At 6ft 8ins Olympic rower Paul Bennett was the tallest member of Team GB’s victorious men’s eight crew in Rio – and a part-time tech intern.
In an immense performance in the final, Team GB led from start to finish to beat their great rivals Germany to play their part in an incredible overall performance by Britain in Brazil.
The 27-year-old Oxford Blue, who has a Masters in computer science, has seen first-hand how performance analytics and the information it delivers can help elite athletes in their pursuit of success.
Earlier this year London-born Bennett, who is also a double world rowing champion, was appointed as an intern at SAS, the analytics partner for British Rowing and the GB Rowing Team.
He told BusinessCloud: “Currently the main use of data we have is through a system referred to as ‘telemetry’. Essentially this is a sensor system which we can fix to boats to gather data about the rowing stroke and the boat speed.
“From this we draw conclusions about how each of us is rowing, and whether we are rowing together. It is essentially a form of coaching by data.
“It has in the past been shown to be useful for identifying areas in which we can improve so we can be better prepared for racing.”
He adds: “The key pieces of information I use are predominantly are my ergo scores. If I am pumping out personal bests all year then I think I am on to be winning medals come the summer.
“When we are in crews we have fixed goals for boat speed during training pieces. These speeds are based on the fastest speeds that boats have ever done. We aim to be beating those speeds when we are practicing at or close to maximum intensity.”
There is also a psychological edge to performance analytics. Bennett says: “Knowing that you are going fast in training and knowing that you are physically at your fittest both can do a lot to calm your nerves before racing.”
He also sees performance analytics continuing to develop in his and other sports. “Currently it all feels very rudimentary. There is a huge amount we can do with the data we have.”
Britain’s rowers returned from Rio with three gold medals – it’s a sport where success is now demanded and expected.
With top British rowers training multiple times a day, the amount of data that a single athlete can produce can be extensive. On top of that there is the combined crew information to sift through.
SAS has worked with British Rowing to provide the tools to examine and interpret all this information more quickly and in-depth.
Mark Homer, GB Rowing Team’s senior sports scientist, says: “Bringing together that array of data, combined with data from competition, we have a huge resource to inform our training and help to enhance athlete performance. But the data is nothing without the tools to analyse it.”
Data analytics is used to spot initial signs of injury so training regimes can be tailored accordingly, enabling rowers to miss fewer sessions. Data modelling also provides the knowledge that allows more informed coaches and managers to make better decisions.
Steve Ludlow, SAS UK and Ireland’s principal analyst, says: “The GB Rowing Team collects lots of different data on each of the rowers – strength and conditioning data, on the water data, physiological data such as blood lactate levels, biomechanical data – as well as other data types such as weather data.
“We analyse their data to find insights that aid decision-making. We can spot anomalies in the data and look into why they might exist, and we can look into correlations between one set of data and another.
“As well as better understanding of what the data is telling us, it can help uncover things we may have not previously known about – the hidden gems or needles in the haystack.
“All this ultimately helps the coaches make better decisions so the boats go faster. If we can find a few marginal gains here and there, that can make a significant difference.
“At the Olympics in Rio, you will have noticed that some of the races were extremely close including one, not involving the GB Rowing Team, close to a dead heat.”
He adds: “A key element is the in-built analytical capability – it’s predictive, meaning it can be used to generate insights into various future scenarios, making it a key aid to better decision-making.
“For example, it could reveal information about a particular rower that is indicative of a slight muscle strain that could lead to a full-blown injury unless their training schedule is adapted accordingly.”