The 2016 Asia-Pacific Sports Analytics Conference took place recently at the NAB Village. Its only the second time this conference is held and I have to say it has done really well. The numbers prove it – 865 attendees (according to the Whova app), 33 sessions that ran concurrently in 3 different rooms, 45 Speakers (all experts in their fields) representing 57 organisations, and 12 startups that pitched their innovative ideas/products/services. There was even a waitlist 2 weeks before the event. This goes to show the
growing booming popularity of data analytics, and the potential impact it could have on the different aspects of sports.
Unfortunately, as with any great conference where there are sessions running at the same time, people would be torn between 2 (or possibly 3) presentations they are keen to attend. Fortunately, from what I heard, videos of all the sessions will be uploaded in a few weeks and we will be able to catch up with every single one that we missed. Just keep a lookout on the conference website here. In the meantime, here are some of my takeaways from the few sessions I managed to attend.
Professor Tino Fuss presented some of the research and development that was going on at RMIT including a smart cricket ball, a smart soccer boot and smart compression garment. With the advancement of inertia sensing microtechnology and novel pressure sensing technology, sensors can be placed unobtrusively on the athlete and equipment, measuring a range of parameters at much higher magnitudes. No doubt that the sensor data that’s acquired has to be analysed to solve a problem or confirm a hypothesis. That’s where analytics play an important role. But applying the appropriate sensor technology does open up opportunities to analyse new parameters like the sweet-spot on a soccer boot that increases the chance of a goal.
Wearable tech for rehab:
Shireen Mansoori is a doctor in physical therapy who applies wearable technology in her practice with elite athletes. She presented a model where she combined physiotherapy and data analytics for athlete optimisation. She uses Catapult units for monitoring an athlete’s Player Load & Hi Deceleration efforts to find trends that lead to injury. But she also uses other wearable tracking devices such as the Misfit shine on the athletes, health/wellness monitoring apps, and an athlete sleep screening questionnaire to monitor an athlete’s sleep and daily activities. Having other forms of data paints a much clearer picture of what an athlete is going through, and allows her to find out why the athlete is recovering faster or performing below expectations.
Video analysis & Artificial intelligence:
In cases where it is still obtrusive to place sensors on athletes (for example in swimming competitions); or where wearable sensors can’t provide specific activity/events information (for example attack, pass or steal events in hockey), sports analysts turn to video analysis/coding. However, much of the video analysis work involves a sports scientist (or two) manually tagging/coding every event during the competition. Stuart Morgan, sports analyst at AIS, talked about developing computer vision algorithms to detect patterns and features and somehow automate the tagging. But this approach (human engineered method) has lots of limitations including it being non-transferrable and not very adaptable (for use in different sports). So AIS is collaborating with researchers at La Trobe Uni to apply deep learning (using Convolutional Neural Networks) to process the video images and work out whats happening. The advantage of deep learning is that it’s adaptable and it automatically creates new features. It still has some way to go as it’s not error free and users can’t really tell what logic led to the decisions.
From elite to grassroots:
Most of the stuff mentioned above happens in the professional/elite athlete space. However there is also an increased trend of sports tech/analytics companies developing products for athletes and coaches who participate in their local leagues. Hudl‘s video analysis software was first developed for professional teams. But today, their software caters to high school teams and their requirements. They have developed mobile apps that allows video recording and editing directly from the coaches’ mobile device, and there’s even a platform for sharing videos and facilitating talent identification.
Athlete tracking wearables have also moved in the same direction. Startup companies like Essential GPS and Sports Performance Tracking have developed more affordable tracking solutions so that teams with lower budgets can also track and monitor their players. Although it seems to be purely GPS data (without motion data), and only post game/training analysis (not real-time), it is still a good start. Or maybe a simplified, cost reduced system is all that is required?
From the startup community:
So there were 12 startups showcased in the conference. Other than the 2 mentioned above, there were 4 other startups that have built hardware in areas of performance tracking, drone racing, rehabilitation, and custom protective gear. The others were mainly software based, providing services and platforms in media, news, sales, marketing, VR and team management. They have all developed solutions hoping to fill a gap identified in the sports industry. Personally I am just amazed at some of the novelty and innovation they have come up with; and as this blog post says it, they are all innovators.
I think what sums up this conference for me is that sports analytics is all about adapting and innovating. Everyone in their own ways are trying to fix a problem (or come up with a better solution) or improve work flow or even create new opportunities (e.g. esports and fantasy league). But the process is never a straight line from point A to B. The solutions need to be adapting over and over (almost like deep learning). Sometimes there needs to be collaborations and sometimes the end solution needs to be a combination of solutions. Whichever the case, iterate the process as quickly as possible till an optimum outcome is reached.
— Julian Chua (@reengineerlabs) July 22, 2016
The”one-size-fits-all” solution doesn’t work very well anymore and mass customisation is becoming the norm. As mentioned by John Eren MP and Laura Anderson during their welcome addresses, we are slowly moving away from economies of scale and towards economies of scope.