Using AI to help NFL teams prevent player injuries

By Sparta Science

April 25, 2019

Sparta Science is using artificial intelligence to help NFL teams with a completely different problem: preventing player injuries.

That’s a competitive space to be in, given that teams lose a significant amount of money to injuries.

According to research from ProFootballLogic.com, a website that tracks and analyzes sports data, players in the NFL have a 4.1 percent chance of being injured during a game.

Injury rates are consistent across positions, for the most part, with the exception of running backs. They face a higher injury rate than others.

Altogether, a player can expect to hit the field 14.2 out of the 16 games each season.

Find a full episode here, or Transcript below: 

WAGNER: I was an athlete in high school, in college, and was injured in quite a few different areas. I was pretty frustrated by the level of guesswork that went on to address some of these injuries, so I really went into coaching and figured, you know what? I’m going to help others avoid this, only to find that other organizations both in the U.S. and where else I worked, Australia and New Zealand, had the same kind of guess-work approach. So I figured, okay, well, I’ve got to learn how people approach death and disease and medicine to really learn the best route to figure out some of these problems.

LINDER: So Wagner attended medical school at the Keck School of Medicine at the University of Southern California.

He quickly realized that one of sports medicine’s faults is what he refers to as the “clipboard approach” to analyzing player health.

Imagine trainers with clipboards standing around players lifting weights and on machines.

A trainer or coach may look at where your hips are compared to your knees when you squat, looking for trouble signs. Then they’ll rate the players’ strengths and weaknesses, quite literally, on a clipboard.

WAGNER: People will write down 1-3 on a clipboard, and if they’re really scientific, they will add up the 1 through 3s on the clipboard to make it even more shiny and "scientific." Then, they might divide it to find some sort of average. So yeah, there’s a lot of confusion on what objectivity means and I think with technology, it’s actually made that confusion harder because, you know, if it’s on an app, then the assumption is that it’s even more scientific, which isn’t the case, it just means it’s digital. And “digital” and “science” are not the same thing.

LINDER: So, just because your Fitbit records your steps and how many flights of stairs you’ve climbed, doesn’t mean there’s any science behind it. It’s still just a pedometer.

Wagner says the clipboard approach leads to subjective screening, meaning that it’s impossible for a trainer to simply look at a player and identify their risk of injury.

WAGNER: People are eyeballing it or grading it with a number or saying, “Yeah, you know, your knee collapses when you do this movement.” But what does that mean? Because everybody is so different with the length of their body segments, the attachments of their muscles, their injury history, their ethnicity, their age, the activity they’re doing, that with all those factors, it’s not so easy as eyeballing it and giving it a subjective number.

LINDER: That’s not necessarily a make-or-break for a team, but it does mean that it’s sort of a guessing game when it comes to predicting injuries.

That’s even more difficult when you consider that players, consciously or not, do try to fudge the system. They can overcompensate in practices or while being evaluated by the medical staff to ensure they’re put on the field.

WAGNER: The athletes themselves are always looking for a way to beat the test. And so then our software gets challenged because individuals are going to try to move differently, in a way where they’re gonna, you know, try to compensate in certain directions.

LINDER: In the long run, that’s even more damaging to the athlete. So Sparta Science created a system that could detect these false positives.

Wagner said that in the earliest days of the company, he worked with the military. They, too, tried to trick the system.

WAGNER: The first day we took that in there for the military, they said, “Thank you for this. We’re going to break it.” That was their exact comment to me, meaning that we’re gonna do whatever it takes to beat the software. And it made the technology and the software better.

LINDER: Players interact with the system by stepping onto a force plate, which sort of resembles a bathroom scale. They jump, balance or hold a plank for 60 seconds as data points are collected.

Wagner says players aren’t told much beforehand. That’s intentional — he says the best technology is invisible.

WAGNER: The software uses a lot of machine learning to identify the most consistent parts of their movement and creates a signature saying, "OK, this is how this individual moves. And, relative to others, here’s where he moves better, here’s where he moves worse, here are the injury risks that he has for himself, or herself, and here is the best plan to address those risks."

So the athlete will interact, you know, when they’re done with that jump or that balance, they’ll interact with the data and say, "OK, here’s where I’m at and that makes sense because I’ve been doing this or that, and I need to change my plan accordingly because my body is now different than it was last week."

Because I think one of the challenges is that when people do assessments, sometimes they assume that there’s changes going on, you know, once a year, twice a year, but the reality is, you change everyday. And, you know, how do you constantly hone your routine to make sure you’re always operating at your best?

LINDER: The easiest injuries to prevent are the ones that occur most in a given position, Wagner said. In baseball, it’s about preventing Tommy John injuries in the elbow. With NFL linemen, it’s foot injuries. With wide receivers, it’s about preventing hamstring injuries.

Wagner said the underlying software relies on both artificial intelligence and predictive analytics to make these kinds of assumptions. The two are slightly different.

WAGNER: The difference, I guess from a high level, is that predictive analytics can be done without AI — you export a very large data set and you send it to another individual or have an individual evaluate it in that spreadsheet form to find the relationship and you’re able to be predictive based on that relatively manual process, whereas AI is much more live and dynamic in its insights that it provides, so it’s happening on a daily, or in some cases more frequently, basis. Some of what we’re doing now is still manual, particularly the aspects that we’re relatively unsure of, but the majority of it is automated within the technology.

LINDER: So what is Sparta Science still unsure about?

For one, guesses about far-fetched circumstances like, what happens if you look at different ethnicities?

Wagner wants to be sure his company only puts out information that’s been validated.

Otherwise, the system will identify correlations that actually have nothing to do with athletic performance. And through machine learning, repeat them.

Until Sparta Science can avoid those pitfalls, these outlying situations aren’t automated. Yet.

There’s a ton of other situations to test, though.

The company has already recorded over 900,000 force trials through its partnerships with the military and professional sports teams.

Right now, Sparta Science is working with the Pittsburgh Steelers, the San Francisco 49ers, the Washington Redskins and the Detroit Lions.

Wagner said that altogether, there’s data on at least 25,000 individuals.

Just having a lot of data isn’t enough, though. Similarly to Cognistx, Sparta Science works best when teams remain relatively unchanged. The more player turnover, the harder it is to make predictions.

WAGNER: People don’t recognize that enough, where it’s really difficult to build a database and an evidence-based approach if you have a new coaching staff every two years.

LINDER: Relative to other teams in the AFC North, the Steelers have low turnover in both players and coaches. That’s key to fully benefiting from the Sparta Science system.

Ideally, Wagner said, just about everyone on the team is looking at the data collected. Over time, coaches and trainers will get more familiar with each individual.

WAGNER: Mike Tomlin sees the data, sports med sees the data, scouts see the data, strength conditioning sees the data, with the idea that at the most simplistic level, at a high level, it should be simple enough for everybody to understand — the player, the coach, the doc, the trainer, etc. And there’s layers, right? And certain individuals are going to want to dive in deeper. Tomlin may not be overly concerned about the average rate of force and that really deep layer, he just needs to know how an individual ranks relative to his peers. Yet the medical level is going to want to know the trend of some of those more deeper insights beyond just that initial layer.

LINDER: But if you want to know if Ben Roethlisberger is a better quarterback than, say, Patrick Mahomes of the Kansas City Chiefs, you’re not going to get that from Sparta’s data.

WAGNER: Everyone wants to know who’s the best athlete. And my response is, what’s the best painting? Everybody does it in a different way, in a unique way, and so even if it’s the same position, because there’s running quarterbacks, and then there’s pocket-passers. So how do we basically limit the weaknesses and celebrate the strengths of every individual?

The goal is not to make Ben this amazing running quarterback. It’s to really maximize his rotational abilities as a pocket-passer and make sure that his weaknesses are limited to allow him to do that.

LINDER: But it is possible to measure the financial savings.

Wagner said that college teams using the Sparta Science system have seen their health insurance premiums drop by hundreds of thousands of dollars. Less injuries, lower premiums.

There are still so few teams working in this space, though. Wagner believes there are only one or two other companies using AI to predict and prevent player injury.

WAGNER: The majority of AI that’s being done in sports is based around fan engagement and ticket sales and pricing.

LINDER: Over at Cognistx, Whitmore and Chopra also said that they only have maybe one or two competitors at the moment.

These companies are drastically different, but both rely on a culture shift in order to see success in the sports technology market.

What they really need is trust.

WAGNER: These processes don’t happen by themselves. You’ve got to get a player to actually assess themselves and sign off and agree to the data being collected. And so, the environment is so intense and stressful that it’s almost prohibitive for a lot of companies to gather enough consistent data to have a way to implement any AI and machine learning.

LINDER: He says it’s less about the players, though, and more about the organizations.

WAGNER: It’s the organizations that really have to educate and assure the player that, look, we’re not going to use this data to cut you or make bad decisions. We’re trying to help you extend your career. I think players tend to be fearful. Like if you rush for 1,500 yards, the reality is, if you’re injury free, no one cares about your force profile because you just ran for 1,500 yards in the season.”

LINDER: The tech industry as a whole has had a reckoning with privacy concerns.

The last thing a player wants is private information about their practice performance or their injury risk to leak out.

And with data breaches on the rise, it’s clear that hackers have incentive to try.

Last November, Marriott hotels had up to 500 million accounts breached.

Question-and-answer website Quora reported up to 100 million of its 300 million users may have been impacted by a December data breach.

Facebook came under fire in March 2018 when 87 million accounts were breached.

For his part, Wagner thinks this is an individual concern, whether you’re an NFL player or not.

WAGNER: I think the NFL is pretty renowned for paranoia. There is some concerns about, I think, security, and I think the only really vetted concerns are those that revolve around health care and HIPAA and probably some of the privacy issues our neighbors Facebook have brought to light. So I think there’s a general individual privacy concern, irrespective of necessarily sports or the NFL. I think there’s a legitimate concern there about who owns the data and who has access to it.

LINDER: So all of that being considered, when might we expect to see every NFL team using technology like Sparta Science?

The short answer is we don’t know.

The long answer? When we see a culture shift within teams.

WAGNER: I don’t think people talk about the human element enough in technology and that’s the real limiting factor when we talk about timelines. Because there’s not really a lot of technology in sports medicine in the muscloskeletal space, whenever you introduce new technology in any sector, the people that have never been measured before are not really excited to start being measured … the Steelers staff is always welcoming of it, they see it as a chance to learn and better the craft, but that’s not the norm. So I think that the limiting factor will be the top-down is going to have to explain to sports medicine, strength and conditioning that, look, you’re going to get on the same page as the rest of us. It may not be wins and losses, but you’re getting measured now. Or it’s going to take those individual practitioners to get comfortable that there’s now metrics where their outcomes can be seen.

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