In the early 2000s, Oakland Athletics general manager Billy Beane brought “Moneyball” to Major League Baseball – and, soon after, the masses. Wheeling, dealing and cobbling together a contender based on calculations made using evidence-based sabermetrics, Beane famously led the cash-strapped A’s to a division title. Suddenly, the ball was set in motion for data-driven analytics across countless other sports.
Daryl Morey followed in Houston, rolling out “Moreyball” in the NBA and relying (as Beane did) on undervalued player attributes to build contenders that are greater than the sum of their parts. In the Premier League, Liverpool F.C. used a Cambridge physicist’s mathematical model to help optimize player and staff recruitment, setting the table for a 2018-19 UEFA Champions league title.
As data in professional sports has evolved and more organizations try to copycat the proven tactics of successful competitors, staying ahead of the curve – learning what data is truly important, and why – has become the latest differentiator. The new frontier in that quest is the development of technology that collects more data, with finer accuracy, than any system before it. That groundbreaking tech – already a fixture in the Premier League, and soon storming the courts of the NBA – is LiDAR.
LiDAR, or light detection and ranging, is a laser-based system that captures and measures player movement with unmatched accuracy and granularity. Although LiDAR allows performance evaluators to track data such as where a player moves on the court and how fast they get there, it also delves much deeper.
Just as the NBA has graduated from volume statistics (points, assists, rebounds) to efficiency metrics (field goal and free throw percentages) to contemporary models (effective field goal percentage and PER), the league is now ready for the next level of advanced statistical analysis – powered by lasers. Consider a few of the cutting-edge stats every pro basketball team may look to track via LiDAR in the future:
Pinpoint-accurate tracking of player locations on the floor at any moment – including data that measures their distance from one another at all times – provide insights that, say, a simple plus/minus statistic can’t explain. With the ability to collect and feed this information into an algorithm, coaches can identify not only who plays best with whom, but also where and in which situations.
Tracking player movement on the floor isn’t just about location. By measuring how quickly a ballhandler bursts off a screen, and at what angle, a coach has objective insights to lean on when guiding a player how to approach those situations. By tracking the explosiveness of a defender’s slide to each side, a performance evaluator might be able to detect an asymmetry that could signal an injury risk.
Probably more than any other sport, basketball players change direction. Actions on offense require players to move toward and away from the basket and laterally – in and out of the lane, around the three-point arc and around the basket. On defense, players mirror these movements, sticking with their assignments. During a change of possession, players are asked to quickly transition and head toward the opposite end of the court.
Understanding how quickly players are moving in these acute situations could also help coaching staffs better understand how they can maximize their talent – designing defenses and offensive sets that best compliment their group’s ability to move in different ways.