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Who’s Hitting The Ball Harder This Year, And Who’s Just Getting Lucky?

We’re just over a quarter of the way into the 2016 MLB season, and the statistics have begun to settle down. Mike Trout leads all hitters in wins above replacement, and Clayton Kershaw dominates nearly every conceivable pitching metric by a healthy margin. As predicted, the Chicago Cubs are the best team in the league, and the Atlanta Braves are jockeying with the Twins for the title of worst record. Baseball’s topsy-turvy early-season universe is starting to make sense again.

But despite these hallmarks of a mature season, some players are still seeing the effects of what might be unusually good or bad fortune. For example, Chicago Cubs utilityman Ben Zobrist is putting up the finest offensive season of his career at age 35 happy birthday, Ben Z.!!! but it’s inflated partially by a batting average on balls in play (BABIP) that is 50 points higher than his career average. On the flip side, Cincinnati’s Joey Votto has seen his Weighted Runs Created Plus fall from 172 last year to 84 this season, to go with a career-worst BABIP. In the past, we would have been left wondering whether such changes were due to differences in ability or simply luck, but MLB’s new radar-based tracking technology Statcast can give us an answer.

Because Statcast tracks how quickly (and at what trajectory) balls come off the bat of each hitter, it is a good barometer of a hitter’s underlying skill set. Unless he possesses some unusual ability, such as exceptional speed or the ability to control the horizontal angle of his batted balls, a player isn’t likely to outperform his batted ball statistics for very long. So, armed with that database — and a model to translate the information into an expected outcome1 — we can predict how well each player should be hitting, absent the capricious effects of luck.

First, let’s get a sense of how much of a role luck can play in a “down” season. Any change in performance (for the purposes of this story, we’ll measure that using on-base plus slugging, or OPS) from one season to the next is only partially due to the player’s skills, as estimated by the predicted OPS metric I created. The differences between actual and predicted OPS can be construed as a form of luck — and as the following list of players with the greatest decreases in OPS since last year illustrates, at this stage in the season, luck can carry far more weight than skill.

CHANGE IN OPS
PLAYER ACTUAL PREDICTED DIFFERENCE (“LUCK”)
Russell Martin -.364 -.111 -.253
Mark Teixeira -.329 -.124 -.205
Joey Votto -.306 -.059 -.247
Prince Fielder -.287 -.035 -.252
Kendrys Morales -.287 +.088 -.375
Chris Coghlan -.274 -.140 -.134
A.J. Pierzynski -.264 -.055 -.209
Adam Lind -.255 -.067 -.188
Ryan Goins -.240 +.023 -.263
Carlos Gomez -.237 -.141 -.096
Average -.284 -.062 -.222
Players with the greatest OPS declines from 2015 to 2016

Data as of May 25.

Sources: MLB Advanced Media, Fangraphs

Most of the players on that list have also seen their skills slightly decline — but a common theme is that those changes have been dwarfed by the influence of bad luck. Let’s go back to Votto: Despite his paltry .694 OPS in 20162 — a stunning 306-point drop from 2015 — his underlying indicators have hardly declined at all. Votto is still driving the ball, even though his hard-hit liners are now falling into outfielders’ gloves instead of finding gaps in the defense.

Of course, some players’ skills really have changed, and it shows up in their Statcast measurements. Here are the 10 players whose predicted OPS numbers have declined the most from 2015 to 2016:

CHANGE IN OPS
PLAYER ACTUAL PREDICTED DIFFERENCE (“LUCK”)
Giancarlo Stanton -.145 -.169 +.024
John Jaso -.007 -.168 +.161
Nori Aoki -.110 -.143 +.033
Carlos Gomez -.237 -.141 -.096
Chris Coghlan -.274 -.140 -.134
Chase Headley -.132 -.136 +.004
Jonathan Schoop +.002 -.127 +.129
Mark Teixeira -.329 -.124 -.205
Chris Davis -.112 -.120 +.008
C.J. Cron -.063 -.117 +.054
Average -.141 -.139 -.002
Players with the greatest predicted OPS declines from 2015 to 2016

Data as of May 25.

Sources: MLB Advanced Media, Fangraphs

These players are either generating less exit velocity with their batted balls or are striking them at less advantageous angles — i.e., directly into the ground for groundouts or straight up into the air for popups. But many of them, such as Miami Marlins outfielder and exit-velocity deity Giancarlo Stanton, remain good hitters despite downturns in their underlying indicators. (Stanton’s exit velocity has declined to such an extent that he’s responsible for only four of the top 10 hardest-hit balls so far this season — last season, he had eight of the top 10. And, yes, I’m being sarcastic — Stanton is still amazing.)

We can also see the effects of age on this list, whether in 36-year-old Yankees first baseman Mark Teixeira’s nosedive into outright badness or 30-year-old Baltimore Orioles first baseman Chris Davis’s more gradual descent from “super-elite” to merely “pretty great.” And in some cases, like that of Pirates first baseman John Jaso, Statcast can see a hitter’s abilities fading when noisier, more conventional data might fail to show any change.

By the same token, Statcast can be quicker than the conventional numbers to pick up on a young player’s breakout:

CHANGE IN OPS
PLAYER ACTUAL PREDICTED DIFFERENCE (“LUCK”)
Victor Martinez +.251 +.270 -.019
Domingo Santana -.034 +.238 -.272
Christian Yelich +.162 +.222 -.060
Mark Trumbo +.127 +.216 -.089
Jose Altuve +.160 +.213 -.053
Michael Saunders +.435 +.207 +.228
Matt Adams +.222 +.200 +.022
Mike Napoli +.019 +.195 -.176
Jason Castro +.089 +.193 -.104
Logan Forsythe +.118 +.190 -.072
Average +.155 +.214 -.060
Players with the greatest predicted OPS gains from 2015 to 2016

Data as of May 25.

Sources: MLB Advanced Media, FanGraphs

The hitters with the most improved ball-striking (according to Statcast) tend to be youngsters making names for themselves. For example, Brewers outfielder Domingo Santana’s Statcast numbers are suggestive of Giancarlo Stanton’s 2015 campaign even if his surface-level statistics (a 101 wRC+, for instance) aren’t as impressive. Meanwhile, Marlins outfielder Christian Yelich is finally living up to the hype, with the underlying indicators to basically match his 10th-in-the-NL OPS.

And then there’s Tigers designated hitter/ageless wonder Victor Martinez at the top of the leaderboard, resurgent after an injury-riddled 2015 debacle. Martinez had one of the greatest age-35 seasons of all time two years ago, and he’s on track to post one of the best age-37 years ever as well. But even though the tracking technology can confirm that Martinez’s season is no fluke — his underlying indicators back up that sky-high OPS — it can’t offer clues about the apparent Fountain of Youth powering his success. Even Statcast has its limits.

Check out our latest MLB predictions.

Footnotes

  1. Specifically, I used a random forest model (trained on 2015 data) to estimate the linear weight value of each batted ball based on its exit velocity, launch angle and the park in which it occurred. For the purposes of this article, I considered only hitters with more than 100 plate appearances so far in 2016.

  2. Stats are as of May 25.

Rob Arthur is FiveThirtyEight’s baseball columnist and also writes about crime.

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