Niall Hendry is a product manager at ChyronHego, specifically focused on ChyronHego’s world leading video and data analysis tools within the professional sports market.
Having worked for ChyronHego over the last 6 years in roles including Operations and Pre-sales, his focus is to provide customers with an intuitive product set which enhances their video, data and graphics content.
What lead ChyronHego to decide that the gathering and visualization of data in live sports was an important line of business?
With the evolution of data within sports, collecting more and more data is inevitable. Hego Group, prior to the merger with Chyron Corporation, actually started tracking football matches 15 years ago, using our TRACAB optical player tracking system. The use cases vary between whether the data is relevant to broadcasters for visualization and editorial or teams for performance and analytics, but fundamentally as long as the data is available, and crucially, available live – then the possibilities are expansive.
What sets ChyronHego’s player tracking solutions apart from other systems?
The market is rapidly becoming more competitive. When developing and promoting ChyronHego’s player tracking solution, it is important that we target our key messages and USPs (Unique Selling Points) at specific audiences, as there are so many solutions on the market that claim to produce the best data in the world. Fundamentally, our key USP, and therefore our key message, is that ChyronHego provides the most accurate live sports data in the world. That is a USP which an independent third party recently proved to be true (watch the video below). To extend this advantage for our customer’s benefit even further, we can then also plug that data into our own leading visualization and coaching tools, such as Virtual Placement, PRIME Graphics, Coach Paint and Coach GO. Alternatively, we can provide the data feeds for users to do whatever they want with it.
In terms of measurement of players performance, what are the vital differences between ChyronHego’s solution and those of your competitors?
Being the most accurate live data provider is key to us.
For more than 5 years, ChyronHego’s TRACAB Generation 4 optical player tracking solution has been the leader in terms of sales and deployments. We’re planning to launch the latest version of TRACAB – Generation 5 – in 2019, and as I say we’ve had the accuracy of our data independently validated using an industry standard methodology.
Whereas TRACAB Generation 4 uses two sets of cameras on both sides of the stadium, TRACAB Generation 5 can scale to position cameras on all four sides of the stadium – where the cameras can be positioned on the opposite sides of the middle of the pitch and behind each goal. By combining these camera angles in innovative ways using the TRACAB Generation 5 software, ChyronHego is able to derive player and ball data in an extremely accurate manner, and for us that accuracy is our key differentiator.
How do sports tracking products measure the risk of potential injury to a player?
ChyronHego is making sports tracking data available to an ever wider and more diverse set of stakeholders.
Tracking systems, both in training and game situations, allow teams to build a physical profile of a player. Utilizing that data to check whether a player might be getting tired, or if they are doing a high number of explosive activities – which may lead them to getting injured, minimizes the risk of injury. By looking at the average output of a player and setting Z-scores for them, teams are able to monitor how they fare in different types of intensity of activity, and tailor their approach to a game or a training session accordingly.
Looking into the future, as it relates to ChyronHego, where do you see the development of sports technology heading?
Suffice to say, sports technology will continue to answer the questions that were previously based on hunches. I believe the number of AI-driven solutions will continue to grow, as this has become the new zeitgeist for sports technology. Fundamentally, we’ve been doing this in TRACAB to recognize and track players for years, so we’re in a good position to make our system even better using this technology. It will be interesting as we learn more about the practical application of deep learning techniques on sport, particularly for analytics. We’re only scratching the surface when it comes to the analysis of sports data, and there is undoubtedly a lot more to come in this space.