How well is your team using the shift?

In a previous article, where I looked at the impact of the shift in 2018, there was some evidence to suggest that shifting was working against left handers and not against right handers.  But there are additional variables which we need to look at in order to confirm or refute this analysis. We need to look at the teams which are shifting. How good is the infield defense of these teams? How much are they shifting? How well are they each doing at shift?

But firstly lets update the position from the last piece. Further to the adjustments made to account for pitchers and the impact of batters on bases, we should account for any selection bias of the hitters (as suggested  by Tom Tango). To do that we weight on number of at bats and shift to non-shift ratio. If a player was shifted 100 times and not shifted 500 times then their weight would be 100, if they were shifted 300 times and not 300 times their weight would be 300.  Repeat this for all hitters, and come up with the weighted average.  Using that and previous adjustments, you get the following.

These adjusted results still align with previous findings, shifting looks like it is working against LHH but not against RHH. So to look at the teams who are shifting to see if there is any influence there. Let’s start with looking at how good the teams are defensively.

The two main metrics which are used to describe defensive performance are DRS (Defensive Runs Saved) and UZR (Ultimate Zone Rating). These both try to measure the same thing, but do it differently, so this analysis uses both to approximate which teams have the best infield defense. The table below ranks the teams based on 2018’s DRS and UZR for infield players only and combines the ranks for an overall ranking. 

With this list of which infields are defensively best, let’s look at which teams shift more that others and see how good they were at it. As with the previous piece this will focus on ‘bases empty’ scenarios only, so that there is no impact of runners on base with the positioning of the infield. The outcomes have been adjusted for batter and pitcher the following ways:

  • To start of with it takes the statistics as before and calculates them for shifted and non-shifted at bats of each team.
  • For these at bats I have also generated a season version of each stat, split by handedness, for the batters and pro rata’d them to give expected rates from these at bats.
  • Using those batter figures, I have then calculated how much above or below this expected outcome the results were.
  • For pitchers, I calculated if there was any difference between ability of pitchers with and without the shift within a team.
  • I then adjusted the expected outcome, for the shift, based on that difference (max pitching team difference for wOBA was 3.6%).
  • Finally I scaled this to 100, with 100 being inline. As we are looking at wOBA from the pitching side now, less than 100 is better expected and greater than 100 is worse than expected.

This data is only split by batter handedness, due to a ‘small sample size’ when splitting the data further, by pitcher handedness.

All the following charts are colour coded with red being good, blue being bad and white being no difference.

Shifting v Left Handed Hitters (LHH)

The table below is ordered by the teams which shift against LHH most. It shows the team’s defensive ability (DEF Rank), the volumes and percent which a team’s shifts, and the outcome for the at bats.

Looking at the shift % and the teams’ defensive rank you can see that the teams with worse defenses shift more often,  with only two of the top 10 defensive teams in the top 10 for shifting and six of them in the bottom 10 for shifting. 

That may make some sense managerially; if you had a infield which was very good you might be less inclined to make changes as you expect the improvements to be more marginal for your team. But this may also indicate that the standard defensive metrics are also skewed towards defenders who play in the traditional defensive positions.

So, let’s look at the performance of the shift…

For the teams which shift most, there are three teams with significant improvements when shifting – with the Astros, Yankees and Twins all showing an improvement of over 15 points.

It looks like most teams are getting positive results out of shifting left handers and teams who have better defensive infielders are shifting less. But how are they getting these results, is this from a reduction of BABIP? Let’s look at the same factors as we did for the batters.

The table below is ordered by the overall performance difference (wOBA) between shifted and non-shifted at bats, compared to expectations.

Reducing BABIP is thought to be the principle component of what the shift is trying to accomplish and 18 teams managed to do this. Of those them 15 managed to bring down the overall production (wOBA). But as you can see there are other factors which contribute to the change in overall production of the batters. Changes in the K-BB and HR rates also have massive impact on the batters overall performance.

Looking at the Astros and the Yankees you can see that they haven’t decreased the BABIP significantly but they have in increased their K-BB rate and decreased their HR rates. Both these teams have inflated HR rates, 60%+ above expectation, when not shifting and decreased rates against expectation when shifting (Astros -20% & Yankees -45%). Conversely you have the Mariners who did reduce BABIP but their HR rate increased (from -47% to -1% against expectation).

But, Rockies…

There is only one team which has significant deterioration – the Rockies at 10 points worse off. What makes this more interesting is that the Rockies are the best ranked team defensively of teams which shift more than 50%.

As we all know already, whenever the Rockies, or their players, appear to ‘look different’ in a stat you should check their home/road splits. In 2018 the Rockies, shifted similarly home and away, but the impact of the shift was dramatically different.  The chart below is for LHH only.

The impact of shifting in Coors Field was a 36 point increase in batter performance. When the shift is on it is 16 points up from expectation but when they don’t shift they reduced the expected batter performance by 19 points. When at Coors Field HR, BB, XBH rates all nearly double when the Rockies put a shift on. That should be a lesson for Bud Black in 2019…

If we look at how other teams do when shifting in Coors Field we also see an increase in batter performance, but a much smaller one. We have decreased sample sizes, so this will require further investigation to see if this impact is significant, but this does suggest that shifting at Coors Field may not be a worthwhile endeavor.

Shifting v Right Handed Hitters (RHH)

In the shart above, against RHH you see that vast usage difference compared to shifting against LHH.

Tampa Bay shift the most but their rate of 36.6% would put them 24th on the LHH shift rate, so teams have clearly not introduced this as widely.  The defensive ranking is split out a bit more evenly here, but there the is still the general trend of teams with worse defenders shifting more. Also, there are similar teams at the top with the exception of Tampa Bay who were 16th for LHH. However, unlike shifting against LHH most of the teams which are shifting the most are not decreasing hitters performance when they do so.

Of teams who shift more than 15% of the time, only the Twins have a significant decrease in batter performance (9 points). Whilst the Yankees have a significant increase (24 points).

The Yankees have seemingly been using the shift against RHH experimentally with them shifting against 65% of batters (78% when at home) in May, but averaging around 20-25% in other months. During that time period they played at home against the other 4 teams in the AL which reached the postseason, and the Angels, so it looks like they were trying to see if there was any impact and stopped doing so once they concluded it wasn’t effective.

Like with the LHH, let’s look at what is going on to see if we can see why. This time I have kept it in shift % order, so it is easier to see what is happening for the teams that shift the most.

Of the teams that shift more than 15% of the time (12 teams), eight of them managed to decrease BABIP when shifting, nine of them drop the HR rate but only three of them managed to decrease wOBA. The difference here, compared to LHH, is that the K & K-BB rates drop significantly for all bar one of these teams, the Royals – who were a historically awful hitting team in 2018. This means the overall impact for most of these teams is an improvement in batter performance and not a decrease.

The Yankees are an extreme case here, with their walk rate being 38% lower than expectation when not shifting and 72% higher than expectation when they shifted. Their strike rate dropped from 37% higher than expectation when not shifting to 22% lower than expectation.  These combined lead to a change the batter overall production from 12% below expectation to 12% above, even though there was only a marginal difference in BABIP and HR rate.

This is the analysis which shows that the shift, for both RHH and LHH, is something way more than just moving some infielders about to stop some singles…

It suggests that batters, pitchers and catchers are all changing their  mentality and approach to what is going on in front of them. If everyone was playing the game the same you wouldn’t see these drastic changes in categories outside BABIP.

The next stage of this investigation will be to look at the pitchers, to see if there are any differences to how they pitch when a shift is put on. Be it change in pitch types, locations or sequencing. Also to look at batters to see if they are being more patient when against the shift, and if they are trying to elevate the ball more.

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