Green Bay Packers cornerback Damarious Randall leaves the field after an apparent injury against the Steelers on Sunday. (Charles Leclaire/USA Today Sports)

In locker rooms across the country, amid the pads, sticks, balls and helmets, sports teams are increasingly relying on a new piece of equipment. It’s typically about the size of a thin flip phone and is worn in the middle of an athlete’s back, usually under a compression shirt. When the biometric sensor starts whirring to life, every athletic movement, every heartbeat and every muscle twitch is converted into numbers, arming teams with more information than ever about an athlete’s performance, potential and health.

The end result is the latest offshoot of the burgeoning analytics world, where teams are leaning on technology and data to not just prevent and rehabilitate injuries but to predict them.

When “Moneyball” hit the scene a dozen years ago and analytics in sports went mainstream, both fans and teams thought of the newfangled statistics in terms of in-game strategy and roster building. Innovative and complex measurements were used to evaluate players in new and revealing ways. But the more recent analytics revolution has focused on health, injuries and keeping players on the field.

“Everybody’s trying to figure out the perfect formula, but there isn’t a perfect formula right now,” said Joe Rogowski, the NBA Players Association’s recently appointed director of sports medicine and research. “It changes every day. Every day there’s new companies coming out with new devices, measuring this and promising that."

Rogowski came into the NBA a decade ago, focusing on strength and conditioning first with the Orlando Magic and most recently with the Houston Rockets, regarded by many as one of the NBA’s leaders in their use of analytics. “When I started in the league, I think I was one of two teams that was looking at heart rate,” he said. “Now almost everybody has looked at that.”

What else do teams monitor? “If you give me an hour, I can go through the whole list,” he said. An abbreviated catalogue includes: speed, hydration, sleep, travel, hormone levels, muscle fatigue, exertion, vitamin D levels, stress.

The resulting information can by used by front offices when evaluating contracts and potential trades, and also by coaches and athletic trainers when deciding how hard to push players in practices and games. It’s not just the NBA. Sports scientists and data analysts are being used in professional baseball, the NFL, NHL and MLS and by a growing number of college teams to manage, anticipate and prevent injuries.

Many sports stadiums and arenas are equipped with motion-sensor cameras, players are wired with GPS tracking devices and many are wearing sensors on the practice field that measure every minuscule body movement. In the NFL, players this season will wear a GPS tracking chip in their shoulder pads during games, but the most detailed and useful data will come during practices, where many teams use the biometric sensors. Analysts then merge data recorded by the sensor with video to spit out reports that measure performance and pinpoint areas of distress and injury potential.

Unlike traditional game statistics, this information doesn’t assess execution on the field as much as effort and whether a player is performing at his or her peak. It’s something teams can measure on a minute-by-minute basis, tracking a player’s growth or regression.

Teams are hesitant to talk about how they’re using analytics and technology to manage injuries, fearful of sharing anything that might aid the competition. A half-dozen teams declined to comment on the record or make officials available for this story.

Spotting injury ‘flags’

Analysts in the field say the landscape was entirely different a decade ago, and the past two to three years especially have seen profound changes.

Brian Kopp is the North American president of Catapult, considered one of the industry leaders when it comes to merging technology and analytics. Kopp’s company signed its first NFL team three years ago. This season, it will work with 20 teams, most of those signing up in the past 1 1/2 years.

Catapult outfits players with a biometric device that uses several tools, including an accelerometer, gyroscope and magnetometer. In all, the data measure every movement — every turn of the shoulder and twist of the hip, every jump, sprint or even subtle lean in any direction. The information is sent in real time to a laptop, often on the sidelines, which can compute all the data into a single metric, which Catapult calls “player load” — “which is our way of saying how hard is your body going?” Kopp explained.

Coaches and athletic trainers use this information in different ways. Kopp’s company works with NBA, NFL, NHL and MLS teams and says many monitor the player load of their athletes throughout each practice. If a player’s number extends outside a certain range, athletic trainers and coaches can respond immediately: have a player take a break and hydrate, for example, or maybe push the player even harder. One NFL team that was particularly committed to the data saw its number of players on injured reserve with soft-tissue injuries drop from 16 in 2013 to just three last season, said Kopp, declining to name the team.

“If you think about it, hamstrings, groins — these are soft-tissue injuries based on overuse and overwork,” he explained. “Without this information, you don’t know what happens until it happens. Now we’re able to identify some of the flags a bit earlier.”

All of this information can be used by training staffs and coaches.

For example, the Toronto Raptors recently scrutinized data that tracked their players’ precise movements and realized that they moved forward in a straight-ahead fashion (think of a clock face: between 11 and 1 o’clock) only 15 percent of the time. The other 85 percent of the movements were sideways, diagonal or backward. That information could change a team’s conditioning habits, devaluing a traditional exercise such as wind sprints and encouraging teams to mix in others that better mimic game movements.

A variety of factors

There have even been efforts to use all of this data to peek into the future. The thinking goes, if a team can anticipate an injury, it can avoid the injury. By combining a variety of metrics and risk factors, David Tenney, the sports science and performance manager for MLS’s Seattle Sounders, feels his team has a strong understanding of probabilities. For example, he says the two biggest risk factors for injury are a previous injury and a player’s age.

“So you build those two things into your model right away,” he said. “And then you have things like, what are their work habits like out of season? That’s extremely predictive. If we know a guy has a previous muscle injury, he’s over the age of 30 and we know he wasn’t fully compliant in the offseason, we know that player is pretty much guaranteed to have an injury the next year. Most of our data shows he’s almost 100 percent going to have an injury.”

Tenney warns there are a variety of considerations — sleep, nutrition, travel, playing surface, workload — and a key is learning how these factors interact, and also how their impact varies from player to player.

In 2013, the Sounders relied on a model that attempted to take all these factors into account and peg an exact injury probability to every player. Tenney knew, for example, a high-risk player might have been 25 percent likely to suffer an injury. Others might be just 5 percent. He said the season largely played out according to the model, but the team decided to scrap the predictive numbers and now focuses more on identifying and minimizing the risk factors and red flags that it knows can lead to injuries.

“Using analytics to predict injuries, it has to come down to understanding the interactions of everything,” Tenney said. “There’s never one cause for injuries. There’s never one single metric that’s going to be predictive of an injury.”

Because the undertaking requires unique software, expensive technology and lots of man hours, many teams contract with outside companies rather than attempt to assemble and analyze all of the data in-house.

Kitman Labs is an Ireland-based company that broke into the field working primarily with rugby and soccer teams. It expanded to the United States last year and is working with the Tampa Bay Rays and Los Angeles Dodgers this season and recently signed on with the Miami Dolphins. It sells clients on the idea that its analysts can keep more players on the field from week to week.

Stephen Smith, the company’s co-founder, points to January’s AFC playoffs for an example. When the Baltimore Ravens met the New England Patriots, 19 of Baltimore’s players were on injured reserve, including four starters and three rookie draft picks.

“Surely if they had more of those players available, they would have won that game,” he said. “If they won that game, could they have won the Super Bowl? I think the answer is yes. . . . Sometimes the margins are that small where one or two players can help you win games.”

Player backlash coming?

While the early reviews for Kitman Labs have been positive, this new technology and data crunching is akin to an arms race, and Leslie Saxon, the executive director of the USC Center for Body Computing, warns that some upstart companies are overpromising what they can deliver. Much of the rapidly escalating data and technology haven’t necessarily been tested, vetted and proven, she said.

“We’re seeing a lot of haphazard science that will end up in the closet,” she said.

Saxon is also worried about how the data will be used down the road and who has access to it all. She’s in favor of new information if it helps improve performance, but not if it is used against players.

“Until leagues, teams and players get together and develop governance around this data that’s in best interest of the player, it won’t move forward,” she said. “At some point, there will be a backlash. The first guy who gets cut or who suddenly appears to be damaged goods is the first time guys will start ripping these off their bodies.”

The collective bargaining agreements between pro leagues and players’ unions largely limit teams from mandatory monitoring of medical minutiae, particularly during games. Both unions and leagues are wrestling with defining the gray area between what’s considered performance data and what is health data.

“I think everyone is paying close attention to this,” said Tara Greco, the NBA Players Association spokeswoman.

While leagues and unions search for a comfortable level of trust, teams in all major sports — including many top-tier college programs, which aren’t restricted by CBAs — will continue to look for an edge, figuring that the healthiest team each year might also be the most successful.

“Anything that can help prevent injuries, teams right now are trying to implement,” Rogowski said.