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"Scouting the stat line" is a pejorative nowadays, meaning you evaluate a player strictly on their baseball-reference page. But not too long ago, it was a pretty smart thing for a front office to do.
In 2002, all you had to do was look at David Justice’s on-base percentage to determine that he might have something left in the tank late in his career. A decade or so later, we started caring less about raw rate stats and focusing more on who was the least lucky player, using expected stats.
xFIP and xSLG came into fashion as ways to measure what a player should have done given a luck-neutral environment. Then came batted ball data, and everyone rushed to figure out who hit the ball the hardest and whose slider had the most spin. If a player’s performance was subpar but the ball data told a different story, you may have a bargain on your hands.
We now know that someone with a good OBP can be pretty useless if they lack power or defensive value. We also, arguably, are starting to realize that expected rate stats, while useful in predicting who might be good next year, don't offer much for tomorrow’s game.
The Limitations of Current Baseball Analytics
Batted ball data, while useful, offers even less in player evaluation, because it doesn’t truly factor in, for instance, a feel for hitting. Luis Arraez has looked terrible under the lens of batted ball data, but would the Marlins prefer anyone else at the plate when all you need is a base hit to swing the game in the late innings?
His mindset as a hitter is elite, and evaluating that could usher in a new wave of analysis.
I always go back to the debate over clutch stats. It’s true that over a big enough sample, players will hit and pitch at the same level in clutch situations as they do normally. But I like to imagine myself, in eighth-grade Babe Ruth League, up at the plate with the game on the line.
I hit scared. Not of the ball, but of striking out, especially when the team needed me to perform. I would flail at everything and could usually put a ball in play, weakly, given that I had three shots at it. I was also kind of fast, the opposing defense was usually poor and I probably went one-for-four in close and late situations.
My approach was terrible. My thought process was terrible. The moment felt too big for me. I made bad swing decisions. My season average? .250. The stats didn’t say I was a bad clutch hitter, but in my mind, I knew that I was.
How do you think David Ortiz in his Red Sox heyday felt with the game on the line? He knew the opposing pitcher’s tendencies and their plan of attack. He knew what pitches he did well against. He’d done it before. A mixture of confidence and savvy is a great combination.
I don’t even doubt that we can find a way to measure both of those variables. We’ll start park-adjusting it and call it SAVCON+. It’ll weigh chase percentage, exit velocity in close and late situations, number of pitchers/hitters faced, number of teammates weddings he attended on a rate basis, and mold it all into a two-digit percentile in which 100 is average.
But tongue in cheek or not, the deeper we go into which stats can predict with higher probability what a player will be going forward, and in certain situations, the more players will succumb to their own analysis. Because once guys start thinking about why their expected performance is so good, the confidence game resets.
Why a Results-Driven Mindset Matters
Once a player hears about a stat and which extreme he falls on, the possibility arises where he says to himself, “I’ve been a groomsman for six different guys on this team and was an usher in four,” or in more realistic terms, “My exit velocity is good, so I don’t need to make any adjustments.” He’s broken the glass.
Showing somebody an impression you do of them has the same effect. The joke is less funny, more mean, and you probably won’t do it as much. Then trying to perform it under pressure is a recipe for disaster.
This ties into the cat-and-mouse game between the pitcher and hitter. If either side is in his own head thinking about how he needs to make swing or pitch decisions based on attaining better expected or batted ball stats, the opponent has a huge advantage.
Every year, we hear about how Max Kepler is going to adjust and start posting a higher average on balls in play, but the self-described tinkerer hasn’t been valuable as a hitter outside of 2019, when Rawlings was producing superballs.
Sometimes Kepler tries to lift the ball more, sometimes he tries to go the other way more. He’s a pretty smart, introspective guy, but he lets pitchers not named Trevor Bauer carve him up on a routine basis. For all we know, the pitchers are Lisa Simpson playing rock paper scissors with Bart, thinking to themselves, “Poor predictable Max, always picks rock.” To which Max thinks, “Good ol’ rock, nothing beats that!”
Measuring the Mental Game
Despite our efforts, we can’t know the truth of what goes on in players’ heads, but it's clear some guys are better at the mental game than others. You can call this sought-after ability character, intangibles, baseball IQ, BALLPAYER™, or Derek Jeter-like. An overemphasis on it by front offices leads to the type of teams that got lapped in the mid-2000s by teams with a basic analytical understanding. An under-emphasis can lead to performance that falls well below a team’s talent level, like the 2022 White Sox (although injuries and Tony La Russa played a big part, as well).
Do the Twins have these kinds of guys?
I have no idea. I rely on beat reporters and league insiders to provide me the occasional morsel of information on a bi-annual basis, such as the revelation that Tyler Mahle didn’t do shoulder-strengthening exercises last year after his injury and then went straight to Driveline after the season concluded to work on his slider. Otherwise, all I have is the eye test.
Joe Ryan looked confident last year, but he definitely seemed to lose a lot of battles, especially against tougher opponents. Did he rely on his fastball too much deep in counts? Will he trust his new offspeed pitches enough to go to them in more high-leverage situations?
Jose Miranda looked pretty excitable at the plate and in the field in his rookie year but also had a lot of big hits. Can he be more selective and let the game come to him in his follow-up campaign? (The opening series was a promising indicator, with Miranda drawing three walks in three games.)
Is Gilberto Celestino a mistake-prone head case or was he a young player pushed into action before he was ready and trying too hard to keep his roster spot?
It’s the front office’s job to figure out these answers, and if the conclusion is a clear negative, it behooves them to try and trade the player to a team that overvalues his statistical output. That could be the new market inefficiency for teams to capitalize on.
In Moneyball, old-timey scouts were mocked by Billy Beane for saying guys “looked like a ballplayer,” or that they might not be confident players based on the women they dated. That kind of analysis is still pretty gauche, but in a world where everything a player does has a metric assigned to it, and every team has access to all of that data beyond even what the public does, perhaps effectively evaluating a player’s mind is a way to win Moneyball 3.0.
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