How to use IoT and data to transform the economics of a sport

“I’ve worked in sports for 20 years, and pro road cycling has by far the most dysfunctional economic model,” says Graham Bartlett, CEO, Velon. “It really doesn’t work. The reaction to that was to form and create Velon.”

Why has professional cycling been so dysfunctional? Global commercial cycling revenues are only $600 million – compared to $8.2 billion in the NFL, and $16.8 billion between just the five biggest European soccer leagues.

The difference is these sports benefit from being able to host large, ticketed events in one place. Cycling, where races cover thousands of miles of open road, does not have this luxury. Without equivalent infrastructure, cycling struggles to achieve the commercial heights of other sports.

Ironically, with pro cycling it's the most dedicated fans – those waiting by the roadside – that can miss out the most. More casual viewers, watching on TV or listening on the radio, can get a more holistic coverage of the race: fans by the roadside can only catch a glimpse of the cyclists as they go past.

But cycling does have its own assets: professional cyclists generate huge amounts of data, from heart rate, to bike speed to information about the terrain on which they are riding. Properly captured, this data presents a huge opportunity for cycling superfans to develop closer, and more intimate engagements with the sport and their favourite teams and riders. 

Velon has close access to these opportunities – the teams they represent generate terabytes of raw data every year. Velon’s goal was to capture, transform, and deliver this data to a global cycling audience of 565 million people.

“We came up with this fascinating problem, which is how do you transmit thousands of data points per second out of a live sporting event while it moves from Bologna to Rome, all across the Alps and everywhere else,” remembers Bartlett. “Pretty much everybody we explained it to except the guys at EY said, ‘Forget it. You're crazy. You can do that at a stadium – and even then it's difficult – but to do it in a bike race that moves 200 miles down the road every day? No chance.’ EY teams looked at it and said, "This is a fantastic IoT challenge.’"

And if this crazy challenge could be overcome, EY and Velon would have the opportunity to give fans worldwide a first-class platform through which to directly engage with cycling – and create the foundations for a truly effective commercial cycling economy.

While it may be relatively easy to capture this data from a stationary exercise bike in a gym, out in the field for a sport in which races cover dozens of miles a day – often through remote countryside with limited connectivity – the logistics were staggering.

To make matters worse, the devices also need to be removed and charged overnight. As races often continue for several days – and bike parts are changed regularly during that time – special care needs to be taken to ensure that each cyclist is paired with the correct device each morning to build up the correct aggregate race data. With data becoming ever more fundamental to the sport, one error in refitting a device could create significant problems for the teams, organizers, broadcasters, and Velon alike – and potentially lose the trust of the fans.

“It’s been an incredibly complicated technical challenge to get that information, and then do something sensible with it, that we can drive insight from, visualize, and show to consumers in a meaningful way,” says Michael Henry, Director, EY Analytics. “We’re taking vast amounts of information across bikes that move up to 100kmph, and they’re travelling across thousands of miles for eight hours at a time. So we’ve got some incredible technical challenges when doing that, over mountains and incredibly isolated terrain, for long periods. We had challenges with water-proofing, battery life, and also the precision and accuracy of the bikes and where they are.”

2.  A trusted, secure central data aggregation platform

After gathering the data on the bikes, it needs to be relayed to a central aggregation platform, which aggregates rider data and combines it with external information sources (such as team names, stats and route maps) to enable deeper analysis and insight.

Even seemingly simple information like calculating rider speed can prove surprisingly complex out on the road, with patchy data signals and GPS coverage – to calculate speed required combining on-bike cadence and power data with GPS data, as no one information source would be sufficient alone.

To compound the challenge further – some of the data being collected needed to be kept proprietary to the individual racing teams. After all, these teams are in competition, and use data to benchmark, analyze and improve their performances – all in a sport that generates prize money and sponsorship opportunities for the winners. While some of the aggregated data is made public, there also need to be clear firewalls between sensitive information that could give competitors unfair insights into each other’s performance and tactics.

“We are the guardian in the middle, owned and trusted by those teams, and they put their faith in us,” explains Bartlett. “That's why working with a collaborator like EY teams is essential, because you have to have a degree of certainty and trust that the right firewalls and the right positions are there, and make sure that nobody could get into that data. That's what we have with EY teams.”

The solution was the EY data platform (pdf), which provides a broad capability for high-speed streaming ingestion, storage, cognitive analytics and artificial intelligence. Within the Velon project, this provided the backbone for building the aggregation and analytics functions of the app.