opinion Will AI Change Baseball Forever?

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Once you define something as the best, there is no need to say more. So I’ll keep my appreciation for Michael Lewis’ 2003 “Moneyball: The Art of Winning an Unfair Game” short. This is the best non-fiction book I’ve ever read, and on the short list of the most influential. Until “Moneyball” revealed how Oakland A’s general manager Billy Beane used statistical analysis to beat rich, foolish opponents, baseball was the realm of the witch doctors. A’s first baseman Scott Hattberg told Lewis that his previous team, the Boston Red Sox, brought in motivational speakers to awaken the mystical aspects of their hitters. One lectured on the virtues of the thymus gland. “You should have beaten your chest before you hit,” Hetberg said, “to release all that unused energy and aggression.”

As a new season begins, the triumph of statistics over the thymus gland is so complete that there are few analytical mountains left to climb. Each team in every major game employs mathematicians and engineers to find a strategic edge that shrinks as all the other mathematicians and engineers do the exact same thing. The Tampa Bay Rays put a uniformed analytics coach in the dugout. Some teams have better data science and player performance models than others, but “Moneyball” demonstrated the insanity of competing against the odds with intuition. He kicked all the schmucks out of the casino.

You’d think this would lead to the book’s gradual obsolescence, but for the sports-obsessed, super-stupid segment of its readers — my people! – “Moneyball” is no longer binding. It’s a belief system, and like feminism and existentialism, it’s going through a second wave, with artificial intelligence in part.

This second wave has multiple progenitors, but we will focus on the least likely. Kyle Boddy was a restless junior software engineer bouncing from Microsoft to PokerStars when he read “Moneyball,” radicalized and started a baseball blog called “Driveline Mechanics.” In 2007, He created Driveline Baseball, a physical lab where he could test his theories. It sounds ambitious, but the first Driveline was in a Seattle basement under an aikido studio next to a strip club adjacent to an RV park. “Obviously, I lived in an apartment next to the RV park, not in the RV park itself,” says Boddy. “I know you are doing journalism here.”

It was from this modest perch that Boddy met Trevor Bauer, a promising young pitcher with four major league appearances for the Arizona Diamondbacks on his resume. Bauer had also read “Moneyball” and his dedication to analytics and self-improvement led him to purchase a brand new $8,000 Edgetronic camera. (Bauer is now baseball’s most deserving pariah.) “It was actually Trevor’s dad who said, ‘If I show you this camera, you’ll want one right away,'” says Boddy. “I was like, ‘Yeah, sure.'”

Edgetronic reduces the blur typical of pitching or hit motion by shooting thousands of frames per second, providing perfect clarity even in close-ups. After just a few frames of footage, Boddy realized that a 150-year-old physics mystery — the effect of small variations in the placement of the fingers on the seam of a baseball, or the angle of the bat as it meets the ball — was on his eye. was hitting , asking to resolve. “I’ve spent my whole life to this point exploring underground technologies and using them in ways that people don’t expect,” says Boddy. “And now here’s this source of optical information that’s never been available or affordable before. I figured if I can’t use this thing, I need to go back and work for Microsoft.”

Bodie immediately bought an Edgetronic on eBay. It was also an important insight into how to use it. Camera data can help players experiment with new pitch grips and improve their swings, and an avalanche of statistical data can confirm results. But to revolutionize player performance — to make players truly understand what they need to do — the two had to come together in simple and elegant software. And the source of this synergy was artificial intelligence.

I’ve talked to a lot of people about AI, and almost every conversation has an awkward point where we both admit we don’t know exactly what AI is. To be fair, it could be many things. There is no fixed definition. But people are quite adamant about the money they expect to make from it, and I’m an AI columnist, so it would be nice if people didn’t talk about the benefits of this technology in such a vague way. , I do not know. , Herbalife?

All that said, Boddy has the most practical definition of AI I’ve heard. “It’s the best translator ever,” he says. “Literally, we communicate with our players in Japanese and Korean and Spanish with a ChatGPT plugin that translates the language of baseball flawlessly in real time. But from a technology perspective – Tinkering with code bases, switching between PHP or Python, none of that matters anymore. … AI takes completely different code or data or insights and synthesizes them. Numbers become words. Words can become images. Everything can talk to everything.”

Boddy and his engineering team now rely on AI to combine dozens of data streams to create customized coaching regimens. I can’t stress enough how little this is like your weekly personal training session. Video analysis breaks down individual muscles and movements inch by inch. The hardware (bats and balls) are equipped with software (sensors) that track every action of the baseball and feed them into equations that measure force and torque. As with all data gurgling AI software, the process gets faster and faster. Driveline has collected enough historical performance data that it can combine five non-baseball physical tests into dead-solid predictions of fastball speed and bat speed.

Even in his infancy, the driveline helped Bauer become one of the best pitchers in baseball. But Bauer arrived at Driveline as a well-known prospect, with talent so evident that he received a $3.4 million amateur signing bonus. Driveline’s ability to take soft-throwing Los Angeles Dodger Tony Gonsolin ($2,500 signing bonus) and help him emerge as an All-Star, through endless cycles of pitch design and muscle development and analysis and Gonsolin’s own hard work, is more It was impressive.

Driveline has now worked with thousands of professionals and over 40 All-Stars. Regardless of their backgrounds or countries of origin, Boddy has found that newcomers to “Moneyball” are more lively than players with Lewis’ profile. Because they’ve all read “Moneyball.” Or at least watched the movie. He grew up fluent in analytics and edgetronics, and he understands that baseball is both a sport and a market in which he is a counterpart. Projection algorithms used by teams on the demand side value and power, so the supply side guys go to the driveline to help them throw faster and hit harder. After which they get paid more. The market is compatible.

If that compromises the romance of the game for you, now would be a good time to stop reading James Earl Jones’ speech from “Field of Dreams.” Because where things really count—and relevant for anyone who works for a living in the age of AI—is where labor and management diverge.

Most teams now have tech like Driveline. The major leagues have poached more than 40 Driveline alums for in-house work, and Boddy is a special consultant to the Red Sox. So why would a player spend their money and travel to an inconvenient place to work on their game? Confidentiality If your employer has kinesiology data showing how you suddenly improve at your job, they also know the situations in which you may suddenly deteriorate. Or how to best train your replacement. Last year’s average MLB player salary was $4.5 million; It makes a lot of sense to pay Driveline $20,000 in training fees and keep your data. Mathematics is probably not that compelling in your work. But if you feel like a version of this tango isn’t ready for your profession, I’ve got thyme oil to sell you.

The more human performance is enhanced by AI-powered data, the more important it will be to establish ownership of that data. Mere pawns in Ben’s “moneyball” chess game, players should be commended for figuring this out quickly. They even negotiated limits on how teams could use in-game data in their collective bargaining agreement. “I have friends who are lawyers and prop traders, and it’s shocking to me how far sports have come when it comes to these technologies,” says Boddy. “A lot of them don’t see what’s going on.” This is called the third wave.

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