How Astros use the ‘Moneyball’ approach

At Rice University, Jimmy Disch, an associate professor of sport management, teaches a popular course called “Moneyball.” The name, of course, is lifted from the book and movie about Billy Beane’s use of statistical analysis applied to baseball. “Sabermetrics,” as it’s called, made the Oakland A’s a competitive team in the late ’90s, despite a shoestring budget.

The Astros were early adopters in the sabermetrics revolution, and they continue to invest in it heavily — and to apply its findings on the field. Take the defensive strategy of infield shifting, for example, which they use more than any other MLB team: When an opposing batter approaches the plate, Astros infielders move left or right, up or down, shifting to the spots on the field that the team’s analysis of the batter’s history suggests will help them snag the ball.

Recently, I talked to Disch to talk about how the field of sabermetrics has evolved, and how the Astros use it to their advantage.

How has data analysis changed the way baseball is played?

Any sport is always looking for ways to modernize and become more competitive. One thing that’s been a major factor in baseball is that analytics has shown how shifting is a supreme advantage to the defense. Some players’ careers have been drastically affected by this, like (Los Angeles Angels first baseman Albert) Pujols specifically. Now that everyone’s shifting him, it’s a lot harder to get a hit.

As far as hitting styles go, the big thing now is launch angle — trying to develop a swing that helps you get the ball in the air more. That has increased the number of strikeouts for hitters, but it’s also increased the number of home runs.

What are some examples of game-changing findings that a statistician was able to observe while a coach could not?

Recently, teams have started applying the data they have to trying to improve pitching styles. It used to be you really didn’t want to throw high in the strike zone, but now you’ve got pitchers doing what they call “tunneling,” where they can start their fastball up high. Then they can start their curve ball in the same “tunnel” and make it harder for the hitter to decide the type of pitch. In the old days, most coaches wanted the pitchers to keep the ball low and just work it in and out. But the Astros were ahead of that trend. [Astros pitching coach] Brent Strom has been preaching that for a long time, well before there were statistics to back it up.

What are some surprising facts or anecdotes you share with the students in your “Moneyball” class?

When the Owls won the Colwith lege World Series in 2003, we had a pitcher, David Aardsma, who went on to play professionally. After he left Rice to play, he took my sports analytics class, and for his final project, he took a scientific look at his best pitches in terms of outs. He hypothesized that his slider was his best out pitch against righthanded batters, and his changeup was the best for left-handed batters. The data showed that the slider was best for righthanded batters — but it was also the best for left-handed batters. If he’d known that earlier, he would have changed his approach.

What do people tend to misunderstand about sabermetrics?

What I try to emphasize in my class is that you’ve got to start with a theory. You can’t just throw a bunch of numbers into an analysis and come up

something meaningful.

It’s also not the case that sabermetrics is a replacement for scouting and coaching. Today you have a blend of analytics and traditional scouting. If the scouts and the numbers agree that a player is worthwhile, you draft him, and if they agree that he’s not, you don’t. But when the scouts say one thing and the numbers say another, that’s when you have to dig more deeply.

How has sports analytics changed in recent years as technology has evolved? What are some of its newer applications?

Instead of player acquisition, the focus is now getting into the area of player development. Everything’s more quantifiable now; you know exactly the spin rate on pitches and how much the ball deviates. Improved video technology has given coaches and players tools where they can immediately analyze what just happened. But at the same time, all the low-hanging fruit is gone. All the easy answers have been found. So now you’ve got to really dig to find out what’s going on.

Why hasn’t data analysis played as big a role in other sports?

Baseball has always been a sport that measured everything, but the fact that the pitcher puts the ball in play is what makes the difference. You always know where the ball’s going to start, and in order for something to happen, the batter’s got to put it in play.

Basketball is also highly analytic, and the Rockets are trendsetters in the NBA with data analysis, just like the Astros are trendsetters in the MLB. There’s also a lot in soccer, but there the scoring is so low that it’s hard to find valid predictors.

In football, the size of the field and the nature of the beast makes it hard. There are still a lot of measures that aren’t predictable. A lot more comes down to coaching in football.

Which do you think would be more helpful to the Astros: all the data in the world or Jose Altuve?

That’s a good question, because it goes back to the value of traditional scouting. All the numbers say no on Altuve. He’s too small; no one expected him to have nearly the power he has. But fortunately, one of our scouts looked at him and gave him a chance. They’d sent him home from tryouts, thinking he’d lied about his age because he was so little, but he got his birth certificate and came back and they let him play — and he was so skilled that they signed him.

In the end, all the numbers can do is maximize the paradigms that you have to work from. You still have to use your judgment.

Latson is a staff writer and editor at Rice University’s Jones Graduate School of Business and the author of “The Boy Who Loved Too Much.”