What is Deep tech?

Deep tech has existed for many years, referring to technology being researched and further developed (R&D), solving substantial economic, environmental and industry challenges. Examples of current technologies being scoped under the deep tech term are: 

• Artificial intelligence and Machine learning

• Vision and speech algorithms 

• Language processing

• Blockchain 

• Biotech

• Big data

• Advanced and Quantum computing

What Deep tech is already making a breakthrough in Sports?

According to TechCrunch, a massive bet from investors has been placed to the deep tech in Sports solving the problems of athlete performance, media and interactions with data, eSports, Gambling (e-bets), Gaming, in-venue technology (events, ticketings, fans), data analysis, health and fitness tech (recovery, sleep, overall day performance). 

Can it replace sports expertise?

Part of my current research focuses on understanding the coach's and athlete's behaviours to understand their building environment. During discussions with top performance coaches in badminton, football, cricket, and eSports, an unexpected theme repeatedly came. Can AI replace coaching? 

This leads to the general question: Can Artificial intelligence and machine learning algorithms replace sports expertise? 

I believe the first real question is to understand the motivation behind this question. From a coach's point of view, the technology offers more and more tools to coach the athlete directly. For example, by giving the athlete the recovery readiness data for each day (via Oura, Polar, and Firstbeat wearables, for example), the athlete could make more good decisions on how to engage and change/adapt the training regimen. This could be true for many other simple coaching advice. 

However, we can argue that this is not coaching—processing and synthesising data based on decision-doesn't necessarily make this a coaching intervention. 

There are a lot of questions if this data leads to a better outcome. For most of the already existing applications, there is not enough data to back this up. 

However, this data in the hands of a coach could make an incredible difference and improve the time a coach needs to collect and synthesise data. Furthermore, the data could be gathered all the time without anyone's engagement. 

In a high-performance environment, this should mean that the coaches could focus on how to use this data and help the athletes to adapt. So they could concentrate on real coaching - a process of communication, teaching, interacting and engaging with the athletes to move them towards their dream. Technical coaching should become easier for less experienced coaches. Tactical coaching should become a game of anticipation to the level of chess, where the adaptations happen in near real-time. Sounds a lot more exciting to me! :) 

On the other side, the coach's feedback will change how the algorithms work. This means that the real difference between results will become smaller. If there is still a way to become a World champion with a massive difference in some sports, this won't be any more possible. The difference between each place should become smaller and smaller if this tech is available for everyone. 

Anticipated "side effect" is those learnings applied to the mass generation of people (all of us). If injuries are a huge problem in sports, injuries are still considered "normal" in a normal generation. Research has identified that at least 23% of all office workers are suffering from Low Back Pain (LBP) and LBP is the main cause of work-related disabilities in people under 45. Like WHAT? Who can accept this normal?

How do we fix this? Applying all science principles to everyone. For example, posture detection algorithms could be used in office spaces to identify the problem early. Or in security cameras where detection of health problems could be "seen" by algorithm a lot earlier compared to a doctor, including heart attacks where the whole movement pattern changes a few hours before the heart attack itself. Where will the tech be developed first? The sport, of course. Or military (hopefully not). 

Of course, there is an ethical and moral side to the problem. However, the time now is a bit earlier than the time this tech is widely available, and the ethical challenge is faced. 

The prediction could be that there are many reasons why deep sports tech could become a breakthrough in general health and improve sports. Coaches should be able to focus on coaching, not data more and hopefully, the data will be adequate to improve the athletic performance in every environment - from the professional sports field to the office.