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.