Defensive Shift and BABIP
It seems that the newest “Moneyball” craze in the majors this season is the defensive shift. Usually used only against left handed hitters, it is being implemented by more and more teams against more and more players. Today, I am going to look at the fantasy implications of the shift.
First, there is little to no information available on what extent the shift is currently being used and against who. Without going back and looking at every game played, I decided to see how the shift was effecting a handful of players. I asked the “great” “minds” here at Fangraphs for 10 players who they see get shifted regularly. Here is the list they came up with:
Carlos Pena
Ryan Howard
Justin Morneau
Eric Hosmer
Alex Gordon
Prince Fielder
David Ortiz
Brian McCann
Jim Thome
Adam Dunn
One important note first. I have noticed that writers are identifying players to shift by their overall pull percentages. This is not the right way to look at it. First, it is only the infielders who are being shifted, so only ground balls and line drive data should be examined. Second, when a player is shifted, the defense puts 3 players close to one side of the field. This setup allows good coverage of the middle part of the field by the defense. It is actually better to look for a low percentage of ground balls and line drives hit to the opposite field to identify shift candidates (see Appendix at end of article for a method to get the percentage of batted balls to different zones of the field).
For reference, here are the career pull and opposite percentages for the above players and the average, high and low values for all of them:
| Ground Balls | Line Drives | |||
| Name | Pull % | Opposite % | Pull % | Opposite % |
| Adam Dunn | 62% | 11% | 41% | 22% |
| Alex Gordon | 61% | 13% | 40% | 25% |
| Brian McCann | 57% | 11% | 36% | 24% |
| Carlos Pena | 68% | 9% | 55% | 12% |
| David Ortiz | 57% | 10% | 41% | 26% |
| Eric Hosmer | 45% | 18% | 30% | 27% |
| Jim Thome | 55% | 8% | 34% | 27% |
| Justin Morneau | 48% | 17% | 38% | 28% |
| Prince Fielder | 47% | 16% | 35% | 22% |
| Ryan Howard | 62% | 8% | 48% | 15% |
| High | 68% | 18% | 55% | 28% |
| Average | 56% | 12% | 40% | 23% |
| Low | 45% | 8% | 30% | 12% |
| Standard Deviation | 7% | 4% | 7% | 5% |
If a person looks at the Pull % on ground balls, the range is 20 percentage points. Instead, if the Opposite % on ground balls is used, the range drops to 10 percentage points. The variation on the line drives shows the same pattern with the opposite field numbers being grouped closer together.
To get an idea of the effects of the shift against players, I calculated the difference in the players’ BABIP and xBABIP for 2011 and 2012. I only looked at these two seasons because that is when teams began to deploy the shift more and more. I subtracted each player’s BABIP from their xBABIP. To get the final value, I weighted the difference in BABIPs by the players’ plate appearance for that season. Here are the final numbers:
| Name | 2011 PA | 2011 BABIP | 2011 xBABIP | Diff | 2012 PA | 2012 BABIP | 2012 xBABIP | Diff | |
| Adam Dunn | 496 | 0.240 | 0.279 | -0.039 | 259 | 0.289 | 0.344 | -0.055 | |
| Alex Gordon | 690 | 0.358 | 0.333 | 0.025 | 256 | 0.293 | 0.294 | -0.001 | |
| Brian McCann | 527 | 0.287 | 0.283 | 0.004 | 189 | 0.234 | 0.325 | -0.091 | |
| Carlos Pena | 606 | 0.267 | 0.281 | -0.014 | 251 | 0.266 | 0.292 | -0.026 | |
| David Ortiz | 605 | 0.321 | 0.327 | -0.006 | 252 | 0.305 | 0.304 | 0.001 | |
| Eric Hosmer | 563 | 0.314 | 0.307 | 0.007 | 232 | 0.230 | 0.333 | -0.103 | |
| Jim Thome | 324 | 0.327 | 0.326 | 0.001 | 32 | 0.400 | 0.421 | -0.021 | |
| Justin Morneau | 228 | 0.257 | 0.279 | -0.022 | 178 | 0.254 | 0.332 | -0.078 | |
| Prince Fielder | 692 | 0.306 | 0.329 | -0.023 | 254 | 0.332 | 0.338 | -0.006 | |
| Ryan Howard | 644 | 0.303 | 0.331 | -0.028 | – | – | – | – | |
| Weighted Average Difference = | -0.009 | Weighted Average Difference = | -0.041 |
With this small sample size, the effect on a player’s BABIP this season is a decent amount (-0.041). It is 4 times the the value for 2011 (-0.009).
This methodology is not a perfect way to determine the exact effect of the shift, but it does show that the shift does seem to be dragging down a hitter’s BABIP. Also, it gives a possible starting off value to show the average change in BABIP because of the shift. To put these numbers into perspective for fantasy owners, here is an example.
Consider the following player:
600 AB
90 K (15% K%)
20 HR
10 SF
0.320 BABIP
The player would end up getting 180 Hits with those stats for a 0.300 AVG. If a shift is able to drop his BABIP to 0.280, the player’s hits would drop to 160 and their average to 0.267. Using these numbers as a reference, a player who is shifted will like see a significant drop in their AVG.
For fantasy owners looking for buy low candidates with their xBABIP exceeding their BABIP, they should know that those hitters who are seeing a shift put on them quite often may have their BABIP suppressed by around 40 points.
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Appendix: To figure out how much a player hits to various zones on the field, first go to:
http://www.baseballheatmaps.com/graph/battedballlocation.php
Select the following:
Select Hitter to examine
Infield: On
Infield Distance: 550 (This puts all the batted balls into 3 bins and makes the OF Zone selection irrelevant).
Number of angle zones to split the field into: 3
Select Hit Type: Grounds and/or Line
Start and Stop Date: User used
Pitch Type: Leave Blank
Press Submit
The output should appear with the negative numbers showing hits to left field and positive numbers to right field.
Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR twice, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.
Nice Work.
Mark Teixeira is another guy off the top of my head.
Thanks and I will add him and A-Gon to the list.
Adrian Gonzalez.
And why don’t these players bunt?
Pena actually does with about 10 bunts per year.
Pena has toyed with the Jays when they’ve shifted on him this season.
if you’ve gotten a power guy to bunt, rather than swing away, then the shift is a success (less total bases given up).
No, that doesn’t really make sense.
A solid power guy might have a SLG of .500 (it would probably be worse if he’s being shifted, but we’ll work with that number). A bunt gives you a SLG of 1.000 on that at-bat alone.
So, if you’re talking total bases, that power guy would only need to be successful on half of his bunts in order to justify doing it.
But since we’re NOT only talking total bases – we’re also talking about generating outs – he would probably come out ahead even if he was less successful than 50%. And since the shift usually only happens with no one on-base, the emphasis should simply be on reaching base, not on driving yourself in.
Wow, that’s really interesting. That strikes me as a considerable effect, especially this year when it seems the shift is being employed more than ever. Eric Hosmer’s -0.103 in 2012 jumps out. Does this dramatically change our expectations for what kind of player we expect him to be this season and going forward? Inquiring minds in keeper leagues want to know.
So Adam Dunn would be hitting .250 without the shift!!!
Josh Hamilton
2011 PA: 538
2011 BABIP: 0.317
2011 xBABIP: 0.343
Diff: -0.026
2012 PA: 253
2012 BABIP: 0.346
2012 xBABIP: 0.356
Diff: -0.010
Given that Hosmer is at the high end of the range for opposite percentages on both ground balls and line drives, does this mean that he is not a good candidate for the shift? Put differently: Is the large differential in his BABIP vs. xBABIP more due to just bad luck, rather than the shift?
Great read!
Isn’t it equally important when comparing last year to this whether or not the shift was routinely implemented against the player last year? Ortiz would seem like a good example. He’s had a shift on him for as long as I can remember and his BABIP #s are pretty similar from ’11 to ’12.
I’m not sure I’m interpreting the #s properly. Does this mean that Ortiz is potentially beating his shift better this year? I’m guessing Hosmer didn’t see the shift much last year but now that teams have enough ABs on him they can limit his hits between 1B/2B and up the middle. Hosmer does go opposite field more than everyone on this list, so I would expect him to be beating the shift more than the others.
Hosmer’s results are certainly bad luck in large part. If you consider that among the names listed he goes to the opposite field more than anyone else, and BABIP *should be* much higher on hits to the opposite field when being shifted, bad luck must still account for much of his problems this year.
I’m sure the shift has had some effect. But it can’t be the whole picture.
Hosmer isn’t even the player least likely to be shifted. While looking at a few more players, Texiera almost distributed the ball perfectly around the field. I am not sure why he is shifted at all.
re Teixeira – are you taking into account that he is a switch hitter?
Jeff not taking anything away from your great read but did you also see the story from last week on ESPN insider? Similar names mentioned + Grandy of course.
http://insider.espn.go.com/mlb/story/_/id/8000608/curtis-granderson-hitters-deserving-defensive-shifts-mlb
They also address something I was going to ask here, is there a Righty comparable analysis? I know most power lefties are pull-heavy but guys like Willingham, Frenchy are very anecdotally pull heavy to the left field side no?
Yes, I did read the article and some ideas in this article were based on the finding in the article
I think it would be foolish to think the shift didn’t at least take away a few hits, which naturally would drop a player’s BABIP. But since we don’t know how often the shift is implemented against each player, I think it’s also foolish to do the math trying to figure it out.
If I had to guess, I would assume every player has some difference between his xBABIP and BABIP, which would further taint the research done here. The xBABIP calculator assumes all line drives and grounders have the same chance of falling for a hit, when in fact, we all know not all batted balls are equal.
Excellent work.
Would looking at babip by batted ball type be insightful?
What I don’t get is why these hitters don’t learn to adjust their game to counter the shift. IE. Learn to hit the other way instead of suffering with miserable performances. They didn’t get to the majors by not being able to adjust so why the fail to do so now?
i wouldn’t say the guys on that list have had “miserable performances” this year (now, adam dunn last year, that was miserable). if you get one of these guys trying to hit a weak grounder to the opposite field, then the shift has “worked” (kept the ball in the park… which is the real threat with most of these power hitters in the first place).
terrific idea to explore. nice work.
what formula did you use for xBABIP? because the slash12 one has not been correct the last couple of years due to the lower BABIP of the league in general. You actually have to adjust it down. So if the actual BABIP is a few points (like 9, say) below the xBABIP, it is probably because the formula for xBABIP overestimates because it is based on a higher run environment, not necessarily because the players were shifted on. Now, some of that decrease in league BABIP could be due to the shift, but you would have to test that somehow first to establish that before calling it fact. If you adjusted the xBABIP formula for the article, disregard this. But if not, then we don’t know the extent to which the shift is responsible for the lower BABIP than expected, or how much is due to having a lower run environment (and league average BABIP) in general.
If you drew a lot from the ESPN Insider piece, that should have been mentioned up front, not matter-of-factly in the comments.
I bet that ESPN article is chock full of standard deviations.
Don’t be an asshole just for sport, especially when your opinions are dumb. I, on the other hand, am being an asshole for good reason. My way’s better.
We are talking baseball, not judging for the Pulitzer Prize.