“Based on the data that’s collected, it tells me how I’m moving compared to previously and how I’m moving compared to my ideal movement signature, as they call it,” Mr. Ross said. Sparta Science then tailors his workouts to move him closer to that ideal.

The Pittsburgh Steelers, the Detroit Lions and the Washington Redskins, among others, use the system regularly, Dr. Wagner said. Sparta Science is also used to evaluate college players in the National Football League’s annual scouting combine.

Of course, it is inevitable that machine learning’s predictive power would be applied to another lucrative end of the sports industry: betting. Sportlogiq, a Montreal-based firm, has a system that primarily relies on broadcast feeds to analyze players and teams in hockey, soccer, football and lacrosse.

Mehrsan Javan, the company’s chief technology officer and one of its co-founders, said the majority of National Hockey League teams, including the last four Stanley Cup champions, used Sportlogiq’s system to evaluate players.

Josh Flynn, assistant general manager for the Columbus Blue Jackets, Ohio’s professional hockey franchise, said the team used Sportlogiq to analyze players and strategy. “We can dive levels deeper into questions we have about the game than we did before,” Mr. Flynn said.

But Sportlogiq also sells analytic data to bookmakers in the United States, helping them set odds on bets, and hopes to sell information to individual bettors soon. Mr. Javan is looking to hire a vice president of betting.

They key to all of this sports-focused technology is data.

“Algorithms come and go, but data is forever,” Mr. Alger is fond of saying. Computer vision systems have to be told what to look for, whether it be tumors in an X-ray or bicycles on the road. In Seattle Sports Sciences’ case, the computers must be trained to recognize the ball in various lighting conditions as well as understand which plane of the foot is striking the ball.