Peter Gray leads Dimension Data’s technology practice and is heavily involved in the company’s sponsorship with the Tour de France. Sport is now an industry that depends on technology and data.
“The reason that’s changing is that our expectations are changing,” he said.
With sport being the last holdout on commercial TV, it is an important medium for sporting codes. But the fan experience has changed.
The Tour de Franc is owned by ASO. Started in 1903, Le Tour was was actually started as a way of selling newspapers. But over time it has evolved from newspapers, to radio and now television. The Tour is more than a cycling event now. It has evolved into a deeper experience.
Dimension Data leverages IoT to access live data from athletes and make it available to fans. That covers hardware, software and services. While there is significant infrastructure that follows the event in trucks, as cloud technologies have matured, the footprint that follows cyclists is becoming easier to deploy.
Data collection was the starting point, Gray said. Digitising that data was a challenge. Before Dimension Data’s involvement, data was manually captured by timekeepers who used a timer to mark times as they pass a fence post or some other landmark. Now, they use sensors placed under the rider’s seat that captures GPS and speed each second.
Getting that signal out is a challenge.
“Cellular communications are worse in remote France than remote Australia,” Gray quipped.
That challenge has been overcome by using helicopters to capture the signal and transmit it to what Gray called a “big data truck”.
When Dimension Data started their involvement in 2015, they focussed on getting live data from the bikes to the TV commentators. The focus was on simple data. But a major crash during that race proved to be a boon. A screen capture of the data that was coming in from the bikes involved in the crash became a point of significant engagement.
An image was tweeted from a Twitter handle that didn’t exist two days before the incident resulted in over 30,000 social media engagements.
Dimension Data ultimately engaged a journalist to provide commentary based on the data. That data was also able to deliver insights such as how tactics influenced race results rather than just raw speed.
Over time, external data sources, such as weather, gradient and other data helped to provide more context around the data. A new hashtag, #TDFdata, has even been adopted by people accessing the data and sharing their insights.
With two years of data, Dimension Data was able to start leveraging predictive analytics and machine as part of their platform. Using that data, and other information, Dimension Data was able to better understand the strengths and weaknesses of each rider. Often, the data was better at predicting results than so-called experts, often picking riders that we’re quite unexpected.
In-race events, such as breakaways (when riders push the pace) are a great source of discussion during races. Dimension Data was able to use telemetry from each of the riders to create a machine learning likelihood to determine the likelihood of the breakaway group being caught.
The model continually evolves as the algorithm receives more data.
Similarly, measuring athlete performance can also be used but there is no agreement on who owns all the data. For example, cycling teams won’t share power data (the pressure exerted on the pedals by a rider). However, Dimension Data came up with an “Effort Index” that used a machine learning index that combined environmental data with insights from Dimension Data’s cycling team to make predictions about other cyclists.
All of these elements have come together in a platform that includes data analytics, IoT, Hybrit IT, a digital workplace and cybersecurity.
- Start with what you can easily access
- Get the communications right
- Build the platform as you go – there’s no need to do everything on day one
- Make the data accessible
- It’s about insights, not just data