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Checking in on 2021 Rangers wOBA/xwOBA

With around 10% of the season finished, its a good time to take a look at how Rangers hitters are faring

Texas Rangers v Los Angeles Angels Photo by Sean M. Haffey/Getty Images

The Texas Rangers are sitting at 9-10 after taking two of three in Anaheim, and have an off day today before starting a three game set in the south side of Chicago against the Pale Hose on Friday.

The combination of having made it a little over 10% of the way through the regular season, and the Rangers having an off day today, makes me feel its appropriate to take a look at how Rangers hitters are faring so far this year, in terms of wOBA and xwOBA. wOBA is a formula intended to provide in one number a “weighted on base average” that allocates value based on how much each method of reaching base is worth. xwOBA is calculated by using, instead of the actual results on batted balls, the expected results (probabilistically weighted) for each batted ball based on exit velocity, launch angle, and in some instances sprint speed. Strikeouts, walks and HBPs are treated the same under each method, and the numbers are not park-adjusted.

The two numbers allow us to compare the actual results on the field with what the expected results would be, based on the quality of contact made by a player. Especially when dealing with small sample sizes, I find xwOBA more informative if I am looking at how the player has performed, since in the long run hard hit balls being caught will generally average out, as will balls that just happen to find a hole, and wOBA and xwOBA should, for most players, converge in the long run.

Here’s the wOBA and xwOBA data for the Rangers so far this year:

Player wOBA xwOBA
Solak, Nick .376 .371
Holt, Brock .371 .405
García, Adolis .368 .338
Lowe, Nate .365 .376
Gallo, Joey .358 .359
Culberson, Charlie .328 .300
Calhoun, Willie .303 .231
Trevino, Jose .279 .305
Kiner-Falefa, Isiah .262 .291
Heim, Jonah .260 .265
Dahl, David .227 .270
White, Eli .216 .247
Guzmán, Ronald .162 .223
Taveras, Leody .135 .221
Tejeda, Anderson .093 .108

So in looking at this not really see anything all that surprising. wOBA says that Nick Solak, Nate Lowe and Joey Gallo are all hitting well, and their xwOBAs are consistent with that. Brock Holt has gotten off to a hot start, and his expected stats are even better than his actual stats. Adolis Garcia has mashed early, and while his xwOBA is below his actual wOBA, its still good.

Leody Taveras and Ronald Guzman have enormous negative spreads between their actual results and their expected results, though even their expected results are poor — they’ve been awful offensively this year in terms of their expected results, just not as awful as their actual numbers reflect.

David Dahl and Eli White also have big negative spreads between actual and expected results, though the spreads aren’t as large as with the other two, and their actual results and expected results are somewhat better. They aren’t hitting well, but it isn’t quite as bad as would appear at first glance.

Aside from Adolis, the only big positive spreads are for Willie Calhoun — who only has 14 plate appearances — and Charlie Culberson, and we aren’t concerned about the Culb at this point.

Isiah Kiner-Falefa...his trajectory has been noteworthy. He started off the year great, stinging the ball and putting up good numbers, but he has since slumped, and has just a .262 wOBA — that translates to a 68 wRC+ or a 67 OPS+, which is not okay, even for a shortstop.*

* Despite the poor offensive showing, IKF has a 0.8 bWAR and a 0.3 fWAR due to the advanced stats loving his defense and baserunning.

His .291 wOBA is...better, at least. Still not what we would describe as “good,” especially if you are going to have him batting leadoff. But that would give him a wRC+ in the high-80s, which, with his glove, would make him worth starting at shortstop.

As always, remember that this is a very small sample size — we are only 19 games into the season — so don’t draw definitive conclusions about guys or otherwise overreact to this data, be it good or bad.