Want to Be Better at Sports? Listen to the Machines – Moneycontrol

Posted: April 16, 2020 at 8:48 pm


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A couple of decades ago, Jeff Alger, then a senior manager at Microsoft, was coaching state-level soccer teams and realised that there was very little science to player development.

There were no objective ways of measuring how good players are, Alger said, and without being able to measure, you have nothing.

He said it offended his sense of systems design to recognise a problem but do nothing about it, so he quit his job, got a masters degree in sports management and started a company that would use artificial intelligence (AI) to assess athletic talent and training.

His company, Seattle Sports Sciences, is one of a handful using the pattern-recognising power of machine learning to revolutionise 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, architect of Seattle Sports Sciences artificial intelligence system, spent a week in October with Spanish soccer team Mlaga, which plays in Spains second division, capturing everything that happened on the pitch with about 20 synchronised cameras in 4K ultra high-definition video.

Its like omniscience, Milton said. The system, ISOTechne, evaluates a players skill and consistency and who is passing or receiving with what frequency, as well as the structure of the teams defence. It even tracks the axis of spin and rate of rotation of the ball.

That is not the only way that the companys 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 players ability, putting a value on an athlete is difficult.

Its a matter of whether that players movements and what they do with the ball correspond to the demands that they will have on your particular team, said 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 dont recognise that theres an injury coming or theres an injury that exists, Wagner said, adding that the system has a proven record of diagnosing or predicting injury. Were 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 Sciences system since he was drafted in 2008. He visits the companys facilities roughly every other week during the offseason to do vertical jumps, sway tests, a single leg balance test and a one-arm plank on the plate, blindfolded.

Based on the data thats collected, it tells me how Im moving compared to previously and how Im moving compared to my ideal movement signature, as they call it, 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, Wagner said. Sparta Science is also used to evaluate college players in the National Football Leagues annual scouting combine.

Of course, it is inevitable that machine learnings 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 analyse players and teams in hockey, soccer, football and lacrosse.

Mehrsan Javan, the companys 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 Sportlogiqs system to evaluate players.

Josh Flynn, Assistant General Manager for the Columbus Blue Jackets, Ohios professional hockey franchise, said the team used Sportlogiq to analyse players and strategy. We can dive levels deeper into questions we have about the game than we did before, 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. 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, Alger is fond of saying. Computer vision systems have to be told what to look for, whether it be tumours in an X-ray or bicycles on the road. In Seattle Sports Sciences case, the computers must be trained to recognise 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 labelled data wins, said Milton, the AI architect.

Seattle Sports Sciences uses Labelbox, a training data platform that allows Miltons data science team in Seattle to work with shifts of workers in India who label data 24 hours a day. Thats how fast you have to move to compete in modern vision AI, Milton said. Its basically a labelling arms race.

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 programme 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 labelled data. Thats whats going to improve things.

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Want to Be Better at Sports? Listen to the Machines - Moneycontrol

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April 16th, 2020 at 8:48 pm

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