The future of the World Series does not wear Dodger blue or Astro orange. It won’t throw a strike, hit a home run or chase a line drive into the gap, though it can predict the probability of such things occurring with remarkable accuracy.

The future of the World Series lives not in the mortal realm, but in mainframes and clouds and flash drives and smartphones carried by men with pedigrees much loftier than half a lifetime in the worn fields of the minor leagues.

The World Series that starts Tuesday night in Los Angeles will feature two teams, in the Los Angeles Dodgers and Houston Astros, who use statistical analysis as their primary operational tool. While other Series teams have relied on analytics, including the Chicago Cubs and Cleveland Indians last year, never have there been two clubs who use it as much as the Dodgers and Astros.

Given their success, it’s hard to imagine more teams won’t try to imitate what they have done. The statistical revolution that has taken over baseball in recent years might be close to complete. If math has become a reliable driver to the World Series, then why won’t everyone fill their front offices with executives who can cull data to find trends?

Twenty-nine years ago, when the Dodgers last went to the World Series, they won the championship in part because an old scout with a good eye for detail told the team that Oakland Athletics star relief pitcher Dennis Eckersley would throw a slider to left handed hitters if the count was 3-2. Kirk Gibson, a slugging Dodgers outfielder, remembered that advice as he faced Eckersley in the ninth inning of the first game, trailing 4-3. He even stepped out of the batter’s box with the count 3-2 to smirk as he thought of the scout’s report. Sure enough, Eckersley threw the slider, and Gibson – on gimpy knees – knocked the ball into the bleachers for a two-run home run that won the game and broke the A’s.

Today, teams don’t need that scout. Such information is available in any number of databases that can be mined as efficiently by a fan at home as a scout in the stands.

What the Dodgers and Astros do now is far more complex than looking at the tendencies of an opposing pitcher. Their analysis is some of the most sophisticated in baseball, changing lives and careers with a deeper understanding of what players do and what works best for them than those players do themselves.

Houston’s baseball offices are run by a former management consultant with degrees in engineering and economics, and a former blackjack dealer and Nasa researcher. The Dodgers’ top decision–makers are an ex-Wall Street analyst and an MIT graduate with a doctorate in philosophy. These are not your typical baseball lifers. But then again, the Astros and Dodgers aren’t looking for typical baseball solutions.

Much of what is lost about the Dodgers’ $270m payroll, the largest in baseball, is that a big part of it is dedicated to players acquired before the new regime – most of whom have either left or will soon. Resources now and in the coming years will be put toward growing their own players in the minors or finding discarded gems that the analysis favors. Players like Justin Turner and Chris Taylor, who were going nowhere until Dodgers’ advisers helped them change their swings and turn them into stars.

A perfect example of what Los Angeles is doing is Tony Cingrani, a reliever in Cincinnati who threw hard but who had little success. The Dodgers acquired Cingrani in a little-noticed trade deadline deal with the hope their research could fix him. As this Fangraphs story shows, they urged Cingrani to throw his slider more and his fastball to the top right. Cingrani has turned into one of the Dodgers’ most important relievers, and was unhittable in the first two rounds of the playoffs.

The Astros have their own statistical approach. In a now famous Sports Illustrated story from 2014, Ben Reiter described an enclave in the team’s offices where a group of employees have created an evaluation database that contains piles of information about every player in the organization, as well as those the team considered drafting.

“The inputs include not only statistics but also information – much of it collected and evaluated by scouts – about a player’s health and family history, his pitching mechanics or the shape of his swing, his personality,” Reiter wrote.

“The system then runs regressions against a database that stretches back to at least 1997, when statistics for college players had just begun to be digitized. If scouts perceived past players to possess attributes similar to a current prospect, how did that prospect turn out? If a young pitcher’s trunk rotates a bit earlier than is ideal, how likely were past pitchers with similar motions to get hurt?”

Such depth is only the start. Almost everything about the teams in this World Series – from why the players are there to how they bat in the lineup – has come from a computer somewhere. While their approaches are not without some human interpretation, the mountains of data on each player reveal information never known to these teams before.

It’s been 14 years since Michael Lewis wrote a book about the methods A’s general manager Billy Beane used to give his cash-poor team a chance against the big-market clubs. Lewis called the book Moneyball, and Moneyball became a word used for every team whose approach involved numbers. Los Angeles and Houston have much bigger budgets than Beane’s A’s, meaning their Moneyball is much more sophisticated than Oakland’s.

Still, the basic philosophies are the same. Math does not deceive the way a human eye can. If Lewis’s book started a revolution in baseball, it seems the struggle has now been won. The future of the World Series seems to be the present.