The need for weighting indirect connections between game variables: Social Network Analysis and eigenvector centrality applied to high-level men’s volleyball

The Decision-Making of High-Level Volleyball Setters in the 2021-2022 Volleyball Men's Superliga: Does the Opponent Matter?

Our primary objective in this study was to investigate the offensive strategies employed in the attack phase of men's volleyball, specifically focusing on side-out as stratified by the type of confrontation that was determined by the opponent's team performance. We analyzed 5524 attacking actions during 22 games of the 12 teams that participated in the Volleyball Men's Superliga (season 2021-2022). Based on their final rankings in the championship, we classified these teams into three tiers: high-performance, intermediate-performance, and low-performance. Subsequently, we examined the dynamics of these matches using Social Network Analysis. We found that the opponent teams' performance levels did not influence the game dynamics. Notably, the eigenvector values were prominently higher for Attack Zones 2 and 4, wherein the middle-blocker jumped to attack close to the setter across all networks. Thus, setters opted for traditional and low-risk strategies to minimize errors, disregarding available information about the skill level of the opposing team, making their offensive tactics predictable.

Contextualizing Evaluation of Performance in Volleyball: Introducing Contextual Individual Contribution Coefficients to Assess Technical Actions

In this study, we proposed novel metrics for evaluating volleyball technical performance in relation to the action context. To assess each player's relative participation, we also introduced relative contextual coefficients. We analyzed 20 games played by the world's top eight teams during the 2019 FIVB Women's Club World Championship, using Data Volley software and Python programming. We evaluated inter- and intra-observer reliability and used binomial logistic regression models to estimate each variable's probability of having contributed to the team's set win. We calculated estimated confidence intervals, standard errors, and Wald values; and we employed Akaike's and Bayesian criteria to evaluate the model's goodness of fit. We identified optimal cut-off points using receiver operating characteristic curves, and we found that the proposed contextual evaluation coefficients prevented overestimation of a player's technical performance in uneven situations. We addressed the issue in which the winning team may be the one that scored the fewest points, and we were able to predict a team's victory with confidence. The proposed coefficients made multiple technical and contextual aspects of the game easily accessible and comprehensible, offering significant beneficial implications for coaches and players.

Inter e intra-variability of the best ranked teams: A network analysis in male high-level volleyball

The present research objective was to analyze the offensive phase from Complex I in high-level male volleyball teams in a macro- and micro-level view, through the inter e intra-team variability analysis of eight best teams of the 2018 Men's Volleyball World Championship over the social network analysis and eigenvector centrality. The sample consisted of 22 matches and 2,743 offensive actions, resulting in 8 sub-networks with 368 nodes and 6221 edges. The results showed from macro view the variables that presented highest centrality values were Attack Zone 4 (range 0.56-0.90), Attack Tempo 2 (0.65-0.87), Power Attack (0.62-0.94), No Touch Block (0.61-1), Attack Effect Continuity (0.59-0.94), and Middle Blocker Centralized (0.60-0.95). In a micro view, Reception Effect, Play Position, Reception Zone, and Block Composition showed high variability in each sub-network. The intra- and inter-team variability presented the importance of to respect each team idiosyncrasies and to consider the different approaches to the game and success.

Assessing Neonatal Intensive Care Unit Structures and Outcomes Before and During the COVID-19 Pandemic: Network Analysis Study

Background

Health care organizations (HCOs) adopt strategies (eg. physical distancing) to protect clinicians and patients in intensive care units (ICUs) during the COVID-19 pandemic. Many care activities physically performed before the COVID-19 pandemic have transitioned to virtual systems during the pandemic. These transitions can interfere with collaboration structures in the ICU, which may impact clinical outcomes. Understanding the differences can help HCOs identify challenges when transitioning physical collaboration to the virtual setting in the post–COVID-19 era.

Objective

This study aims to leverage network analysis to determine the changes in neonatal ICU (NICU) collaboration structures from the pre– to the intra–COVID-19 era.

Methods

In this retrospective study, we applied network analysis to the utilization of electronic health records (EHRs) of 712 critically ill neonates (pre–COVID-19, n=386; intra–COVID-19, n=326, excluding those with COVID-19) admitted to the NICU of Vanderbilt University Medical Center between September 1, 2019, and June 30, 2020, to assess collaboration between clinicians. We characterized pre–COVID-19 as the period of September-December 2019 and intra–COVID-19 as the period of March-June 2020. These 2 groups were compared using patients’ clinical characteristics, including age, sex, race, length of stay (LOS), and discharge dispositions. We leveraged the clinicians’ actions committed to the patients’ EHRs to measure clinician-clinician connections. We characterized a collaboration relationship (tie) between 2 clinicians as actioning EHRs of the same patient within the same day. On defining collaboration relationship, we built pre– and intra–COVID-19 networks. We used 3 sociometric measurements, including eigenvector centrality, eccentricity, and betweenness, to quantify a clinician’s leadership, collaboration difficulty, and broad skill sets in a network, respectively. We assessed the extent to which the eigenvector centrality, eccentricity, and betweenness of clinicians in the 2 networks are statistically different, using Mann-Whitney U tests (95% CI).

Results

Collaboration difficulty increased from the pre– to intra–COVID-19 periods (median eccentricity: 3 vs 4; P<.001). Nurses had reduced leadership (median eigenvector centrality: 0.183 vs 0.087; P<.001), and neonatologists with broader skill sets cared for more patients in the NICU structure during the pandemic (median betweenness centrality: 0.0001 vs 0.005; P<.001). The pre– and intra–COVID-19 patient groups shared similar distributions in sex (~0 difference), race (4% difference in White, and 3% difference in African American), LOS (interquartile range difference in 1.5 days), and discharge dispositions (~0 difference in home, 2% difference in expired, and 2% difference in others). There were no significant differences in the patient demographics and outcomes between the 2 groups.

Conclusions

Management of NICU-admitted patients typically requires multidisciplinary care teams. Understanding collaboration structures can provide fine-grained evidence to potentially refine or optimize existing teamwork in the NICU.

Habitat Quality and Social Behavioral Association Network in a Wintering Waterbirds Community

Migratory waterbirds concentrated in freshwater ecosystems in mosaic environments rely on quality habitats for overwintering. At West Dongting Lake National Nature Reserve (WDLNNR), China, land-use change and hydrology alternation are compounding factors that have affected important wintering areas for migratory waterbirds. Presently, changes in the hydrology and landscape have reshaped natural wintering habitats and their availability, though the impact of hydrological management on habitat selection of wintering waterbirds is largely unknown. In this study, we classified differentially managed habitats and calculated their area using the normalized difference vegetation index (NDVI) to evaluate suitable habitat availability over the study period (2016–2017 and 2017–2018 wintering periods). We then used social behavioral association network (SBAN) model to compare habitat quality through species-species social interactions and species-habitat associations in lakes with different hydrological management. The results indicated that social interactions between and within species structured wintering waterbirds communities, which could be dominated by one or more species, while dominant species control the activities of other co-existing species. Analysis of variance (ANOVA) tests indicated significant differences in SBAN metrics between lakes (p = 0.0237) and habitat (p < 0.0001) levels. Specifically, lakes with managed hydrology were preferred by more species. The managed lakes had better habitat quality in terms of significantly higher habitat areas (p < 0.0001) and lower habitat transitions (p = 0.0113). Collectively, our findings suggest that proper hydrological management can provide continuous availability of quality habitats, especially mudflats and shallow waters, for a stable SBAN to ensure a wintering waterbirds community with more sympatric species in a dynamic environment.

Coexistence of Distinct Performance Models in High-Level Women's Volleyball

In performance analysis, and most notably in match analysis, generalizing game patterns in a sport or competition may result in formulating generic models and neglecting relevant variability in benefit of average or central values. Here, we aimed to understand how different game models can coexist at the same competitive level using social network analysis with degree centrality to obtain systemic mappings for six volleyball matches, one for each of the six national teams playing in the 2014 World Grand Prix Finals, guaranteeing a homogeneous game level and balanced matches. Although the sample was not recent, this was not relevant for our purposes, since we aimed to merely expose a proof of concept. A total of 56 sets and 7,176 ball possessions were analysed through Gephi Software, considering game actions as nodes and the interaction between them as edges. Results supported the coexistence of different performance models at the highest levels of practice, with each of the six teams presenting a very distinct game model. For example, important differences in eigenvector centrality in attack zones (ranging from 0 to 34) and tempos (20 to 38) were found between the six teams, as well as in defensive lines (20 to 39) and block opposition (22 to 37). This further suggests that there may be multiple pathways towards expert performance within any given sport, inviting a re-conceptualization of monolithic talent identification, detection and selection models. Future studies could benefit from standardizing the metrics in function of the number of ball possessions.

The Sequencing of Game Complexes in Women's Volleyball

In volleyball, each team must use no more than three hits to return the ball to the opponent’s court. This unique aspect of volleyball means that playing actions can be grouped into different complexes, mainly based on the initial defensive action. The purpose of this study was to find out which game complexes are most common in women’s volleyball and how those phases are sequenced. The study analyzed 4,252 complexes from 1,176 rallies or points (seven matches, with 27 sets in total) in the 2015 and 2016 Copa de la Reina. The variables analyzed were the game complex, complex efficacy, and number of complexes per point. Two Markov chains were defined to visualize how the complexes are sequenced. The first chain looked only at categories of the game complex variable, taking seven states and 24 transitions into consideration. The second chain combined the game complex and complex efficacy variables, taking 26 states and 125 transitions into consideration. These chains provide practical information regarding which sequences of complexes occur most frequently in the competition analyzed, and therefore which ones should be the main focus in training sessions. The most frequent sequence was Complex 0 (the serve), followed by Complex I with in-system attack, followed by Complex II without continuity.