Preparing for the 2016 Rio Olympic Games, the US Olympic team had a geographical problem they needed to solve. They faced huge distance and logistic challenges in organizing national training camps, yet the synchronized diving team needed a way to see which divers would be the best match for each other. The solution came in the form of wearables that tracked the jumps and machine learning software that made the matches based on the data from the wearables. This was a huge asset that allowed the jumpers to do their daily training routine from wherever they were, without having to even see each other. The results were staggering: USA winning 4 medals from synchro diving.
If you look at big trends in IOT globally from the past few years, Data collection is what really stands out. In heavy industry we see entire supply chains tapped into IOT servers. For example, we have witnessed Kone elevators talk to each other. In the health care and insurance industries, we are seeing improvements on how they are getting to know the customer better and provide services not in bulk, but tailored to individual needs.
One of the biggest and most passionate business areas that is leading the way in data collection is sports. It is a multi-billion dollar business with growth coming from the simple need of people wanting to be entertained. Many of the tech solutions seem to be focused on entertaining spectators and justly so. Who would not want to see the power of a punch on your screen in real time when we watch a heavy-weight bought? Reaction time of sprinters? Number of spins on a figure skater doing a jump? It is easy to see how the data will change the way we see and consume sports.
It’s not all about gimmicks that allow us, as spectators, to feel more connected to the action. Data is the legal doping that has turned professional sports into a playground for startups innovating on the quantified athlete.
In addition to the US Synchro diving team hitting it big in Rio, there are several other success stories to be told. For example Germany vs. Argentina Fifa World Cup finals in 2014 (yes, ancient history already). German coach Joachim Löw knew he needed an energy boost in the final minutes of the game. He had two options for the substitute. Lucky for him, he had the data to make the decision easier and he chose Mario Goetze. A few minutes later, the midfielder scored the game and world cup winning goal.
Here’s a few insights to keep in mind, if you are thinking about taking the leap into this rapidly growing movement of the quantified athlete.
1. Make the data meaningful for the user
This really is a no brainer if you want to create traction on your solution. Think about the end user. Are you designing for a high level athlete or a dashboard for coaching? If you are designing your product for a casual practitioner then the way you display the data should look very different compared to what professional coach see. In that sense, understanding the needs of the user is imperative. You do not want to create another app that tells you “great job, you went for a run”. If the only incentive for the user is a virtual trophy, chances are the wearable gets forgotten as soon as the battery dies for the first time. Instead, provide insights. Develop algorithms that learn from the user and create a personalized experience each time they use your app.
2. Don't be jealous of the data
Don’t try to do everything yourself. Concentrate on what you are best at. Hooking up to other eco systems might be your best bet at really providing the best value for the user. The human body is an incredible machine, and when it comes to athletes every metric counts. Rest, Muscle tension and preventing injuries are crucial for the athletes and coaches. Even if your solution only covers one specific need, do it well and you are sure to find partners that complete the big picture. Remember, it’s all about that 1% that makes or breaks the game.
3. Take the longer route and avoid White label tech
“Is white label more powerful? No, Quicker, easier, more seductive.”
Those wise words from Master Yoda lead us to the most important point I want to make really clear. There are a lot of semi ready solutions out there that offer something for everyone. Easy? yes. Quick? in the beginning, most definitely. At the end of the day though, it’s all about the individual needs and problems of the end user that you are trying to solve. It might take you longer to create that shiny algorithm to really learn from your training and tell you that you need to work on your explosiveness, but make no mistake, you are creating real and undeniable value for the user and THAT is your biggest selling point. Hardware is affordable and easy to get your hands on, so get out there and experiment.
You might not even know all the ways you can use data yet, but the point is to start and learn. It is no coincidence that optimising athlete’s rest and preventing injuries with the help of data is now the hottest thing in sports.
As in sports there are no shortcuts to success here but the fact is if you are not already getting data, you are most definitely missing out.
The rise of the quantified athlete is upon us...