How the Shift has Changed the Game

The shift is one of the most discussed changes in baseball in many years. It is probably the biggest purely defensive change in decades (right?). Commissioner Manfred has publicly stated that he dislikes it. Players are actively working with hitting coaches to beat the shift. People are asking, how can we beat the shift? And some are starting to deny we can. FanGraphs comments predict that the shift will be bad for baseball, because less offense is less fun.

But just how big is the shift? Just how much has it changed the league?


Okay, “Zero” is too strong. It might have changed something, but if it has we can’t tell.

Okay, that too is too strong, but, the number of obvious statistical correlates of an effective shift, seen in terms of league wide stats, is zero. Maybe we can tell, but if so, it can only be told in some serious data-mining that goes beyond obvious results, like number of outs, even in splits, since teams started shifting. No evidence exists of a change in the league-wide stats you would expect the shift to change. BABIP is unchanged. Grounder BABIP is unchanged. Left-handed batter BABIP is unchanged. In fact, BABIP is higher today than it was 40 years ago, but BABIP inflated about .02 from the 1970s to the 1990s and hasn’t evidently changed since.

The shift is a defensive strategy whose intent is to depress run expectancy on balls in play. The likely effect of the shift, if the strategy works, would be in increasing outs on balls in play. Here is a table of BABIP since 1995, the last 20 years:

Year    BABIP
1995   0.298
1996   0.301
1997   0.301
1998   0.300
1999   0.302
2000   0.300
2001   0.296
2002  0.293
2003   0.294
2004   0.297
2005   0.295
2006   0.301
2007   0.303
2008   0.300
2009   0.299
2010   0.297
2011   0.295
2012   0.297
2013   0.297
2014   0.299
2015   0.299

The apparent trend is obvious, if something can be obviously non-existent.

We can look deeper: how have lefties, whom the shift allegedly affects more, been hurt by the shift? Well, in 2015 lefty hitters had their highest BABIP (.301) versus lefty pitchers in the last 13 years (as long as FanGraphs data goes for that split.) Against right-handed pitchers, left-handed batters tied their second-worst season (.299) in the last 15 years, for a whopping one hit in 500 less than the average during that time (.301).

You see, the problem is that we need to look at grounders: fly balls and line drives aren’t really being affected, but grounders are, so in the long run, the shift is slightly depressing hits. Except the obvious correlate isn’t there either.  In 2015, grounders had a .236 BABIP, .004 higher than the 13-year average.

2015 isn’t some sort of outlier. In every easy-to-research split you might choose, BABIP fluctuations in the last 13 years are within the range of random variation. The recent years of the shift era show not even a statistically insignificant decrease in BABIP: in many of those splits, BABIP has by a hair increased. (See tables linked below.)

Another source of evidence that the shift works might be found by comparing defense-independent pitching models with non-defense-independent stats. Maybe BABIP leaves something out, but we see that runs are down relative to DIPS predictions. If so, one possible explanation is the shift. FIP, a great DIPS, is equal to 3*BB+13*HR-2*K + C, where C is a constant that makes league-average FIP equal league-average ERA. If C is smaller now, that suggest (but does not prove) that BIP outs have changed. C is bigger now (by just .0053, or .048 runs per inning), suggesting that more runs are scored from balls in play. It’s no proof, but if balls in play were a lot more frequently outs, we wouldn’t expect them, overall, to account for more runs and ERA would be down more than peripherals imply.

We can’t infer from this data that some individual hitters are unaffected by the shift. Jeff Sullivan’s recent piece on adjusting to the shift is what brought me to the data (I was seeking to investigate just how badly lefty hitters have been hurt, and discovered something far more interesting), and he mentioned Jimmy Rollins’ attempts to adjust to the shift. I recall a lot of speculation about Mark Teixeira being hurt by the shift. Maybe those guys are. Maybe they aren’t. Maybe they aren’t, but others yet to be named are. Things which don’t have league-wide effect may interact with particular skillsets in hard-to-identify ways.

It’s possible that the shift has changed things by reducing the value of range up the middle, allowing more offensively-oriented players to man those positions. But that seems more like an effect that we would see in future, not one we have seen, because it should take years of player development for those sorts of changes to have a league-wide effect.

It is possible that the shift increases strikeouts and depresses walks. It would be hard to know this, though. It is also possible that the shift has reduced the value of certain defensive skills (e.g., range) and that the decreased need for range has allowed teams to play more offensively-oriented guys up the middle, effectively cancelling the BABIP effects. It sounds farfetched to suppose that two of eight hitters being more offensively-minded can cancel an effect of a shift that should apply to eight of eight of them, but we haven’t ruled it out.

Overall, league scoring is down. But DIPS suggest this is mostly the result of more strikeouts, with a little home-run and walk noise thrown in. There are some ways in which the shift might be having an effect — please offer further hypotheses below. All the evidence here is correlational and correlation doesn’t imply causation. Even anti-correlation doesn’t imply non-causation (if people who drink more exercise more — both are correlated positively with wealth — drinking might get anti-correlated with bad health because exercise compensates for the health impact of drinking). But when no correlation is found and no obvious counter-effects can be sighted, the lack of a correlation suggests weak influence at best.


League BABIP, 1975 to 2015

LHB v. LHP and LHB v. RHP, all available years

Ground Ball BABIP, all available years

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Chris Walker
Chris Walker

One of the better fan posts. I enjoyed reading it, especially since you didn’t bury the lede. Making a slightly unscientific statement of “zero”, then reinforcing and shaping it as you went, made it entertaining.


My guess: teams have gradually become much more aggressive with platooning hitters, which has offset the effect of the shift.

“in 2015 lefty hitters had their highest BABIP (.301) versus lefty pitchers in the last 13 years”

A possible reason LHBs are doing so well against LHPs recently is that only the best LHBs are allowed to face them.

“fly balls and line drives aren’t really being affected”

The other thing not being measured is the indirect effect on a batter’s approach. If he’s taking a different type of swing to avoid grounding into the shift, it may be affecting SLG, IFFB%, etc.

Peter 2
Peter 2

Watching baseball over these last few years, my intuition is that:

1. When a team knows how to do it right, aggressive defensive shifting seems to work.
2. When a team doesn’t really know how to do it right, it seems to do more harm than good.

When the early adopters (namely, the Rays) started doing it, and seemingly effectively, everyone started copycatting. Next thing I know I’m watching the Yankees clumsily shifting their infield around, with personnel that didn’t necessarily seem comfortable doing it, with lukewarm results.

The Rays posted a .265 BABIP in 2011—league leading by a mile. They’ve had consistently low BABIP against since then, consistently among the league leaders:

2012: .277
2013: .284
2014: .286
2015: .284

So shifting may not work for everyone, but I think that the Rays would have a pretty good argument that it’s working *for them*. Might not surprise you to hear that it might be working for Maddon’s new team as well (Cubs 2015 BABIP against was .287).

What about those Yankees I was talking to you about? I’m sure they got sick of seeing Teixeira hitting into the teeth of Rays shifts 5 years ago, and decided what was good for the goose was good for the gander. Did it work? Not that I can tell:

(2010: .281)
2011: .297
2012: .296
2013: .302
2014: .298
2015: .300

It’s a copycat world, but just because a strategy works for one team that has the personnel and sophistication to do it, doesn’t mean it’s going to work out great for everyone.