In “Moneyball,” the 2003 book authored by Michael Lewis and its 2011 film adaptation, a central tension drives the narrative – and has been dominating much of the discussion around sports ever since: How important are advanced metrics in predicting future player performance?
In a word: very. As it turns out, old-school scouts, with their eyeball tests and grizzled conventional wisdom, tend to be less reliable than information technology and reams of data. However, determining which of that data is valuable and translating it into actionable insights is another story. An enlightened talent evaluator would likely say that the best scouts are those who are most proficient at aggregating the visual data they take in and store over time. Essentially, the best way to analyze sports may be a combination of the two seemingly opposed disciplines.
That brings us to LIDAR, a cutting-edge technology that is relatively new on the scene in sports. LIDAR, which stands for “light detection and ranging,” is a laser-based remote sensing method best known for its by the military in upgrading security at forward operating bases. But with the capability to track dynamic player positions and movement for the purpose of identifying useful trends and forming new insights in sports, LIDAR appears to have untapped potential as a game breaker in the field of athletic performance evaluation and prediction.
It has already been in use for some time as part of our games and competitions. Fujita Laboratories incorporated LIDAR into its technologies for tracking the movements of gymnastics competitors in an effort to assist in judging degree of difficulty scores. International soccer and Major League Baseball have begun to use LIDAR to track player and ball movement and positioning, and the NBA has used similar tech to build out metrics that influence everything from Xs-and-Os strategy to load management and player heath.
Rather than relying on the naked eye, foggy memories and inherent biases of humans, LIDAR and similar technologies produce fine data points that are unassailable in their accuracy. Fully automatic time (FAT), for instance, is more reliable than a coach with a stopwatch. We already know that speed translates to the playing field, and a more trustworthy read of that measurement makes a player evaluation department just a tiny bit smarter for it. But at the most elite levels of professional sports, the simple “metric” of speed doesn’t provide enough insight into performance. LiDAR allows teams to delve much deeper into movement, gaining insight regarding acceleration, curved runs, direction and turns.
What will be interesting is learning all the ways these sensory technologies translate to new insights. Which measurements are most important? How do they translate to player performance? Can they be applied strategically on the playing field or, as in the case of “Moneyball,” in the front office?
In fact, don’t be surprised if more organizations begin employing “directors of sensory data” or “spatial-movement coaches” who are trained to optimize data from LIDAR and other sources, mining these new nuggets of information to maximize the shrinking margins in sports analysis. If we’re able to solve for the processing of the data, the ways in which sports organizations can benefit from these new technologies are virtually unlimited.
Sports analytics is long past its arrival. Organizations around the world rely heavily on advanced metrics to make personal decisions, determine where tactical advantages lie and what weaknesses teams must address. But the most forward-thinking sports franchises are now reevaluating the ways in which that data is measured and collected. Soon they’ll have an advantage.
Those ignoring this trend? They’ll be left at the bottom of the table.
Comments