Without doubt, the Rio Olympic Games were the most data-driven Olympics ever. From Japan to Britain to Australia, almost all the leading nations used predictive and even real time data to “get that edge” over rivals. Some succeeded, some did not. Australia belongs to the latter category.
The performance of Australian sportspersons has been disappointing compared to their previous outings. The country ended up with a medals tally of 29 which included 8 gold. The poor performance, besides a public outcry, forced even the Australian Olympic Committee (AOC) President John Coates to go on record midway through the Games to say that "something has seriously gone wrong in Rio".
After all, the Australian Government had coughed up close to $340 million in the last four years to fund the development of the Rio Olympic contingent. A respected name in Australian sports research Professor Brian Stoddart even pegged it at close to $600 million after allotments of over $200 million from state and territory governments were included.
While a lot of data around the Australian teams and its athletes is being bandied about and dissected (there’s talk of $20 million spent per medal), I have analysed the available data to understand which Australian teams “delivered the best bang for the buck”, manner of speaking, and the ones that have sadly been costly washouts. [Special attention: Australian Institute of Sport (AIS)].
So rather than break it down to simple averages, this breakup will help readers better understand which sport delivered and which did not.
One presumption made while analysing the performance output from the Rio Olympics is that winning a gold medal is much harder than winning a bronze. As such, my statistical model has pegged a Gold as equivalent to 3 medal points, Silver as 2 medal points and Bronze as 1 medal point.(See Olympics table for details results and table of medal points.)
The BigInsights “Best Bang 4 Buck” Aussie Olympics Teams
The AIS, which recruits Australia’s best athletes, and has been using technology and data for over a decade now to get more athletes on the podium, must look at the above results to separate the performers from the failures, in order to understand the macro picture. It needs to match it against its data base of Olympians, best athletes and of course, each sporting category, to figure out why some things worked, why others didn’t. For example, in the “Washout” category, was it the talent identification factor that had gone awry, or was it talent tracking, to begin with?
That’s the way data analytics can unlock the potential in sports teams, and help organisations form strategies that are backed by historical data and performance predictions.