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The NBA and Big Data

American sports franchises are working hard to navigate the still very new waters of performance analytics in professional sports. The practice of tracking and monitoring the performance of elite athletes is nothing new. Indeed, sports performance statistics is what built the sports trading card industry, which along with other sports collectibles and autographed sports memorabilia generates about $370 billion in revenue annually.

But moving from documenting per-shot, per-game, or per-season stats to having players wear devices that can calculate and analyze a joint torque during practice to further personalize his off-season training program is where many players and trainers seem to be having the most trouble.

We asked Shawn Windle, Director of Performance for the Indiana Pacers, and Bill Burgos, Head Strength and Conditioning Coach for the Orlando Magic to chime in on how the NBA is leveraging sports data to build better teams.

The Challenge Shifting the Status Quo in NBA Coaching

The NBA has shaped and cultivated some of greatest basketball players of all time. The league is also responsible for building and introducing the world to some of the most influential athletes in history.

In 1985, Gen X teens and tweens and their parents were introduced to the original Air Jordan sneakers. The shoes were designed for Nike to give to Michael Jordan by Peter Moore back in 1984. The new shoes were released for public sale a year later. Today, Millennials (now between 21 and 38 years old), Gen Zers (between 8 and 20 years old), and Generation Alpha (7 and under) have their pick of more than two dozen different styles of the iconic sports shoe.

Understandably, sports tech firms and data analysts have their work cut out for them when it comes to trying to tell world-renowned NBA coaches how numbers and machines can help them do their jobs better.  One of the primary goals, Windle says, is getting everyone working collaboratively as part of a group effort instead of creating departmental silos.

That’s important because as much as he believes coaches and trainers can benefit from having a better understanding of Sports Science and Data Analytics, Windle also believes the technology can’t stand alone and put forth training recommendations based solely on numbers.  Numbers alone don’t provide a complete picture of what’s going on with an athlete. “The athlete has a bad performance,” says Windle. “I go back and look at all of his data, but I don’t see why he shot the ball poorly.

There are so many variables that go into performance that can’t be analyzed from the data that I receive.  Maybe the reason a player played poorly was because we were in a city where he had friends and decided to stay out late so his lack of sleep caused him to be a half a step slower to react throughout the game.  You just don’t know.  We like to think they’re all robots living in a bubble and looking at numbers trying to explain their performance but we don’t have the whole story.”  

Educating the Higher-Ups, Onboarding the Players

To complicate matters further, because Sports Science is still very new to the NBA, that means not only is there a learning curve as far as onboarding players and coaches, there’s also the need to educate stakeholders and front office people who can’t quantify the value of Sports Science because they can’t yet distinguish it from data analytics, and collecting data.

At least in the NBA, putting players in wearable devices to collect data during practices isn’t very widespread. There are just a few teams in the NBA currently doing it. The Indiana Pacers is one such team.  But getting the players comfortable wearing performance monitoring technology isn’t easy. Many of them haven’t yet fully grasped the value of having big data on their side.

Windle adds, “One of the challenges is teaching a young guy that’s got buy-in with skill and athleticism that this is important for career longevity, injury prevention, consistency of play night-in / night-out, developing a routine, developing a mind-set… All of these things outside of physical strength and conditioning.”

Technology nowadays is allowing the sports practitioner to see things we’ve never seen before, such as accelerative and deceleration loads, and how it compares to its physiological load, and the amount of force associated with it.

Bill Burgos

Sports Science versus Data Analytics

At a very basic level, most people do have some understanding of sports science, the science of how the body works during exercise and competitive sports. For the unschooled, sports science shows up in the form of common-sense advice like eat well, get eight hours of sleep, train consistently, keep your mental focus in the days before a big game.

But more sophisticated insights can be a little harder to grasp. “Technology nowadays is allowing the sports practitioner to see things we’ve never seen before, such as accelerative and deceleration loads, and how it compares to its physiological load, and the amount of force associated with it,” says Burgos.

No longer is a hamstring pull a hamstring pull.  Predictive technologies can now detect even a 1.5% change in an athlete’s performance. Movement analysis technology like the DARI system from Scientific Analytics, Inc. can assess physical weaknesses, asymmetries, and dysfunctions weeks before they happen, giving coaches and trainers ample time to devise training programs to prevent imminent injuries, even before pain begins.

These physiological changes trickle through sports tech as numerical data points which are then analyzed by data analysts and given to sports scientists like Shawn Windle and conditioning coaches like Bill Burgos to use as insights to develop action sets to strengthen and/or rehabilitate elite athletes.

Most sports organizations – from amateur, through collegiate sports, up to professional sports – can find ways to generate far more data than they can meaningfully interpret and apply. As it turns out, generating the data is not the issue. Finding analysts to interpret the data and knowledgeable sports strength and conditioning professionals to develop personalized training programs around the data is where the primary problem lies for most organizations.

“We cannot keep adding data points! It slows down our efficiency, and you lose a feel for the athletes; we lose our eyes,” Windle says. “As a Strength Coach if you’re constantly looking at just a GymAware and looking at velocity profiles you have pretty good idea of how the athlete is responding to your program or future direction of training but you’re missing so much – relationship building with that athlete or the psychology behind their performance.”

The Future of Sports Science in the NBA

As more professional American sport teams adopt the use of wearable technology to enhance performance and increase player availability, Windle suspects most teams will have in-house data analysts whose job is to generate, organize, and objectively interpret performance data for individual athletes and teams. But that model is years away yet.

So, how will early adopters like Indiana’s Shawn Windle position the Pacers and other NBA teams to leverage the power of Sports Science? Gradually.

Burgos believes emerging VR technology will make it possible for athletes to enhance their pattern of recognition without the wear and tear on their bodies. And artificial intelligence will help with predictive analysis and enable coaches, trainers and practitioners to do their jobs more efficiently and effectively.

Windle thinks coaching education is slated to be the next big thing. Some NBA coaches are starting to see the impact Sports Science and data can have on player performance, but there needs to be a greater understanding and acceptance of the discipline of Sports Science within the coaching community before it will become more widely used in the NBA.

Bill Burgos agrees. “With all the data being collected today, and the best minds trying to make sense of it will make things easier for the sports practitioner and allow Coaches to coach more effectively.”

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