Technology and ‘Big Data’ in high performance sport

We draw mainly on the findings from a 2 year research project centered on an Aviva Premiership Rugby Union team. This study offers deeper and more nuanced empirical and theatrical frames to discuss the merits of technology in sport to inform the practice of both coaching and coach education. High Performance Sport has seen an increasing emphasis on ‘Big Data’ operationalized through multiple technological devices leading to heavily led statistical approaches. Administered with conviction because of the perceived benefits where ‘Big Data’ can deliver ‘better’ knowledge (Boyd & Crawford, 2012). It is suggested that ‘Big Data’ is a progressive force in sport but there are some heightened critical questions that require answering (Millington & Millington, 2015). These topics would include: What is the impact ‘data overload’ can have on sports performance? How do technologies intervene between coaches and player relationships? How is the coaching role changing to accommodate masses of data? The contradictions of using ‘Big Data’ in the sporting workplace. What are the lived implications of ‘Big Data’ from the elite athlete perspective? Results reflect the actions of a technocratic coach who was compelled to interpret and manipulate numeric views of individual and collective performance. This statistically created reality provided a powerful objective mandate to control and regulate players who became fixated with meeting performance indictors shutting down improvisation on the field of play (Williams and Manley, 2014). Through being subjected to endless standardisation and evaluation this created an uncomfortable socio-technological alliance which burdened the players to such an extent that they were left broken. However, after the dismissal of this Head Coach a new dawn was heralded and collective self-esteem was restored through a more humanistic balance between art and science being struck. The combination of findings from this longitudinal project responds to calls to demonstrate both ‘best’ and ‘worst’ practice in regard to analysis in team sports (Passos, Araujo and Volossovitch 2016).