Hughes MT, Hughes MD, Williams J, James N, Vuckovic G, Locke D. Performance indicators in rugby union. J. Hum. Sport Exerc. Vol. 7, No. 7, pp. 383-401, 2012. Team performance in rugby has typically been assessed through the comparison of winning and losing teams, however, the distinction between winning and losing was used as the sole independent variable. Thus potential confounding variables that may affect performance such as match venue, weather conditions and the strength of the opposition were not considered in this profile of a rugby team. Insufficient data currently exist regarding the development and measurement of performance indicators in rugby union. In particular, there is little research concerning position-specific performance indicators and their subsequent performance profiles. Research has also yet to establish the confidence to which these performance profiles are representative of an individual's performance. The aim of this study was to exploit the unique opportunity of a large dataset from the 2011 World Cup, from analysts working with national teams, and combine this with examples of data taken from previous studies, in an attempt to identify a more focused direction for the analysis of rugby union. The majority of data collected in the results section were during and after the 2011 Ruby Union World Cup in New Zealand by professional analysts working for a firm called PGIR, which has the analysis franchise for the England RFU. All data were checked for accuracy and reliability by cross-referencing actions to post event from video. It was concluded that in a complex dynamic interactive team sport, such as rugby, that simple analyses of frequency data, although informative, cannot possibly be expected to model this very difficult and multivariate problem.
The growing dependency on digital technologies is becoming a way of life, and at the same time, the collection of data using them for surveillance operations has raised concerns. Notably, some countries use digital surveillance technologies for tracking and monitoring individuals and populations to prevent the transmission of the new coronavirus. The technology has the capacity to contribute towards tackling the pandemic effectively, but the success also comes at the expense of privacy rights. The crucial point to make is regardless of who uses and which mechanism, in one way another will infringe personal privacy. Therefore, when considering the use of technologies to combat the pandemic, the focus should also be on the impact of facial recognition cameras, police surveillance drones, and other digital surveillance devices on the privacy rights of those under surveillance. The GDPR was established to ensure that information could be shared without causing any infringement on personal data and businesses; therefore, in generating Big Data, it is important to ensure that the information is securely collected, processed, transmitted, stored, and accessed in accordance with established rules. This paper focuses on Big Data challenges associated with surveillance methods used within the COVID-19 parameters. The aim of this research is to propose practical solutions to Big Data challenges associated with COVID-19 pandemic surveillance approaches. To that end, the researcher will identify the surveillance measures being used by countries in different regions, the sensitivity of generated data, and the issues associated with the collection of large volumes of data and finally propose feasible solutions to protect the privacy rights of the people, during the post-COVID-19 era.
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