Perhaps the most important development in professional sports this century has been the industry’s embrace of data. After finally moving beyond the limitations of the stopwatch and tape measure, sports organizations and athletic performance evaluators have broadly expanded the scope of performance measurables they track—while at the same time growing their understanding of how that data can be practically applied.
That said, we are very much in the early stages of sports’ Big Data movement. The realm of professional sports represents a galaxy of undiscovered data—an ecosystem of information and numbers we haven’t yet thought to examine or, in some cases, look for at all.
Today’s executives, coaches and other performance managers often rely on the most advanced, or most popular, tools of the trade at the time they broke into the business (think GPS and camera-based solutions). They are comfortable with this tech, frequently have longstanding connections with vendors who distribute it, and are familiar with the metrics and reports these tools generate.
It’s easy for a performance manager to fall behind the times with a simple go-with-what-you-know approach. Technology doesn’t stand still: Dozens of established companies, startups and academics are pitching new ideas and breakthrough technologies at any given time. Sorting innovative measurement, tracking and analytics solutions from overhyped new products is practically a job all on its own. Many performance managers are aware their current tech isn’t 100% accurate, or sensitive enough to detect certain nuances, but they will settle for that uncertainty over the unknown of new technology.
This leads to holes in our knowledge where data should reside. Consider load management in professional soccer: Current technological solutions don’t allow for accurately measuring the power, or level of a player’s physical exertion, on a kick. Although a player’s number of kicks is tracked, their classifications go unheeded. And because many sports scientists agree that the power of kicks (especially those of the medium- and high-impact variety) is strongly associated with fatigue and injury risk, a key indicator in the data collection around load management is slipping through the cracks.
Until somewhat recently, the same could be said for tracking a player’s change-of-direction quickness and their speed of acceleration and deceleration. Although the NFL Combine’s shuttle run was designed for helping evaluators measure those abilities, it’s an old-tech solution that measures a player’s aggregate time through a pre-planned course. What serves evaluators better? Technology that offers granular detail and accurate measurements of the component parts of the shuttle run that, in theory, are what teams are hoping to record. Even more helpful are the same insights gleaned during actual game plays, where physical capacities, coupled with decision-making, separate a Tom Brady from a Steve Beuerlein.
Thankfully, that technology has arrived. LiDAR is a laser-based system that provides reliable, detailed measurements of these skills—vital information when a fraction of a second in highly specific game situations may separate an All-Pro cornerback from a practice squad player. A number of computer vision and machine learning technologies have led to incremental change in this area, but LiDAR embodies true innovation—a solution that levels up from less accurate technologies that use camera data as source material.
Several computer vision technologies with artificial intelligence offer incremental changes by estimating the athletes’ position on the field, but LiDAR doesn’t rely on guesswork and instead uses millions of laser beams to measure the players’ position accurately.
As any coach will tell you, the little things matter. Margins make champions. More accurate tracking of player starts, stops and turns can give coaches better information for game-planning and deploying personnel. A scientifically sound method for measuring stamina levels and injury risk could offer insight into the health of an aging superstar that makes or breaks a season.
Currently, we’re working with three English Premier League clubs that have integrated LiDAR technology and its outputs into their daily analyses. Another 10 clubs are undergoing pilot projects with the technology. Their main focus is on hyper-accurate physical data, working with performance, sports science and strength and conditioning departments. The work is oriented around some of those analytical “blind spots,” focusing on delivering insights into acceleration and deceleration, change of direction, turns and fatigue monitoring.
Additionally, some clubs are working on using the technology on talent development for their academies, baselining players on key metrics versus first-team players—highlighting differences and areas for improvement. Thus far several clubs are using the data to make changes to training drills and programs, exposing players in training sessions to the forces and intensity levels of games. Additionally, the data is being used to help develop an architecture for player rehab.
Advancing the cause of data in sports and replacing blind spots with transparent vision can be achieved with creative algorithms and radical new ideas, but not exclusively. Sometimes the next step just means doing the basics better. Simply tracking an established measurable, but with more accuracy, can reveal data that performance managers never knew they needed—or even thought possible.