If you listened to Jorge Posada’s recent appearance on CC Sabathia and Ryan Ruocco’s R2C2 podcast, one thing you’d take away is that the former catcher loves winning. Normally, that’s the kind of cliché I’d brush off. What professional athlete doesn’t like to win?
But when it comes to Posada, for better and for worse, that’s a meaningful descriptor.
The podcast episode noted Posada’s outbursts of competitive frustration, his diligent work ethic, and his strong sense of what it means to play winning baseball.
“Me flipping over the cups, me yelling at the pitchers. All that came from ‘sada,” Sabathia said before bringing his friend on for the interview. “He was a madman bro. He was fun to watch and I loved it!”
The same feistiness that made Posada a strong competitor has also rendered him very opinionated. And one of Posada’s opinions that I don’t exactly agree with is his skepticism towards the use of new baseball Analytics.
Listening to Posada’s interview proved somewhat of an emotional roller coaster. At first I found his position on Analytics intriguing. Posada explained that data had always been a part of his playing experience. He recalled reading printed scouting reports of what opposing players tended to throw/hit. Posada (and Sabathia’s) concern, however, is that nowadays data is not a mere resource, but the be-all and end-all of player decision making.
The deeper Posada went into this conversation, however, the more reactionary his position became. He cracked a joke about not wanting to take instructions from “some nerd with big glasses.”
There’s nothing wrong with raising specific questions about the value of analytics. I have questions, for instance, about the utility of stats like BABIP (batting average on balls in play) and FIP (fielding-independent pitching scaled to resemble ERA). When people come out as more broadly anti-Analytics, however, it is hard not to read their position as anti-scientific. That’s because Analytics are rooted in the scientific method. They are the empirical statistical output of countless baseball events.
Sabathia, for his part, gave an example of the kind of Analytics findings that make him skeptical. He noted that Analytics-focused coaches will zero in on the types of pitches batters struggle to hit. Sabathia countered that good hitters can adjust to any kind of pitch. He then added, “A guy can be good at hitting changeups, but that doesn’t mean he can hit my changeup!”
There are two problems with Sabathia’s thinking. Firstly, existing Analytics acknowledge this nuance. The idea that a good hitter can make adjustments is accounted for in the sabermetric teaching that pitchers should avoid pitching to the same batter three times in a game. And the abilities of each pitcher to throw different kinds of pitches is accounted for in pitch value data.
Secondly, Sabathia may be falling prey to what is known as the “availability heuristic.” This is the psychological trap in which we assess situations based not on thorough analysis, but on the most prominent images in our mind. Many people fear air travel, for instance, because plane crashes create vivid, terrifying images. But this doesn’t change the fact the planes are statistically far safer than cars.
Similarly, players who are skeptical of Analytics to begin with may be more inclined to remember the occasions when data-driven advice led to failure. In fact, it may be far easier for them to notice these failures than Analytically-driven successes. This is a particular risk in a game where even great hitters come up short in 70% of their at-bats.
So Where Should Analytics Go From Here?
While I could just conclude this article by questioning all of Posada and Sabathia’s takes, including their skepticism about the use of openers, and Posada’s suggestion that Randy Johnson wasn’t successful in New York (the way I see it, Johnson was quite good for a 42-44 year-old pitching in a hitters’ park), that doesn’t solve a bigger problem. Baseball is a game of numbers and a game of legends. And as it stands, there is a major rift between baseball’s legends and its statisticians.
And it’s not just the legends. Plenty of active players have questions about analytics too. Take former Yankee Didi Gregorius, for instance. When asked to compare managers Aaron Boone and Joe Girardi, Gregorius said the following:
“The biggest difference? Let’s see,” Gregorius said. “They’re both good managers. For me, the only thing I see different is Joe goes more with his instincts – that’s what I think – and Boone goes more with analytics.”
Gregorius explained: “That’s what the team is doing now most of the time. I always tell people I understand the analytics part of the game, but you’ve also got a take the heart of the player. You can’t measure that on paper or anything.
“So I think they still have to consider that you also have to trust the player, and if (Boone) sees something and he wants to do something different, then he should be allowed.”
Didi Gregorius is not a hot-tempered player of a pre-Analytics generation. And he doesn’t come across as close-minded to learning new things either. After all, it’s not every player who teaches himself animation, and draws playful caricatures of his teammates.
The lesson from Gregorius’s comments are clear. As data grows more thorough and more objective, it’s taking the human element out of player-manager communication. And if even a manager as friendly and supportive as Boone struck Gregorius as under-responsive to the in-the-moment needs of his players, that should raise red flags.
As a not-exactly-athletic baseball fan, it stings a little to hear one of my childhood heroes say that he doesn’t want his game getting ruined by nerds (hey Jorge, back in 2003 I made my own “Posada For MVP” lawn sign!). But what Posada, and certainly the likes of Sabathia and Gregorius, are saying is not that data cannot be useful. They are saying that they want to sense the people giving them advice are not just computers, but listeners.
“You see so many things in the box, things you don’t see in the dugout,” Posada told Sabathia earnestly. One can hardly fault a catcher for wanting coaches to believe what he saw with his very experienced eyes.
The next generation of baseball statisticians will have to develop emotional intelligence alongside their computational intelligence. Once that bridge is crossed, then hopefully Jorge Posada can be convinced that we nerds are also in it to win.