As the New York Yankees prepare to start their spring training play rather soon, there are a few things we need to keep in mind.
If a struggling player doesn’t seem to find a way to end his March slump, don’t panic. The season hasn’t started yet! If the elite pitching prospect is getting rocked game after game, calm down! He may be experimenting with a new grip or putting advice from big-league coaches into practice.
Meanwhile, if a journeyman is hitting the crap out of the ball and leads the Grapefruit League in homers with a week of spring games left, don’t overreact. He may be taking advantage of all those previous scenarios. This time of year, it’s important to take everything with a grain of salt.
Spring training games often feature lots of minor leaguers because teams want to give them a look. They are also full of hurlers trying a new pitch, or hitters experimenting with a new batting stance or a retooled swing.
How can we consider a stat line legit if it comes in a small sample size inflated by facing lots of inexperienced minor leaguers or players trying some changes in their game?
Small sample sizes
Consider this paragraph written by Steve Slowinski for Fangraphs a few years ago to explain what sample size means to the game of baseball. Yes, the article is from 2010, but the point (and the big picture) remains the same:
”Each little moment in baseball is essentially random. Not random in the sense that all outcomes are equally likely and subject completely to chance, but random in the sense that the most likely outcome doesn’t happen every time. If the best hitter in baseball faced the worst pitcher 100 times, he would very likely strike out a couple of times and hit into a double play or two. He wouldn’t always hit a home run even if it was Coors Field and the pitcher was throwing meatballs.
When dealing with pitches flying 90+ miles per hour and split second movements, a whole bunch of randomness gets thrown into the pot. This means that any one plate appearance might have a funky result, meaning that you need to see lots of events to get a clear picture of what is going on.”
The last sentence is crucial to our analysis. We need to see lots of events to get a clearer picture of what is going on. A handful of spring training at-bats shouldn’t be, and aren’t, predictive of anything.
That’s why the baseball industry began, a few years ago, to use the concept of stabilization. When we collect what we can call enough data of a specific stat, we say it becomes more stable, and when this happens, we get a better approximation of the truth, of the real skill level of the hitter/pitcher.
Here are the stabilization points for offensive stats:
- 60 PA: Strikeout rate
- 120 PA: Walk rate
- 240 PA: HBP rate
- 290 PA: Single rate
- 1610 PA: XBH rate
- 170 PA: HR rate
- 910 AB: AVG
- 460 PA: OBP
- 320 AB: SLG
- 160 AB: ISO
- 80 BIP: GB rate
- 80 BIP: FB rate
- 600 BIP: LD rate
- 50 FBs: HR per FB
- 820 BIP: BABIP
And the stabilization points for pitching stats:
- 70 BF: Strikeout rate
- 170 BF: Walk rate
- 640 BF: HBP rate
- 670 BF: Single rate
- 1450 BF: XBH rate
- 1320 BF: HR rate
- 630 BF: AVG
- 540 BF: OBP
- 550 AB: SLG
- 630 AB: ISO
- 70 BIP: GB rate
- 70 BIP: FB rate
- 650 BIP: LD rate
- 400 FB: HR per FB
- 2000 BIP: BABIP
If you look at the big picture, few of these stabilization points are hit during spring training. And even if a player gets 60 plate appearances, the quality of the data will not offer useful results to say that X player’s strikeout rate is Y, for example. That’s because of the reasons we’ve outlined: there are lots of minor leaguers, new approaches, new swings, new pitches, an overall lack of motivation by established veterans, and more.
Spring training stats aren’t really predictive: the evidence
Consider these stat lines from last year (batting average/on-base percentage/slugging percentage):
- Robinson Cano: .441/.476/.610
- Ildemaro Vargas: .355/.385/.484
- Frank Schwindel: .327/.379/.531
- Brandon Drury: .315/.373/.574
- Johnny Field: .305/.397/.508
- Manuel Margot: .304/.361/.571
- Rio Ruiz: .283/.371/.566
- Lewis Brinson: .278/.304/.593
Their regular season lines:
- Robinson Cano: .256/.307/.428
- Ildemaro Vargas: .269/.299/.413
- Frank Schwindel: .067/.067/.067
- Brandon Drury: .218/.262/.380
- Johnny Field: .234/.287/.421 (Triple-A)
- Manuel Margot: .234/.304/.387
- Rio Ruiz: .232/.306/.376
- Lewis Brinson: .173/.236/.221
Of course, some players do carry their spring success to the majors. Pete Alonso, Yoan Moncada, Marcus Semien, Tim Anderson, JD Davis and Jorge Soler did it. However, it’s not the norm.
Let’s apply the opposite case with the New York Yankees: these players struggled badly in spring training last year:
- Clint Frazier: .143/.228/.245
- Mike Ford: .143/.217/.286
- DJ LeMahieu: .205/.271/.273
- Gary Sanchez: .211/.225/.368
And then, they went on to have good seasons:
- Clint Frazier: .267/.317/.489
- Mike Ford: .259/.350/.559
- DJ LeMahieu: .327/.375/.518
- Gary Sanchez: .232/.316/.525 (low for his standards, but good in perspective)
We can’t use spring training stats to predict future performance. Sometimes, the changes that a player is trying will work. Sometimes, they won’t. Sometimes, hitters just ride a month-long hot streak. We can’t know for sure. So next time a Yankee is struggling in March, give him time. Don’t take him for dead just yet.