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Consistency and identifiability of football teams: a network science perspective.

ABSTRACT: We investigated the ability of football teams to develop a particular playing style by looking at their passing patterns. Using the information contained in the pass sequences during matches, we constructed the pitch passing networks of teams, whose nodes are the divisions of the pitch for a given spatial scale and links account for the number of passes from region to region. We translated football passings networks into their corresponding adjacency matrices. We calculated the correlations between matrices of the same team to quantify how consistent the passing patterns of a given team are. Next, we quantified the differences with other teams' matrices and obtained an identifiability parameter that indicates how unique are the passing patterns of a given team. Consistency and identifiability rankings were calculated during a whole season, allowing to detect those teams of a league whose passing patterns are different from the rest. Furthermore, we found differences between teams playing at home or away. Finally, we used the identifiability parameter to investigate what teams imposed their passing patterns over the rivals during a given match.

SUBMITTER: Garrido D

PROVIDER: S-EPMC7661721 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Consistency and identifiability of football teams: a network science perspective.

Garrido D D Antequera D R DR Busquets J J López Del Campo R R Resta Serra R R Jos Vielcazat S S Buldú J M JM

Scientific reports 20201112 1

We investigated the ability of football teams to develop a particular playing style by looking at their passing patterns. Using the information contained in the pass sequences during matches, we constructed the pitch passing networks of teams, whose nodes are the divisions of the pitch for a given spatial scale and links account for the number of passes from region to region. We translated football passings networks into their corresponding adjacency matrices. We calculated the correlations betw ...[more]

PMID: 33184412

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Project description:AimThe aim of the study was to describe practices that support collaboration in interprofessional primary health care teams, and identify performance indicators perceived to measure the impact of this collaboration from the perspective of interprofessional health providers.BackgroundDespite the surge of interprofessional primary health care models implemented across Canada, there is little evidence as to whether or not the intended outcomes of primary health care teams have been achieved. Part of the challenge is determining the most appropriate measures that can demonstrate the value of collaborative care. To date, little remains known about performance measurement from the providers contributing to the collaborative care process in interprofessional primary care teams. Having providers from a range of disciplinary backgrounds assist in the development of performance measures can help identify measures most relevant to demonstrate the value of collaborative care on the intended outcomes of interprofessional primary care models.MethodsA qualitative study; part of a larger mixed methods developmental evaluation to examine performance measurement in interprofessional primary health care teams. A stakeholder workshop was conducted at an annual association meeting of interprofessional primary health care teams in the province of Ontario, Canada. Six questions guided the workshop groups and participant responses were documented on worksheets and flip charts. All responses were collected and entered verbatim into a word document. Qualitative analytic strategies were applied to each question.FindingsA total of 283 primary health care providers from 14 health professions working in interprofessional primary health care teams participated. Top three elements of interprofessional collaboration (total n = 628) were communication (n = 146), co-treatment (n = 112) and patient-based conferences (n = 81). Top three performance indicators currently used to demonstrate the value of interprofessional collaboration (total n = 241) were patient experience (n = 71), patient health status (n = 35) and within team referrals (n = 30).

Project description:Possession time in football has been widely discussed in research but few studies have analyzed the importance of the field area in which possession occurs. The objective of this study was to identify the existence of significant differences in the field zone of ball possession between successful and unsuccessful teams and to acknowledge if the match status modulates the possession model. To this end, 2,284 attacks were analyzed corresponding to the matches in the final phase of the UEFA Euro 2016 France, recording possession time and field zone in which possession occurred. Video recordings of matches were analyzed and coded post-event using notational analysis. We have found that successful offensive game patterns are different from unsuccessful ones. Specifically, field zone in which major possession occurs changes significantly between successful and unsuccessful teams (x2 = 15.72, p < 0.05) and through Welch's T significant differences were detected in possession time between successful and unsuccessful teams (H = 24.289, p < 0.001). The former are characterized by longer possession times, preferably in the middle offensive zone, on the other hand, unsuccessful teams have shorter possession times and preferably on the middle defensive zone. Logistic regression also allowed us to identify that greater possession in the middle offensive zone is a good indicator of success in the offensive game, allowing us to predict a greater chance of victory in the match. Specifically, every time the teams achieve possession in the middle offensive zone, the chance of winning the match will increase 1.72 times and, the probability of winning the match making longer possessions in the middle offensive zone is 44.25%. Applying the Kruskal-Wallis test we have also been able to verify how match status modulates the teams possession time, specifically, when teams are winning they have longer possessions x2 = 92.628, p = 0.011. Results obtained are expected to help gain more knowledge about successful offensive game models, as well as performance factors of the offensive phase, which will allow teams to optimize their training process and performance during the match.

Dataset Information

Consistency and identifiability of football teams: a network science perspective.

Publications

Consistency and identifiability of football teams: a network science perspective.

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