Many of the use cases for data analytics in sports have focused on improving the fan experience. Twenty years ago, it was the yellow first-down line for American football broadcasts. More recently, it was a graphic during an Australian rugby league broadcast showing how a player about to attempt a game-winning kick was able to quickly lower his heart rate.
“The experience for the fan, the narrative, was that much more engaging because of the data,” Adir Shiffman, executive chairman of performance technology vendor Catapult, said on a panel at last week’s MIT Sloan Sports Analytics Conference.
Next up: Applying data to improve the player experience — to find the right players, to help them achieve the best results, and to help them recover from on-field injuries and the off-field challenges of mood, nutrition, sleep, and more.
Here’s how conference speakers see this unfolding.
Fine-tuning the NFL combine. C.J. Anderson rushed for more than 4,000 yards in high school and attended the University of California Berkeley in the Pac-12 but went undrafted after graduation. The Denver Broncos signed him as an undrafted free agent in 2013; in the years since, he has won a Super Bowl, made the Pro Bowl, and rushed for more than 1,000 yards in a season.
Meanwhile, Anderson’s former teammate in Denver, Montee Ball, set numerous University of Wisconsin, Big Ten, and NCAA rushing records. Ball was drafted in the second round in 2013 but was waived two years later and is no longer playing football.
Anderson said the NFL combine — essentially a spring job fair for players who have declared for the NFL draft — could use virtual reality technology to evaluate how college players would react to the stronger and faster players in the NFL.
“Our coaches and GMs and owners can make better decisions about who to pick and who not to pick,” Anderson said. “VR can give game-like experiences to those college kids. Our college games don’t transfer to the NFL these days. If a guy is going to get drafted as a left tackle, soon he’s going to run into [Broncos linebacker] Von Miller,” who has made the Pro Bowl six times.
Pitching to a hologram? Chris Capuano made his Major League Baseball debut in 2003 and retired in 2016. The former left-handed pitcher will begin as an MIT Sloan Fellow in June.
Capuano said that by the end of his career analytics had “revolutionized” the way pitchers prepare for games. “We can analyze millions of data samples, see [a hitter’s] hot and cold zones, and decide where to throw pitches,” he said.
The next advance, Capuano said, would be incorporating virtual reality or even hologram technology into training. To avoid wear and tear, pitchers limit their repetitions in between the games they start. In these side sessions, pitchers focus on the batting lineup they will face in their next start. Coaches typically stand in for opposing hitters — but virtual reality could make this experience more real, Capuano said.
“Could a hologram have someone stand in the box so that, by the time I get in the game, I’ve already pitched to these guys?” Capuano asked.
A better understanding of when injuries could happen. Capuano’s pitching side sessions, and Anderson’s running reps at practice, take their toll on athletes. Anderson tore his meniscus in 2016. Capuano underwent two Tommy John surgeries to repair ligaments in his left elbow and, especially after turning 30, suffered numerous nagging injuries.
“I knew my body was tight. I knew I was close to being injured if I went too far,” Capuano said.
Added Anderson: “I had nagging injuries in my right knee. I was told, ‘It will eventually go away.’ I’d like the data to understand when.”
Advances in wearable technology help athletes and coaches monitor performance and alter training routines accordingly. Beyond wearables that track heart rate and sleep, there are patches to measure an athlete’s hydration level, Capuano noted, or compression gear to measure which muscles are working hardest.
Shiffman sees an opportunity to integrate data from these devices with self-reported athlete data on metrics that are harder to measure.
“How much of this data is being driven by … general mood?” he said. “State of mind and mood have a modulating impact on performance.”
Seeing the big picture of player health. In a separate session at the conference, Fergus Connolly, director of performance and operations for the University of Michigan, emphasized the importance of helping players optimize the off-field factors that impact on-field performance.
Connolly said the greatest strength of sports science and analytics has also been its greatest weakness. It’s easy to measure physical attributes, but it’s all too often done in isolation, not in the context of overall health.
“Players who make it to elite level, they’re already physically capable,” he said. “For every two hours of practice, there are 22 hours away from you. The person comes before the athlete.”
As teams assemble staff to address factors such as strength training, nutrition, and psychology, it’s important to build a holistic communications model, said Connolly, who described this model in his book, “Game Changer: The Art of Sports Science.” This way, staff use the same terms to describe problems and identify the best pathways for addressing the problem.
“If you collect data without a model, a moment, there’s no context,” he said. “If there is not one holistic model, then we’re all speaking different languages.”