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Road to the NBA Playoffs: Data Points Teams Should Monitor

In the NBA, it’s not just a marketing cliche: the playoffs are a new season. Efforts are redoubled, coaches get more time to scout an opponent, and the best players are expected to hit even higher heights than they reached during the regular season.



But how do we determine exactly whether all that’s happening? Some of those changes may appear to be noticeable but with so much dynamic movement happening on the floor at once – 10 players operating independently in free-flowing game play – how can NBA teams determine with absolute certainty who is delivering and who isn’t?


The Sportlight system’s LiDAR technology has the potential to do exactly that. With its fine-movement tracking and unmatched accuracy, Sportlight can assist performance evaluators in the data points that matter most as the NBA ramps up for the postseason.


Here are just a few of the data points worth identifying and evaluating as teams begin making decisions with an eye toward its “second” season:


Performance in maximal minutes. LeBron James and Steph Curry are examples of high-volume and high-leverage performers who have proven, even in their relatively advancing ages, that their productivity won’t drop off – and may even rise – in extended, late-season minutes. That’s important because NBA benches tend to shorten in the playoffs, and the top players are often asked to shoulder a heavier load at the exact time when most mortal bodies would be coming to a grinding halt. With the ability to determine which players’ movement patterns show the strongest similarities when their bodies are fresh compared to when they’re under greater physical strain, performance evaluators have the power to help NBA coaches make key rotation decisions when teams enter do-or-die mode.


Imbalances or performance drop-offs. Any NBA club worth its salt should be looking for red flags that may suggest fatigue or injury risk, so the best organizations will already be consistently monitoring players’ kinetic symmetry and output relative to established baseline levels. On the cusp of the playoffs, though, identifying any physical inconsistencies down the stretch of the regular season will be critical to optimizing player availability and performance in the playoffs. Small changes in movement can foretell bigger problems that may be avoided with rest, therapeutics or other interventions.


Shot contest in on-ball defense. The NBA already collects granular data on shot contests, but most of the metrics focus on the shooter’s percentages based on the proximity of the defender. But wouldn’t it be helpful for a coach to know which defenders are most successful at contesting those shots? Who covers the most ground before the shot release? Who is in the best position to contest when the shot goes up? This is valuable knowledge that can be picked up by LiDAR and confirm a coach’s eyeball tests.


Maximal distance created off the screen. When a player sets a pick, what the coach who designed the play is hoping to create is separation. And because defenders tend to follow the ball, that space created usually belongs to the screener. Ball handlers play a role here, too, but the best screeners in the business are those who consistently get separation from defenders to dive to the basket or pop to the perimeter for an open look. Measuring that distance created – how much? How consistently? – can better inform a coach’s lineup decisions.


Reaction time off the pass. Open shots come at a premium in the playoffs. Although the NBA features the best shooters in the world, it’s also home to the quickest, longest, smartest defenders. But defense is also about anticipation and reaction time – a separate skill set that can be measured by how quickly a defender moves off their spot after the ball is passed to an open shooter. Tracking this metric, as well as similarly nuanced positioning data (how quickly does a defender get into ideal help position off a swing pass?) can separate the best defenders from merely the biggest or most athletic ones.



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