A Blog by Jonathan Low


Apr 17, 2020

Game Changer: The Ways Machine Learning Is Changing Sports

It is providing better data on a player's actual skills and value.

And as working with coaches and teammates may become more difficult, algorithmic training and evaluation may become even more important. JL

Craig Smith reports in the New York Times:

The pattern-recognizing power of machine learning (is) revolutionizing coaching and make advanced analytics available to teams of all kinds. The systems evaluate a player’s skill and consistency. The trend is touching professional sports and changing sports medicine. Professional teams derive a growing slice of revenue from selling players. But without objective measurements of a player’s ability, putting a value on an athlete is difficult. And, it has altered the odds in sports betting. With algorithms and cloud computing power now available everywhere, the differentiator is data.
A couple of decades ago, Jeff Alger, then a senior manager at Microsoft, was coaching state-level soccer teams and realized that there was very little science to player development.
“There were no objective ways of measuring how good players are,” said Mr. Alger, “and without being able to measure, you have nothing.”
He said it offended his sense of systems design to recognize a problem but do nothing about it, so he quit his job, got a master’s degree in sports management and started a company that would use artificial intelligence to assess athletic talent and training.
His company, Seattle Sports Sciences, is one of a handful using the pattern-recognizing power of machine learning to revolutionize coaching and make advanced analytics available to teams of all kinds.
The trend is touching professional sports and changing sports medicine. And, perhaps inevitably, it has altered the odds in sports betting.
John Milton, the architect of Seattle Sports Sciences’ artificial intelligence system, spent a week in October with the Spanish soccer team Málaga, which plays in Spain’s second division, capturing everything that happened on the pitch with about 20 synchronized cameras in 4K ultra high-definition video.
“It’s like omniscience,” Mr. Milton said. The system, ISOTechne, evaluates a player’s skill and consistency and who is passing or receiving with what frequency, as well as the structure of the team’s defense. It even tracks the axis of spin and rate of rotation of the ball.
That is not the only way that the company’s technology is being used. Professional soccer teams derive a growing slice of revenue from selling players. Soccer academies have become profit centers for many teams as they develop talented players and then sell them to other teams. It is now a $7 billion business. But without objective measurements of a player’s ability, putting a value on an athlete is difficult.
“It’s a matter of whether that player’s movements and what they do with the ball correspond to the demands that they will have on your particular team,” said Mr. Alger, now the president and chief executive of Seattle Sports Sciences. He said, for example, that his company could identify a player who was less skilled at other phases of the game but was better at delivering the ball on a corner kick or a free kick — a skill that a coach could be looking for.
Some systems can also detect and predict injuries. Dr. Phil Wagner, chief executive and founder of Sparta Science, works from a warehouse in Silicon Valley that has a running track and is scattered with equipment for assessing athletes’ physical condition.
The company uses machine learning to gather data from electronic plates on the ground that measure force and balance. The system gathers 3,000 data points a second and a test — jumping or balancing — takes about 20 seconds.
“Athletes don’t recognize that there’s an injury coming or there’s an injury that exists,” Dr. Wagner said, adding that the system has a proven record of diagnosing or predicting injury. “We’re identifying risk and then providing the best recommendation to reduce that risk.
Tyson Ross, a pitcher competing for a roster spot with the San Francisco Giants, has been using Sparta Science’s system since he was drafted in 2008. He visits the company’s facilities roughly every other week during the off-season to do vertical jumps, sway tests, a single leg balance test and a one-arm plank on the plate, blindfolded.
“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.
To do that, teams of workers first have to painstakingly annotate millions of images. The more annotated data, the more accurate the machine-learning analysis will be. “Basically, whoever has the most labeled data wins,” said Mr. Milton, the A.I. architect.
Seattle Sports Sciences uses Labelbox, a training data platform that allows Mr. Milton’s data science team in Seattle to work with shifts of workers in India who label data 24 hours a day. “That’s how fast you have to move to compete in modern vision A.I.,” Mr. Milton said. “It’s basically a labeling arms race.”
Dr. Wagner of Sparta Science agrees, noting that with algorithms readily available and cloud computing power now available everywhere, the differentiator is data. He said it took Sparta Science 10 years to build up enough data to train its machine-learning system adequately.
Sam Robertson, who runs the sports performance and business program at Victoria University in Melbourne, Australia, said it would take time for the technology to transform sports. “The decision-making component of this right now is still almost exclusively done by humans,” he said.
“We need to work on the quality of the inputs,” he said, meaning the labeled data. “That’s what’s going to improve things.”



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