This was my first taste of a professional sports conference, focusing on ‘analytics’ in sport.
Analytics as a term has elbowed its way into my vocabulary without me ever really thinking about it – why not just stick with analysis? Looking analytics up online, the simplest definition is ‘the science of analysis’. But after today I think I will adopt Professor Steve Haake’s distinction of analysis as covering the investigation/interrogation of smaller data sets, whilst analytics applies at a much larger scale: to terra- or even peta-bytes of data analysis.
Today’s conference, at the Oval in south London, was well-organised and packed full of insight and experience from a large variety of experts belonging to top sporting teams and institutions.
The subjects varied from sports medicine and performance innovation at Olympic level to Red Bull’s highly contrarian approach in training their athletes and even a discussion on the growing importance of social media in sport. We were also treated to a presentation by a performance scientist [Dr Harvey Galvin] on his work on hypoxic training applied to professional tennis players for the LTA.
The general theme of the day which was evident throughout most of the thirteen 30min presentations referred to performance analysis in sport as a ‘journey’ with general agreement about the widespread changes and innovations over the past 5-10 years that have taken place in terms of the integration of ‘big data’ into sporting institutions.
There were also many different approaches to coaching discussed – perhaps the extremes were highlighted by Red Bull’s Darren Roberts who championed putting the emphasis on the athlete first and foremost and letting the individual drive their training requirements. His statement that “we don’t look at risk” was one of the more provocative sound bites of the day! On the other side of the coin came Chelsea’s Head of Academy Performance Systems – Ben Smith – who detailed Chelsea’s extensive monitoring of academy prospects involving in some cases daily reports and several different methods to give and receive feedback between player and coach. Checks, controls and reviews that the Red Bull team would probably have baulked at! Clearly different sports and individuals at different stages of their careers need different training approaches.
The general consensus was that the data analysis currently used isn’t by any means perfect and is difficult to apply with confidence in the workplace. In most sports there is clearly a strong requirement for cost/benefit analysis with regards to research and implementation which hinders some progress in innovation (and at the same time avoids other projects being given too much time and resource). All too often the problem seems to be that there are so many variables involved in every test, experiment or model that we can rarely convince ourselves how to correctly identify the proportion of effects from possible causes. For my money Dr Marco Cardinale, outgoing head of sports science for the BOA, gave the best explanation of how this ‘noise’ can only be overcome by evidence-based coaching. He provided a great analogy of the pharmacology model: ‘what is the smallest dosage of medicine to give the biggest possible effect’? This philosphy with regards to the aggregation of marginal gains has clearly stood the BOA in great stead recently and on the basis of his clear and richly informative presentation Dr Cardinale will be a real loss to the British team.
What I found notable was a marked reluctance from the speakers involved in team sports (rugby and football) to speak about prediction or forecasting. At least on a public level, I suppose that talking over-confidently about prediction and modelling is potential career suicide at least if you don’t state probabilistic outcomes (and even then you can end up with egg on your face). Name checked at various points throughout the day (and I expect more of the same tomorrow) were Moneyball/Michael Lewis, Nate Silver and Nicolas Taleb with his black swan theory – the cult celebrities of the data analysis movement over the past 5-10 years or so.
The highlights for me came in actually the last 2 presentations of the day: Prof Steve Haake from the Centre for Sports Engineering Research (CSER) and Tony Strudwick of Manchester United. For me, what set these presentations apart was the speakers’ willingness to provide detail and graphical information from their work and the practical initiatives they have been working on over the past few years.
Haake gave a really interesting view of his research into measured performance for different track and field events since records began. Did you know, for example, that the global men’s 100m best times follow a general improving trend which was stilted by WW1, WW2 and then in the 1970s when electronic timing was introduced and athletes ‘lost’ 0.4secs due to the removal of human timing (affected by reaction times)? He showed off some of the performance analysis innovations created for different olympic sports and how the CSER team have colloborated with coaches and athletes in order to produce the best products/services possible for the greatest possible effect.
Strudwick impressed me with his detailed discussion of sports science and its application at the top level for Manchester United, particularly in injury prevention. The reported link of metabolic power training loads to injuries and the spread of injuries through the playing year was interesting. In addition, the disclosure of Man Utd’s use of Matlab and advanced statistical analysis was beyond my expectations – and their partnership with Liverpool John Moores University for a collaboration on scientific research also shows forward-thinking in a way one could be forgiven for thinking may not happen behind the scenes at a club whose hierarchy and decision-making is so often reported alongside one name: Sir Alex Ferguson.
Next up is of course day 2 of SALDN. I’m following that up with the Science + Football conference at the weekend but I’ll post some updates on my thoughts from those experiences as soon as I can.