Consensus on a netball video analysis framework of descriptors and definitions by the netball video analysis consensus group.

Using an expert consensus-based approach, a netball video analysis consensus (NVAC) group of researchers and practitioners was formed to develop a video analysis framework of descriptors and definitions of physical, technical and contextual aspects for netball research. The framework aims to improve the consistency of language used within netball investigations. It also aims to guide injury mechanism reporting and identification of injury risk factors. The development of the framework involved a systematic review of the literature and a Delphi process. In conjunction with commercially used descriptors and definitions, 19 studies were used to create the initial framework of key descriptors and definitions in netball. In a two round Delphi method consensus, each expert rated their level of agreement with each of the descriptors and associated definition on a 5-point Likert scale (1-strongly disagree; 2-somewhat disagree; 3-neither agree nor disagree; 4-somewhat agree; 5-strongly agree). The median (IQR) rating of agreement was 5.0 (0.0), 5.0 (0.0) and 5.0 (0.0) for physical, technical and contextual aspects, respectively. The NVAC group recommends usage of the framework when conducting video analysis research in netball. The use of descriptors and definitions will be determined by the nature of the work and can be combined to incorporate further movements and actions used in netball. The framework can be linked with additional data, such as injury surveillance and microtechnology data.

Creating a Live and Flexible Normative Dataset for Netball.

A previous research has identified large data and information sources which exist about netball performance and align with the discussion of coaches during the games. Normative data provides context to measures across many disciplines, such as fitness testing, physical conditioning, and body composition. These data are normally presented in the tables as representations of the population categorized for benchmarking. Normative data does not exist for benchmarking or contextualization in netball, yet the coaches and players use performance statistics. A systems design methodology was adopted for this study where a process for automating the organization, normalization, and contextualization of netball performance data was developed. To maintain good ecological validity, a case study utilized expert coach feedback on the understandability and usability of the visual representations of netball performance population data. This paper provides coaches with benchmarks for assessing the performances of players, across competition levels against the player positions for performance indicators. It also provides insights to a performance analyst around how to present these benchmarks in an automated "real-time" reporting tool.

Visualizing Rugby Game Styles Using Self-Organizing Maps.

Rugby coaches and analysts often use notational data describing match events to assess their team's performance and to devise strategic plans for upcoming matches. However, given the volume and complexity of the data available, it is difficult for them to recognize high-dimensional relationships among the available performance variables. A nonlinear approach using self-organizing maps (SOM) can help visualize the performance of a team and its opponents as well as the subsequent suitability of certain game styles, given the style of the opponent.