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Feature: Can New Technology Bring Baseball’s Data Revolution to Fielding?

For the rest of the game, I watched Bradley on every pitch. Occasionally he began moving even before the batter made contact. His sense of where the ball would go seemed uncanny. When I asked Willman about this, he looked up the data on all the balls hit in Bradley’s direction this season. Then he compared them with all the balls hit to every other center fielder.

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A “spray chart” for the Houston Astros’ second baseman José Altuve, based on Statcast data. Credit Graphic from Daren Willman/MLB Advanced Media

Bradley proved to be far from the fastest runner, and while his teammates claim that he usually takes the shortest routes to get to where fly balls will land, it turns out that his are far from the most efficient. In several directions, including heading straight back to get a ball, he ranked below the league average in that regard. But the quickness of Bradley’s first step on all batted balls was near the top among outfielders; on balls that resulted in outs, he was the best in baseball. On some plays, Statcast showed that Bradley was moving before the ball was even hit — exactly what I thought I was seeing in Fenway.

To track a moving object like a ball, you typically set two cameras perpendicular to each other. Then a computer program hunts through the images from each camera for something that, based on its shape and movement, seems likely to be a ball. By triangulating the two views, you can figure out where that ball was at a given moment, and then where it went. In 2006, BAM started using that approach to gauge the velocity, movement, location and spin rate of every pitch. It called the system PitchF/X. But PitchF/X doesn’t work nearly as well with forms that don’t have a predictable shape, like people. And when you try to follow those forms as they come together and then split apart in seemingly random patterns, which happens in baseball on nearly every play, it doesn’t work well at all.

In 2012, at the International Broadcasters Conference in Amsterdam, the Swedish company Hego (now ChyronHego) introduced a technology it had patterned after the human visual system. “It could see the depth of the field inherently,” recalls Joe Inzerillo, M.L.B.’s chief technology officer. The new technology couldn’t pick up balls, which were too small and moving too fast. But Inzerillo believed it would have no trouble tracking players.

BAM had been trying to capture data on player movements ever since its start more than a decade before. At the Amsterdam conference, Inzerillo sensed that he and his team were close. He proposed the idea of trying to integrate the Hego system with another technology so they could follow the ball and the players simultaneously. It wouldn’t be PitchF/X, he knew; in a large space like the entire field, the prospect of the ball getting lost entirely amid the background clutter would increase drastically. But he wondered about the modified Doppler radar system, called TrackMan, that was starting to be used to measure the trajectory of thrown and batted balls. “Radar, on the other hand, does not really see the background,” Inzerillo says. “And one unit can cover the field pretty well. We literally sat down and sketched it out on a piece of paper and figured out how these two systems could talk to each other.”

Hego’s two camera pods have now been installed, 70 to 150 feet apart, along the third-base line in all but two major league ballparks. (In Boston and Milwaukee they’re along the first-base line because of architectural quirks.) The TrackMan system, adapted from one used for missile defense, traces the ball as it would any moving object. Statcast is programmed to layer the information generated by one atop the other, creating a representation of what’s happening on the field. Usually it works.

Sometimes, though, it doesn’t. Nearly three years since an initial trial run in the Arizona Fall League, Statcast is still committing rookie errors. Chopped grounders that bound high into the air elude the radar. So do high pop-ups. The system is accurate at the middle of the field, less so toward the foul lines. And even when the technologies are in sync, glitches can occur. Willman showed me an example: an out made by the Boston right fielder Mookie Betts earlier this season, a play categorized by the Statcast database as one that’s made successfully only 5 percent of the time. When Willman called up the archived video from the home telecast, I expected to see Betts diving across the outfield for a sinking liner or leaping against the wall to pull a home run from the stands. Instead, this was a fly hit directly at him. “Easy play for Mookie,” the announcer said. Willman shook his head. “That’s one that got lost in the radar,” he said.

Even if the vast majority of the Statcast data is accurate, its sheer volume — millions of lines of digital output from every day of the baseball season — remains difficult to process. Merely coming up with a program to unpack the pages of computer coding is beyond the wherewithal of most teams. “It’s nice to say that we have the technology, the means of capturing data,” says Jeff Bridich, the Colorado Rockies’ general manager, who played baseball at Harvard. “But now we’re plowing through it, trying to understand it. What does all of this mean?”

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The sports analyst Daren Willman at his home outside Houston. Credit Thomas Prior for The New York Times

That’s not to say Statcast isn’t already having an influence. Not every franchise can risk $ 72.5 million on an untested outfielder, which is what Boston did with Rusney Castillo, a Cuban defector who played 99 games in the outfield for the Red Sox over the last three seasons before being sent back to the minors for good. Instead, some teams hire young analysts to crunch data. They take a chance on unproven technologies. And like Beane’s A’s a generation ago, they try to find an edge.

The Tampa Bay Rays have one of baseball’s lowest payrolls. Baseball insiders also assume the team has the largest analytics department, though because of the team’s secretiveness, nobody can say for certain. “They have people who are just out of school, smart people,” Scott says. “They’re not paying them much — it’s kind of the sweatshop model. They’re just cranking it out. The risk is whether the data they’re using is real, that they’re actually learning what they think they’re learning.” When I visited Tropicana Field this summer, none of the Tampa analysts were allowed to talk with me. But I asked the manager, Kevin Cash, about Statcast. “It has become very popular here,” he admitted. “You’re able to compare a lot of things that you couldn’t compare before.”

When Kiermaier was struggling offensively last year, Cash wanted him to understand how well he was nonetheless playing. “What he was doing was helping us win games more than anyone picking up a newspaper could tell,” he said. So Cash did something that every big-league manager might be doing in five years. Using Wins Above Replacement, a somewhat arbitrary (but increasingly popular) statistic that amalgamates the output of various categories into a single number, he showed Kiermaier how well a particular All-Star outfielder was hitting. Then he used Statcast data to quantify Kiermaier’s value into an approximate defensive equivalent. “I said, ‘Here’s what you’re doing — not with home runs, not with batting average, just on defense,’ ” Cash said. “ ‘You’re impacting our club in a huge way, just like he’s impacting their club.’ ” Relieved of the pressure to hit, Kiermaier loosened up. He finished the season at .263. He also won the Gold Glove Award as the best center fielder in the American League.

While Kiermaier was slumping, Jackie Bradley Jr. was struggling to stay in the majors. As of early August last year, his batting average stood at .102. That was not only well below the standard that Farrell had set for him to stay in Boston’s lineup; it was on pace for the worst batting average by a position player ever recorded.

Fortunately for Bradley, the Red Sox — who would finish last in their division for the second consecutive season — were looking ahead. That month, they hired the former Expos, Marlins and Tigers executive Dave Dombrowski as their new president. Though Dombrowski considered Bradley a gifted outfielder, he had never seen him play more than a few games at a time. Lacking the statistical means to judge just how good he was, he hoped to find out by watching him in center field for the rest of the season. So Bradley remained in the Boston lineup.

Unexpectedly, he began to hit. From Aug. 6, shortly before Dombrowski was hired, to Sept. 7, Bradley went 39 for 92, hit seven homers and drove in 32 runs. He finished the season with a respectable .249 batting average. “The defense allowed him the opportunity to grow into an everyday player,” Farrell says. This season, Bradley has done even better as a batter. He had exceeded his 2015 output in every major offensive category by the end of June. At one point, he recorded a hit in 29 consecutive games. He was voted into the All-Star Game as a starting outfielder. By late September, he was hitting .276, with 26 home runs, and was closing in on 100 R.B.I.

I was in Houston visiting Willman the weekend before the All-Star Game in July. As he drove me to the airport, he chuckled at the perception of Bradley as a breakout star. Like Kiermaier, Bradley had been contributing to his team even when he wasn’t hitting. It’s just that nobody understood quite how much.

Before he was hired by M.L.B. Advanced Media in January, Willman worked as a software developer for the Harris County district attorney. He also created the popular statistical website Baseball Savant, which he still runs, now under the MLB.com umbrella. The insights he offered were so acute that several franchises interviewed him for positions in their analytics department. All of them wanted him to take a pay cut, which astounded him. “Think of how good the Red Sox could be right now if they used this data,” he said. “They have the money. They could spend $ 1 million and hire five guys like me, who really understand baseball and really know the technology. How much is a win worth? Five million? More? If we give you three or four wins, that $ 1 million pays for us several times over.”

When Willman played college baseball at Texas Lutheran, he was a talented defensive center fielder. But the pros had no interest in him. “I just didn’t hit well enough,” he said. “Even if I was good enough to be the best defensive outfielder in the majors, there was no way to quantify that. So all they could look at was my hitting.”

The same approach to evaluating talent that might have undervalued Willman then seems to be undervaluing him now. But Willman is confident that his skills will be appreciated, at least in hindsight, as Statcast transforms his sport. “Where it’s really going to end up is, players are going to start getting paid a ton more money because they play great defense and everybody realizes it,” he said. As he talked, his phone dinged every few moments, signaling that his work had been retweeted. “There will be a whole new baseball revolution based on information that we are just starting to get.”

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