation to these challenges, we propose multiple possible solutions that researchers and practitioners could consider for optimizing their approach to talent identification and development. In summary, talent is a complex and largely misunderstood phenomenon lacking robust research evidence, and given concerns that it is potentially unhealthy, talent identification and selection at younger ages is not recommended.
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Affiliation(s)
- Kevin Till
- Carnegie Applied Rugby Research (CARR) Centre, Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom
| | - Joseph Baker
- School of Kinesiology and Health Science, York University, Toronto, ON, Canada
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Abstract
The aim of this study was to characterize handball from a social network analysis perspective by analyzing 22 professional matches from the 2018 European Men's Handball Championship. Social network analysis has proven successful in the study of sports dynamics to investigate the interaction patterns of sport teams and the individual involvement of players. In handball, passing is crucial to establish an optimal position for throwing the ball into the goal of the opponent team. Moreover, different tactical formations are played during a game, often induced by two-minute suspensions or the addition of an offensive player replacing the goalkeeper as allowed by the International Handball Federation since 2016. Therefore, studying the interaction patterns of handball teams considering the different playing positions under various attack formations contributes to the tactical understanding of the sport. Degree and flow centrality as well as density and centralization values were computed. As a result, quantification of the contribution of individual players to the overall organization was achieved alongside the general balance in interplay. We identified the backcourt as the key players to structure interplay across tactical formations. While attack units without a goalkeeper were played longer, they were either more intensively structured around back positions (7 vs. 6) or spread out (5 + 1 vs. 6). We also found significant differences in the involvement of wing players across formations. The additional pivot in the 7 vs. 6 formation was mostly used to create space for back players and was less involved in interplay. Social network analysis turned out as a suitable method to govern and quantify team dynamics in handball.
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12
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Quantitative Spielanalyse – den Überblick bei zunehmender Heterogenität der Ansätze behalten. GERMAN JOURNAL OF EXERCISE AND SPORT RESEARCH 2019. [DOI: 10.1007/s12662-019-00623-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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13
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Buldú JM, Busquets J, Echegoyen I, Seirul Lo F. Defining a historic football team: Using Network Science to analyze Guardiola's F.C. Barcelona. Sci Rep 2019; 9:13602. [PMID: 31537882 PMCID: PMC6753100 DOI: 10.1038/s41598-019-49969-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 09/03/2019] [Indexed: 12/03/2022] Open
Abstract
The application of Network Science to social systems has introduced new methodologies to analyze classical problems such as the emergence of epidemics, the arousal of cooperation between individuals or the propagation of information along social networks. More recently, the organization of football teams and their performance have been unveiled using metrics coming from Network Science, where a team is considered as a complex network whose nodes (i.e., players) interact with the aim of overcoming the opponent network. Here, we combine the use of different network metrics to extract the particular signature of the F.C. Barcelona coached by Guardiola, which has been considered one of the best teams along football history. We have first compared the network organization of Guardiola's team with their opponents along one season of the Spanish national league, identifying those metrics with statistically significant differences and relating them with the Guardiola's game. Next, we have focused on the temporal nature of football passing networks and calculated the evolution of all network properties along a match, instead of considering their average. In this way, we are able to identify those network metrics that enhance the probability of scoring/receiving a goal, showing that not all teams behave in the same way and how the organization Guardiola's F.C. Barcelona is different from the rest, including its clustering coefficient, shortest-path length, largest eigenvalue of the adjacency matrix, algebraic connectivity and centrality distribution.
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Affiliation(s)
- J M Buldú
- Complex System Group & GISC, Universidad Rey Juan Carlos, Madrid, Spain.
- Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.
- Institute of Unmanned System and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, 710072, China.
| | | | - I Echegoyen
- Complex System Group & GISC, Universidad Rey Juan Carlos, Madrid, Spain
- Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - F Seirul Lo
- Departamento de Metodología, F.C. Barcelona, Barcelona, Spain
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14
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Laporta L, Afonso J, Valongo B, Mesquita I. Using social network analysis to assess play efficacy according to game patterns: a game-centred approach in high-level men’s volleyball. INT J PERF ANAL SPOR 2019. [DOI: 10.1080/24748668.2019.1669007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Lorenzo Laporta
- Centre for Research, Formation, Innovation and Intervention in Sport.Faculty of Sport, University of Porto, Porto, Portugal
| | - José Afonso
- Centre for Research, Formation, Innovation and Intervention in Sport.Faculty of Sport, University of Porto, Porto, Portugal
| | - Beatriz Valongo
- Centre for Research, Formation, Innovation and Intervention in Sport.Faculty of Sport, University of Porto, Porto, Portugal
| | - Isabel Mesquita
- Centre for Research, Formation, Innovation and Intervention in Sport.Faculty of Sport, University of Porto, Porto, Portugal
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15
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Korte F, Link D, Groll J, Lames M. Play-by-Play Network Analysis in Football. Front Psychol 2019; 10:1738. [PMID: 31402892 PMCID: PMC6669815 DOI: 10.3389/fpsyg.2019.01738] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 07/12/2019] [Indexed: 11/16/2022] Open
Abstract
This study identifies dominant and intermediary players in football by applying a play-by-play social network analysis (SNA) on 70 professional matches from the 1. and 2. German Bundesliga during the 2017/2018 season. SNA provides a quantification of the complex interaction patterns between players in team sports. So far, the individual contributions and roles of players in football have only been studied at match-level considering the overall passing of a team. In order to consider the real structure of football, a play-by-play network analysis is needed that reflects actual interplay. Moreover, a distinction between plays of certain characteristics is important to qualify different interaction phases. As it is often impossible to calculate well known network metrics such as betweenness on play-level, new adequate metrics are required. Therefore, flow betweenness is introduced as a new playmaker indicator on play-level and computed alongside flow centrality. The data on passing and the position of players was provided by the Deutsche Fußball Liga (DFL) and gathered through a semi-automatic multiple-camera tracking system. Central defenders are identified as dominant and intermediary players, however, mostly in unsuccessful plays. Offensive midfielders are most involved and defensive midfielders are the main intermediary players in successful plays. Forward are frequently involved in successful plays but show negligible playmaker status. Play-by-play network analysis facilitates a better understanding of the role of players in football interaction.
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Affiliation(s)
- Florian Korte
- Chair of Performance Analysis and Sports Informatics, Technical University of Munich, Munich, Germany
| | - Daniel Link
- Chair of Performance Analysis and Sports Informatics, Technical University of Munich, Munich, Germany
| | - Johannes Groll
- Chair of Performance Analysis and Sports Informatics, Technical University of Munich, Munich, Germany
| | - Martin Lames
- Chair of Performance Analysis and Sports Informatics, Technical University of Munich, Munich, Germany
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16
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Praça GM, Lima BB, Bredt SDGT, Sousa RBE, Clemente FM, de Andrade AGP. Influence of Match Status on Players' Prominence and Teams' Network Properties During 2018 FIFA World Cup. Front Psychol 2019; 10:695. [PMID: 30984084 PMCID: PMC6447613 DOI: 10.3389/fpsyg.2019.00695] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 03/12/2019] [Indexed: 11/15/2022] Open
Abstract
The analyses of players and teams’ behaviors during the FIFA World Cup may provide a better understanding on how football tactics and strategies have developed in the past few years in elite football. The Social Network Analysis (SNA) has been carried out in the investigations about passing distribution, improving the understanding on how players interact and cooperate during a match. In football official matches, studies have used the SNA as a means of coding players’ cooperation and opposition patterns. However, situational variables such as match status were previously investigated and associated with changes on teams’ dynamics within and/or between matches, but were not considered in studies based on Social Network Analysis. This study aimed to analyze the influence of match status on teams’ cooperation patterns and players’ prominence according to playing positions during 2018 FIFA World Cup. Fourteen matches of the knockout stage were analyzed. Macro and micro network measures were obtained from adjacency matrixes collected for each team, in each match status (winning, drawing, and losing). A one-way ANOVA was used to compare teams’ networks (macro-analysis variables) within each match status, while a two-way ANOVA (match status × playing position) was used to compare the micro-analysis variables. Results showed no differences between match status for macro analysis. Winning situations induced higher prominence in central midfielders (0.107; p = 0.001), wide midfielders (0.093; p = 0.001), and center forward (0.085; p = 0.001), while in losing situations lower prominence levels were observed for goalkeepers (0.044; p = 0.001) and center forward (0.074; p = 0.001). Data revealed that teams do not change macrostructures according to match status. On the other hand, the microstructures showed important adaptations regarding game styles, with changes in players’ behaviors according to playing positions. In general, the levels of centrality and prestige in players of different positions indicated a more direct play style in winning situations and a more build-up style in losing situations. These results allow a better understanding about the influence of match status on players’ and teams’ performance during high-level football competitions and may help coaches to improve athletes’ performance in these situations.
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Affiliation(s)
- Gibson Moreira Praça
- Centro de Estudos em Cognição e Ação/ UFMG Soccer Science Center, Departamento de Esportes, Escola de Educação Física, Fisioterapia e Terapia Ocupacional, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Bernardo Barbosa Lima
- Centro de Estudos em Cognição e Ação/ UFMG Soccer Science Center, Departamento de Esportes, Escola de Educação Física, Fisioterapia e Terapia Ocupacional, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sarah da Glória Teles Bredt
- Centro de Estudos em Cognição e Ação/ UFMG Soccer Science Center, Departamento de Esportes, Escola de Educação Física, Fisioterapia e Terapia Ocupacional, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Raphael Brito E Sousa
- Centro de Estudos em Cognição e Ação/ UFMG Soccer Science Center, Departamento de Esportes, Escola de Educação Física, Fisioterapia e Terapia Ocupacional, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Filipe Manuel Clemente
- School of Sport and Leisure, Polytechnic Institute of Viana do Castelo, Viana do Castelo, Portugal.,Instituto de Telecomunicações, Lisbon, Portugal
| | - André Gustavo Pereira de Andrade
- Centro de Estudos em Cognição e Ação/ UFMG Soccer Science Center, Departamento de Esportes, Escola de Educação Física, Fisioterapia e Terapia Ocupacional, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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17
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Castellano J, Echeazarra I. Network-based centrality measures and physical demands in football regarding player position: Is there a connection? A preliminary study. J Sports Sci 2019; 37:2631-2638. [PMID: 30893004 DOI: 10.1080/02640414.2019.1589919] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
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.
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Affiliation(s)
- J Castellano
- Faculty of Education and Sport, University of the Basque Country (UPV/EHU) , Vitoria-Gasteiz , Spain
| | - I Echeazarra
- Faculty of Education and Sport, University of the Basque Country (UPV/EHU) , Vitoria-Gasteiz , Spain
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18
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Buldú JM, Busquets J, Martínez JH, Herrera-Diestra JL, Echegoyen I, Galeano J, Luque J. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Front Psychol 2018; 9:1900. [PMID: 30349500 PMCID: PMC6186964 DOI: 10.3389/fpsyg.2018.01900] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 09/18/2018] [Indexed: 11/21/2022] Open
Affiliation(s)
- Javier M. Buldú
- Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
- Complex Systems Group and GISC, Universidad Rey Juan Carlos, Móstoles, Spain
- Grupo Interdisciplinar de Sistemas Complejos, Madrid, Spain
| | | | - Johann H. Martínez
- Grupo Interdisciplinar de Sistemas Complejos, Madrid, Spain
- INSERM-UM1127, Institute du Cerveau et de la Moelle Épinière. H. Salpêtrière, Paris, France
| | | | - Ignacio Echegoyen
- Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
- Complex Systems Group and GISC, Universidad Rey Juan Carlos, Móstoles, Spain
- Grupo Interdisciplinar de Sistemas Complejos, Madrid, Spain
| | - Javier Galeano
- Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, Madrid, Spain
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19
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Do Long-time Team-mates Lead to Better Team Performance? A Social Network Analysis of Data from Major League Baseball. Sports Med 2018; 48:2659-2669. [DOI: 10.1007/s40279-018-0970-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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