Team sports performance analysed through the lens of social network theory: implications for research and practice

This paper discusses how social network analyses and graph theory can be implemented in team sports performance analyses to evaluate individual (micro) and collective (macro) performance data, and how to use this information for designing practice tasks. Moreover, we briefly outline possible limitations of social network studies and provide suggestions for future research. Instead of cataloguing discrete events or player actions, it has been argued that researchers need to consider the synergistic interpersonal processes emerging between teammates in competitive performance environments. Theoretical assumptions on team coordination prompted the emergence of innovative, theoretically-driven methods for assessing collective team sport behaviours. Here, we contribute to this theoretical and practical debate by conceptualising sports teams as complex social networks. From this perspective, players are viewed as network nodes, connected through relevant information variables (e.g., a ball passing action), sustaining complex patterns of interaction between teammates (e.g., a ball passing network). Specialized tools and metrics related to graph theory could be applied to evaluate structural and topological properties of interpersonal interactions of teammates, complementing more traditional analysis methods. This innovative methodology moves beyond use of common notation analysis methods, providing a richer understanding of the complexity of interpersonal interactions sustaining collective team sports performance. The proposed approach provides practical applications for coaches, performance analysts, practitioners and researchers by establishing social network analyses as a useful approach for capturing the emergent properties of interactions between players in sports teams

What Performance Analysts Need to Know About Research Trends in Association Football (2012–2016): A Systematic Review

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

CPU and Cache Efficient Management of Memory-Resident Databases

Memory-Resident Database Management Systems (MRDBMS) have to be optimized for two resources: CPU cycles and memory bandwidth. To optimize for bandwidth in mixed OLTP/OLAP scenarios, the hybrid or Partially Decomposed Storage Model (PDSM) has been proposed. However, in current implementations, bandwidth savings achieved by partial decomposition come at increased CPU costs. To achieve the aspired bandwidth savings without sacrificing CPU efficiency, we combine partially decomposed storage with Just-in-Time (JiT) compilation of queries, thus eliminating CPU inefficient function calls. Since existing cost based optimization components are not designed for JiT-compiled query execution, we also develop a novel approach to cost modeling and subsequent storage layout optimization. Our evaluation shows that the JiT-based processor maintains the bandwidth savings of previously presented hybrid query processors but outperforms them by two orders of magnitude due to increased CPU efficiency