The long arc of performance-tracking-technology development has been driven by the same forces that make athletes, as well as the coaches, practitioners and other associated staff who work with them, want to compete in the first place: to discover improvement in the margins, to test assumptions and theories to determine best practices and to push the boundaries of what previously had been thought possible.
To say that these technologies have come a long way is a gross understatement. To give context to the leaps and bounds we’ve witnessed in the field, imagine demonstrating today’s state-of-the-art, laser-based player-tracking tech for, say, the inventors of the stopwatch. Or consider Dr. James Naismith taking a front-row seat at an NBA Dunk Contest. Brilliant minds would be blown.
But every journey starts with a few humble initial steps that set a course for progress and create the momentum necessary to break through future obstacles that lead to even greater strides. A look back through the evolution of movement tracking in sports shows not only the leaps and bounds of technological innovation but also helps put in perspective the potential for future growth in the space.
Tracking Individual Athlete Performance
Most of the earliest performance-tracking tools were adaptations of existing devices that had been around for years, and sometimes centuries. For example, the goniometer – a device used to measure joint ranges in each plane of a joint – was invented in the 1500s. But while the first goniometer was created for the study of crystals, modern practitioners now use forms of the device to measure range of motion in rehabilitation settings. Variations and advancements, such as inclinometers and goniometer-equipped smartphone software, have added new forms of measurement (lateral movement) and supercharged usability and access. Potentiometers, nearly 200 years old, were invented to control the amount of electricity sent to a device or circuit and have been applied to ceiling fans, audio equipment, computers and more. They are also now used to measure the amount of power generated by a cyclist or a runner.
Similarly, the stopwatch and stop-motion photography weren’t built for performance tracking – but they inarguably elevated the early science of training and rehabilitation optimization. Each allowed for the collection of information for the purposes of aggregation, comparison, analysis and applied problem solving – processes that had previously been executed entirely via eyeball tests, memory, guesswork and vaguely informed experimentation. As these and other technologies were improved and combined, not only did they provide the spark for such sports staples as photo finishes and video replays – which brought new drama and depth to competition and the fan experience – they also provided an early platform on which practitioners could begin to capture and learn from objective performance data.
Widening the Scope and Sharpening the Data
The process of iteration and innovation has provided sports scientists with increasingly dynamic and utilitarian devices for measuring individual athlete movement and performance – including motion capture using magnetic, optical or inertial sensors, as well as camera-based mocap systems that free up athletes from cumbersome suits and sensors. Each of these systems has varying strengths and weaknesses, from portability and price to accuracy and adaptability to an environment.
In addition to capturing individual athlete movements, the growing field of performance tracking began adapting additional technologies to capture player location and spatial positioning on the field of play. Global positioning system (GPS) and local positioning system (LPS) – radio-based navigation systems that work off satellite and local networks, respectively – have been useful in this regard.
But the performance-tracking systems that deliver the strongest results today and show the most promise for the future are designed with laser-based tracking tech and are driven by artificial intelligence. LiDAR (light detection and ranging) provides the most accurate tracking readings – and without the need for sensors, satellite networks or wearables. Meanwhile, AI and modern computer processing give practitioners the power to make sense of that trusted data and build more effective athlete-optimization and rehabilitation programs. It’s these technologies that figure to lead the way as clubs search for more reliable, useful and accessible systems to invest in toward the care of their athletes.
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