The Sports Analytics Innovation Summit 2014 in London
Put some distinguished sports scientists and managers in one of London’s top sports venues (the Emirates Stadium), add several presentations about how stats and data can be used to enhance sporting performance,
mix with generous doses of efficient organization and good networking… and the recipe for a great Sports Analytics Innovation Summit is served!
If you’re mad about sports and sports analysis like me, the recipe works wonders. The Summit was interesting because the sports people in it made it interesting. Though the focus was strongly on how to use training and competition data to improve individual athletes’ performance, the speakers – and topics - were diverse enough to cater for non-scientists too.
The presentations ranged from race-analysis in Olympic swimming (showing that you can swim as fast as the winner and yet end up 7’’ behind them – see below why!) to how you can collect tens of thousands of data points regarding a Premier League football team’s players – and what to do with them. From the issues in planning personalised training sessions for Team GB boxers, to how Barcelona FC manages the breadth of sporting knowledge its over 1,400 athletes and 1,300 (!) employees collectively generate.
What struck me is how crucial can predictive info based on high and low-tech data collection can be in shaping training strategies for sports such as swimming, where time is key and you can set target times for e.g. winning the next Olympic 200 m freestyle gold medal.
While on the other hand, in sports where the issues are different, such as football, large volumes of player metrics serve for example to create models to predict the likelihood of injuries.
Big data approaches for predictive purposes in (elite) sports may not be here yet – one recurrent issue was the relatively small size of data samples scientists had to work with – but the road is clearly marked. An increasingly high number of key decisions both on and off the sporting field are being taken using objective data in the form of performance metrics, ratings and statistical models.
As this is GSN’s own specialty, we can only be happy about it, and keep working on refining our statistics.
Finally, a big thank you to the Innovation Enterprise for running a highly informative and entertaining event.
And what about the 7’’ gap (an eternity...) between the USA and GBR Olympic 4 x 100 m freestyle relay at London 2012? While the speeds (in metre per second) in the swimming parts of the race were identical, Phelps, Lochte and co. won hands - and feet - down in the other, equally crucial, parts: the start off the blocks, the underwater phase and the turns. Analyse, analyse, analyse!
